US20050027461A1

US20050027461A1 - Methods for evaluating oligonucleotide probe sequences

Info

Publication number: US20050027461A1
Application number: US10/877,231
Authority: US
Inventors: Karen Shannon; Paul Wolber; Glenda Delenstarr; Peter Webb; Robert Kincaid
Original assignee: Individual
Current assignee: Individual
Priority date: 1998-02-10
Filing date: 2004-06-24
Publication date: 2005-02-03
Also published as: US20030054346A1; US6251588B1

Abstract

Methods are disclosed for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides is identified. The unique oligonucleotides are chosen to sample the entire length of a nucleotide sequence that is hybridizable with the target nucleotide sequence. At least one parameter that is independently predictive of the ability of each of the oligonucleotides of the set to hybridize to the target nucleotide sequence is determined and evaluated for each of the above oligonucleotides. A subset of oligonucleotides within the predetermined number of unique oligonucleotides is identified based on the evaluation of the parameter. Oligonucleotides in the subset are identified that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence. The method may be carried out with the aid of a computer.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 09/784,674 filed on Feb. 15, 2001; which application is a continuation of application Ser. No. 09/021,701 filed on Feb. 10, 1998 and now issued as U.S. Pat. No. 6,251,588; the disclosures of which are herein incorporated by reference.

Appendix

This patent application includes an appendix (the “Appendix”), which contains the source code for the software used in carrying out the examples in accordance with the present invention.
A portion of the present disclosure contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as it appears in the U.S. Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever.

BACKGROUND OF THE INVENTION

1. Field of the Invention
Significant morbidity and mortality are associated with infectious diseases and genetically inherited disorders. More rapid and accurate diagnostic methods are required for better monitoring and treatment of these conditions. Molecular methods using DNA probes, nucleic acid hybridization and in vitro amplification techniques are promising methods offering advantages to conventional methods used for patient diagnoses.
Nucleic acid hybridization has been employed for investigating the identity and establishing the presence of nucleic acids. Hybridization is based on complementary base pairing. When complementary single stranded nucleic acids are incubated together, the complementary base sequences pair to form double-stranded hybrid molecules. The ability of single stranded deoxyribonucleic acid (ssDNA) or ribonucleic acid (RNA) to form a hydrogen bonded structure with a complementary nucleic acid sequence has been employed as an analytical tool in molecular biology research. The availability of radioactive nucleoside triphosphates of high specific activity and the development of methods for their incorporation into DNA and RNA has made it possible to identify, isolate, and characterize various nucleic acid sequences of biological interest. Nucleic acid hybridization has great potential in diagnosing disease states associated with unique nucleic acid sequences. These unique nucleic acid sequences may result from genetic or environmental change in DNA by insertions, deletions, point mutations, or by acquiring foreign DNA or RNA by means of infection by bacteria, molds, fungi, and viruses. The application of nucleic acid hybridization as a diagnostic tool in clinical medicine is limited due to the cost and effort associated with the development of sufficiently sensitive and specific methods for detecting potentially low concentrations of disease-related DNA or RNA present in the complex mixture of nucleic acid sequences found in patient samples.
One method for detecting specific nucleic acid sequences generally involves immobilization of the target nucleic acid on a solid support such as nitrocellulose paper, cellulose paper, diazotized paper, or a nylon membrane. After the target nucleic acid is fixed on the support, the support is contacted with a suitably labeled probe nucleic acid for about two to forty-eight hours. After the above time period, the solid support is washed several times at a controlled temperature to remove unhybridized probe. The support is then dried and the hybridized material is detected by autoradiography or by spectrometric methods. When very low concentrations must be detected, the above method is slow and labor intensive, and nonisotopic labels that are less readily detected than radiolabels are frequently not suitable.
A method for the enzymatic amplification of specific segments of DNA known as the polymerase chain reaction (PCR) method has been described. This in vitro amplification procedure is based on repeated cycles of denaturation, oligonucleotide primer annealing, and primer extension by thermophilic polymerase, resulting in the exponential increase in copies of the region flanked by the primers. The PCR primers, which anneal to opposite strands of the DNA, are positioned so that the polymerase catalyzed extension product of one primer can serve as a template strand for the other, leading to the accumulation of a discrete fragment whose length is defined by the distance between the 5′ ends of the oligonucleotide primers.
Other methods for amplifying nucleic acids have also been developed. These methods include single primer amplification, ligase chain reaction (LCR), transcription-mediated amplification methods including 3SR and NASBA, and the Q-beta-replicase method. Regardless of the amplification used, the amplified product must be detected.
One method for detecting nucleic acids is to employ nucleic acid probes that have sequences complementary to sequences in the target nucleic acid. A nucleic acid probe may be, or may be capable of being, labeled with a reporter group or may be, or may be capable of becoming, bound to a support. Detection of signal depends upon the nature of the label or reporter group. Usually, the probe is comprised of natural nucleotides such as ribonucleotides and deoxyribonucleotides and their derivatives although unnatural nucleotide mimetics such as peptide nucleic acids and oligomeric nucleoside phosphonates are also used. Commonly, binding of the probes to the target is detected by means of a label incorporated into the probe. Alternatively, the probe may be unlabeled and the target nucleic acid labeled. Binding can be detected by separating the bound probe or target from the free probe or target and detecting the label. In one approach, a sandwich is formed comprised of one probe, which may be labeled, the target and a probe that is or can become bound to a surface. Alternatively, binding can be detected by a change in the signal-producing properties of the label upon binding, such as a change in the emission efficiency of a fluorescent or chemiluminescent label. This permits detection to be carried out without a separation step. Finally, binding can be detected by labeling the target, allowing the target to hybridize to a surface-bound probe, washing away the unbound target and detecting the labeled target that remains.
Direct detection of labeled target hybridized to surface-bound probes is particularly advantageous if the surface contains a mosaic of different probes that are individually localized to discrete, known areas of the surface. Such ordered arrays containing a large number of oligonucleotide probes have been developed as tools for high throughput analyses of genotype and gene expression. Oligonucleotides synthesized on a solid support recognize uniquely complementary nucleic acids by hybridization, and arrays can be designed to define specific target sequences, analyze gene expression patterns or identify specific allelic variations. One difficulty in the design of oligonucleotide arrays is that oligonucleotides targeted to different regions of the same gene can show large differences in hybridization efficiency, presumably due, at least in part, to the interplay between the secondary structures of the oligonucleotides and their targets and the stability of the final probe/target hybridization product. A method for predicting which oligonucleotides will show detectable hybridization would substantially decrease the number of iterations required for optimal array design and would be particularly useful when the total number of oligonucleotide probes on the array is limited. A method to predict oligonucleotide hybridization efficiency would also streamline the empirical approaches currently used to select potential antisense therapeutics, which are designed to modulate gene expression in vivo by hybridizing to specific messenger RNA (mRNA) molecules and inhibiting their translation into proteins.
While it is well known that the structure of the target nucleic acid affects the affinity of oligonucleotide hybridization, current methods for predicting target structures from the primary sequence fail to predict target regions accessible for oligonucleotide binding. Consequently, selection of oligonucleotides for antisense reagents or oligonucleotide probe arrays has been largely empirical. As most of the target sequence is sequestered by intramolecular base pairing and not accessible for oligonucleotide binding, the process of identifying good oligonucleotides has required large numbers of low efficiency experiments.
The design and implementation of algorithms that effectively predict the ability of oligonucleotides to rapidly and avidly bind to complementary nucleotide sequences has been an important problem in molecular biology since the invention of facile methods for chemical DNA synthesis. The subsequent inventions of the polymerase chain reaction (PCR), antisense inhibition of gene expression and oligonucleotide array methods for performing massively parallel hybridization experiments have made the need for effective predictive algorithms even more critical.
Previous attempts to solve the nucleic acid probe design problem include PCR primer design software applications (e.g., OLIGO®), neural networks, PCR primer design applications that search for sequences that possess minimal ability to cross-hybridize with other targets present in a sample (e.g., HYBsimulator™), and approaches that attempt to predict the efficiency of antisense sequence suppression of mRNA translation from a combination of predicted nucleic acid duplex melting temperature and predicted target strand structure. The methods that predict effective oligonucleotide primers for performing PCR from DNA templates work well for that application where relatively stringent conditions are employed. This is because PCR experimental design greatly simplifies the prediction problem: hybridization is performed at high temperature, at relatively low ionic strength and in the presence of a large molar excess of oligonucleotide. Under these conditions, the oligonucleotide and target secondary structures are relatively unimportant.
Unfortunately, these conditions do not apply to oligonucleotide arrays, which are usually hybridized under relatively non-denaturing conditions, or to antisense suppression of gene expression, which takes place in vivo. Oligonucleotide arrays can contain hundreds of thousands of different sequences and conditions are chosen to allow the oligonucleotide with the lowest melting temperature to hybridize efficiently. These “lowest common denominator” conditions are usually relatively non-denaturing and secondary structure constraints become significant. Accordingly, the above applications require new predictive methods that are capable of estimating the effects of oligonucleotide and target structure on hybridization efficiency. For these reasons, current algorithms for designing PCR primer oligonucleotides fail badly when applied to the problems of oligonucleotide array or antisense oligonucleotide design.
To date, the most effective approach for identifying oligonucleotides with good hybridization efficiency has been an empirical one. Such an approach involves the synthesis of large numbers of oligonucleotide probes for a given target nucleotide sequence. Arrays are formed that include the above oligonucleotide probes. Hybridization experiments are carried out to determine which of the oligonucleotide probes exhibit good hybridization efficiencies. Examples of such an approach are found in D. Lockhart, et al., Nature Biotech., infra, L. Wodicka, et al., Nature Biotechnology, infra., and N. Milner et al. Nature Biotech, infra. One major drawback to this approach is the vast number of oligonucleotides that must be synthesized in order to achieve a satisfactory result. Typically, about 2%-5% of the test probes synthesized yield acceptable signal levels.
The use of neural networks for oligonucleotide design has also been investigated. Neural networks are easily taught with real data; they therefore afford a general approach to many problems. However, their performance is limited by the “senses” that they are given. An analogy works best here: the human brain is an astoundingly capable neural network, but a blind person cannot be taught to reliably distinguish colors by smell. In addition, a large amount of data is required to adequately teach a neural network to perform its job well. A comprehensive database for either oligonucleotide array design or antisense suppression of gene expression has not been made available. For these reasons, the performance reported to-date of neural network solutions against the probe design problem is mediocre.
Finally, approaches that have attempted to use target nucleic acid folding calculations to predict experimental results inferred to depend upon hybridization efficiency (e.g. antisense suppression of mRNA translation) have so far only demonstrated that the predictions of current nucleic acid folding calculations correlate poorly with observed behavior. The probable reason for this is that the structures predicted by such programs for long sequences are poor predictors of chemical reality; the results of experiments that attempt to confirm the predictions of such calculations support this assessment. Recent improvements to this approach which use predicted RNA structure topology as a predictor of relative RNA/RNA association kinetics have been more successful at forecasting the results of antisense experiments. However, these methods are not computationally efficient, and have so far only been shown to work for targets less than 100 bases long. Such methods are therefore not yet capable of predicting the behavior of full-length mRNA targets, which are typically between 1,000 and 2,000 bases in length.
2. Description of the Related Art
U.S. Pat. No. 5,512,438 (Ecker) discloses the inhibition of RNA expression by forming a pseudo-half knot RNA at the target's RNA secondary structure using antisense oligonucleotides.
Cook, et al., in U.S. Pat. No. 5,670,633 discuss sugar-modified oligonucleotides that detect and modulate gene expression.
Antisense oligonucleotide inhibition of the RAS gene is disclosed in U.S. Pat. No. 5,582,986 (Monia, et al.).
U.S. Pat. No. 5,593,834 (Lane, et al.) discusses a method of preparing DNA sequences with known ligand binding characteristics.
Mitsuhashi, et al., in U.S. Pat. No. 5,556,749 discusses a computerized method for designing optimal DNA probes and an oligonucleotide probe design station.
U.S. Pat. No. 5,081,584 (Omichinski, et al.) discloses a computer-assisted design of anti-peptides based on the amino acid sequence of a target peptide.
A PCR primer design application that searches for sequences that possess minimal ability to cross-hybridize with other targets present in a sample is available as HYBsimulator™, version 2.0, AGCT, Inc., 2102 Business Center Drive, Suite 170, Irvine, Calif. 92715 (714) 833-9983.
A PCR primer design software application is available as OLIGO®, version 5.0, National Biosciences, Inc., 3650 Annapolis Lane North, #140, Plymouth, Minn. 55447 (800) 747-4362.
D. J. Lockhart, et al., Nature Biotech. 14:1675-1684 (1996) describe a neural network approach to the selection of efficient surface-bound oligonucleotide probes.
M. Mitsuhashi, et al., Nature, 367:759-761 (1994) disclose a method for designing specific oligonucleotide probes and primers by modeling the potential cross-hybridization of candidate probes to non-target sequences known to be present in samples.
R. A. Stull, et al., Nuc. Acids Res., 20:3501-3508 (1992) describe a method of predicting the efficacy of antisense oligonucleotides, using predicted target secondary structure and predicted oligonucleotide/target binding free energy as input parameters.
N. Milner, et al., Nature Biotechnology, 15:537-541 (1997) compare observed patterns of probe hybridization to those expected from the predicted secondary structure of the nucleic acid target.
L. Wodicka, et al., Nature Biotechnology, 15:1359-1367 (1997) describe simple rules for avoiding inefficient and non-specific probes during design and synthesis of oligonucleotides arrays.
J. SantaLucia Jr., et al., Biochemistry, 35:3555 (1996) disclose parameters and methods for the calculation of thermodynamic properties of DNA/DNA homoduplexes.
N. Sugimoto, et al., Biochemistry, 34:11211 (1995) disclose parameters and methods for the calculation of thermodynamic properties of DNA/RNA heteroduplexes.
J. A. Jaeger, et al., Proc. Natl. Acad. Sci. USA, 86:7706 (1989) disclose methods for estimation of the free energy of the most stable intramolecular structure of a single-stranded polynucleotide, by means of a dynamic programming algorithm.
S. F. Altschul, et al., Nature Genetics, 6:119-129 (1994) disclose methods for calculating the complexity and information content of amino acid and nucleic acid sequences.
T. A. Weber and E. Helfand, J. Chem. Phys., 71, 4760 (1979) describe approaches for the modeling of polymer structures by molecular dynamics simulations.
V. Patzel and G. Sczakiel, Nature Biotech., 16, 64-68 (1998) disclose methods for estimating rate constants for association of antisense RNA molecules with mRNA targets by examination of predicted antisense RNA secondary structures.
Light-generated oligonucleotide arrays for rapid DNA sequence analysis is described by A. C. Pease, et al., Proc. Nat. Acad. Sci. USA (1994) 91:5022-5026.
Mitsuhashi discusses basic requirements for designing optimal oligonucleotide probe sequences in J. Clinical Laboratory Analysis (1996) 10:277-284.
Rychlik, et al., discloses a computer program for choosing optimal oligonucleotides for filter hybridization, sequencing and in vitro amplification of DNA in Nucleic Acids Research (1989) 17(21):8543-8551.
A strategy for designing specific antisense oligonucleotide sequences is described by Mitsuhashi in J. Gastroenterol. (1997) 32:282-287.
Mitsuhashi discusses basic requirements for designing optimal PCR primers in J. Clinical Laboratory Analysis (1996) 10:285-293.
Hyndman, et al., disclose software to determine optimal oligonucleotide sequences based on hybridization simulation data in BioTechniques (1996) 20(6): 1090-1094.
Eberhardt discloses a shell program for the design of PCR primers using genetics computer group (GCG) software (7.1) on VAX/VMS™ systems in BioTechniques (1992) 13(6):914-917.
Chen, et al., disclose a computer program for calculating the melting temperature of degenerate oligonucleotides used in PCR or hybridization in BioTechniques (1997) 22(6):1158-1160.
Partial thermodynamic parameters for prediction stability and washing behavior of DNA duplexes immobilized on gel matrix is described by Kunitsyn, et al., in J. Biomolecular Structure & Dynamics, ISSN 0739-1102 (1996) 14(1):239-244.

SUMMARY OF THE INVENTION

One embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined set of unique oligonucleotide sequences is identified. The unique oligonucleotide sequences are chosen to sample the entire length of a nucleotide sequence that is hybridizable with the target nucleotide sequence. At least one parameter that is predictive of the ability of each of the oligonucleotides specified by the set of sequences to hybridize to the target nucleotide sequence is determined and evaluated for each of the above oligonucleotide sequences. A subset of oligonucleotide sequences within the predetermined set of unique oligonucleotide sequences is identified based on the examination of the parameter values. Finally, oligonucleotide sequences in the subset are identified that are clustered along one or more regions of the nucleotide sequence that is hybridizable to the target nucleotide sequence. The oligonucleotide probes corresponding to the identified sequences find use in polynucleotide assays particularly where the assays involve oligonucleotide arrays. For a discussion of oligonucleotide arrays, see, e.g., U.S. Pat. No. 5,700,637 (E. Southern) and U.S. Pat. No. 5,667,667 (E. Southern), the relevant disclosures of which are incorporated herein by reference.
Another embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a complementary target nucleotide sequence. A set of overlapping oligonucleotide sequences is identified based on a nucleotide sequence that is complementary to the target nucleotide sequence. At least two parameters that are independently predictive of the ability of each of the oligonucleotides specified by the oligonucleotide sequences to hybridize to the target nucleotide sequence are determined and evaluated for each of the oligonucleotide sequences. Independence is assured by requiring that the parameters be poorly correlated with respect to one another. A subset of oligonucleotide sequences within the set of oligonucleotide sequences is identified based on the examination of the parameter values. Finally, oligonucleotide sequences in the subset are identified that are clustered along one or more regions of the nucleotide sequence that is complementary to the target nucleotide sequence.
Another embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a complementary target nucleotide sequence. A set of overlapping oligonucleotide sequences is obtained based on a nucleotide sequence of length L, complementary to the target nucleotide sequence. The oligonucleotide sequences of the set of overlapping oligonucleotide sequences are of identical length N and spaced one nucleotide apart. The set comprises L-N+1 oligonucleotide sequences. Parameters are determined for each of the oligonucleotide sequences of the set of overlapping oligonucleotide sequences. One parameter is the predicted melting temperature of the duplex of each of the oligonucleotides specified by the oligonucleotide sequences and the target nucleotide sequence, corrected for salt concentration. The other parameter is the predicted free energy of the most stable intramolecular structure of each of the oligonucleotides specified by the oligonucleotide sequences at the temperature of hybridization of the oligonucleotide with the target nucleotide sequence. A subset of oligonucleotide sequences within the set of oligonucleotide sequences is selected based on an examination of the parameter values by establishing cut-off values for each of the parameters. Oligonucleotide sequences in the subset that are clustered along one or more regions of the complementary nucleotide sequence are ranked based on the sizes of the clusters of oligonucleotide sequences. Finally, a subset of the clustered oligonucleotide sequences is selected that statistically samples the clusters of oligonucleotide sequences. The selected sampled subset is used to specify the synthesis of oligonucleotides for experimental evaluation.
Another aspect of the present invention is a computer based method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides within a nucleotide sequence that is hybridizable with the target nucleotide sequence is identified under computer control. The oligonucleotides are chosen to sample the entire length of the nucleotide sequence. A value is determined and evaluated under computer control for each of the oligonucleotides for at least one parameter that is independently predictive of the ability of each of the oligonucleotides to hybridize to the target nucleotide sequence. The parameter values are stored. A subset of oligonucleotides within the predetermined number of unique oligonucleotides is identified by examination of the stored parameter values under computer control. Then, oligonucleotides in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence are identified under computer control.
Another aspect of the present invention is a computer system for conducting a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. The system comprises (a) input means for introducing a target nucleotide sequence into the computer system, (b) means for determining a number of unique oligonucleotide sequences that are within a nucleotide sequence that is hybridizable with the target nucleotide sequence where the oligonucleotide sequences are chosen to sample the entire length of the nucleotide sequence, (c) memory means for storing the oligonucleotide sequences, (d) means for controlling the computer system to carry out for each of the oligonucleotide sequences a determination and evaluation of a value for at least one parameter that is independently predictive of the ability of each of the oligonucleotide sequences to hybridize to the target nucleotide sequence, (e) means for storing the parameter values, (f) means for controlling the computer to carry out an identification from the stored parameter values a subset of oligonucleotide sequences within the number of unique oligonucleotide sequences based on the examination of the parameter, (g) means for storing the subset of oligonucleotides, (h) means for controlling the computer to carry out an identification of oligonucleotide sequences in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence, (i) means for storing the oligonucleotide sequences in the subset, and (j) means for outputting data relating to the oligonucleotide sequences in the subset.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a general flow chart depicting the method of the present invention.
FIG. 2 is a flow chart depicting a preferred embodiment of a method in accordance with the present invention.
FIG. 3 is a contour plot of normalized hybridization intensity from multiple experiments, as a function of the free energy of the most stable probe intramolecular structure (ΔG_MFOLD) and the difference between the predicted RNA/DNA heteroduplex melting temperature (T_m) and the temperature of hybridization (T_hyb).
FIG. 4 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to a portion of the rabbit β-globin gene (radiolabeled antisense RNA target).
FIG. 5 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the HIV PRT gene (fluorescein-labeled sense RNA target).
FIG. 6 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the G3PDH gene (fluorescein-labeled antisense RNA target).
FIG. 7 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the p53 gene (fluorescein-labeled antisense RNA target).
FIG. 8 shows the observed hybridization patterns for oligonucleotides selected using a method in accordance with the present invention and additional oligonucleotides to the HIV PRTs gene (using data from the GeneChip™ data).

DEFINITIONS

Before proceeding further with a description of the specific embodiments of the present invention, a number of terms will be defined.
Nucleic Acids:
Polynucleotide—a compound or composition that is a polymeric nucleotide or nucleic acid polymer. The polynucleotide may be a natural compound or a synthetic compound. In the context of an assay, the polynucleotide is often referred to as a polynucleotide analyte. The polynucleotide can have from about 20 to 5,000,000 or more nucleotides. The larger polynucleotides are generally found in the natural state. In an isolated state the polynucleotide can have about 30 to 50,000 or more nucleotides, usually about 100 to 20,000 nucleotides, more frequently 500 to 10,000 nucleotides. It is thus obvious that isolation of a polynucleotide from the natural state often results in fragmentation. The polynucleotides include nucleic acids, and fragments thereof, from any source in purified or unpurified form including DNA (dsDNA and ssDNA) and RNA, including tRNA, mRNA, rRNA, mitochondrial DNA and RNA, chloroplast DNA and RNA, DNA/RNA hybrids, or mixtures thereof, genes, chromosomes, plasmids, the genomes of biological material such as microorganisms, e.g., bacteria, yeasts, viruses, viroids, molds, fungi, plants, animals, humans, and the like. The polynucleotide can be only a minor fraction of a complex mixture such as a biological sample. Also included are genes, such as hemoglobin gene for sickle-cell anemia, cystic fibrosis gene, oncogenes, cDNA, and the like.
The polynucleotide can be obtained from various biological materials by procedures well known in the art. The polynucleotide, where appropriate, may be cleaved to obtain a fragment that contains a target nucleotide sequence, for example, by shearing or by treatment with a restriction endonuclease or other site specific chemical cleavage method.
For purposes of this invention, the polynucleotide, or a cleaved fragment obtained from the polynucleotide, will usually be at least partially denatured or single stranded or treated to render it denatured or single stranded. Such treatments are well known in the art and include, for instance, heat or alkali treatment, or enzymatic digestion of one strand. For example, dsDNA can be heated at 90-100° C. for a period of about 1 to 10 minutes to produce denatured material.
Target nucleotide sequence—a sequence of nucleotides to be identified, usually existing within a portion or all of a polynucleotide, usually a polynucleotide analyte. The identity of the target nucleotide sequence generally is known to an extent sufficient to allow preparation of various sequences hybridizable with the target nucleotide sequence and of oligonucleotides, such as probes and primers, and other molecules necessary for conducting methods in accordance with the present invention, an amplification of the target polynucleotide, and so forth.
The target sequence usually contains from about 30 to 5,000 or more nucleotides, preferably 50 to 1,000 nucleotides. The target nucleotide sequence is generally a fraction of a larger molecule or it may be substantially the entire molecule such as a polynucleotide as described above. The minimum number of nucleotides in the target nucleotide sequence is selected to assure that the presence of a target polynucleotide in a sample is a specific indicator of the presence of polynucleotide in a sample. The maximum number of nucleotides in the target nucleotide sequence is normally governed by several factors: the length of the polynucleotide from which it is derived, the tendency of such polynucleotide to be broken by shearing or other processes during isolation, the efficiency of any procedures required to prepare the sample for analysis (e.g. transcription of a DNA template into RNA) and the efficiency of detection and/or amplification of the target nucleotide sequence, where appropriate.
Oligonucleotide—a polynucleotide, usually single stranded, usually a synthetic polynucleotide but may be a naturally occurring polynucleotide. The oligonucleotide(s) are usually comprised of a sequence of at least 5 nucleotides, preferably, 10 to 100 nucleotides, more preferably, 20 to 50 nucleotides, and usually 10 to 30 nucleotides, more preferably, 20 to 30 nucleotides, and desirably about 25 nucleotides in length.
Various techniques can be employed for preparing an oligonucleotide. Such oligonucleotides can be obtained by biological synthesis or by chemical synthesis. For short sequences (up to about 100 nucleotides), chemical synthesis will frequently be more economical as compared to the biological synthesis. In addition to economy, chemical synthesis provides a convenient way of incorporating low molecular weight compounds and/or modified bases during specific synthesis steps. Furthermore, chemical synthesis is very flexible in the choice of length and region of the target polynucleotide binding sequence. The oligonucleotide can be synthesized by standard methods such as those used in commercial automated nucleic acid synthesizers. Chemical synthesis of DNA on a suitably modified glass or resin can result in DNA covalently attached to the surface. This may offer advantages in washing and sample handling. For longer sequences standard replication methods employed in molecular biology can be used such as the use of M13 for single stranded DNA as described by J. Messing (1983) Methods Enzymol, 101:20-78.
Other methods of oligonucleotide synthesis include phosphotriester and phosphodiester methods (Narang, et al. (1979) Meth. Enzymol 68:90) and synthesis on a support (Beaucage, et al. (1981) Tetrahedron Letters 22:1859-1862) as well as phosphoramidite techniques (Caruthers, M. H., et al., “Methods in Enzymology,” Vol. 154, pp. 287-314 (1988)) and others described in “Synthesis and Applications of DNA and RNA,” S. A. Narang, editor, Academic Press, New York, 1987, and the references contained therein. The chemical synthesis via a photolithographic method of spatially addressable arrays of oligonucleotides bound to glass surfaces is described by A. C. Pease, et al., Proc. Nat. Acad. Sci. USA (1994) 91:5022-5026.
Oligonucleotide probe—an oligonucleotide employed to bind to a portion of a polynucleotide such as another oligonucleotide or a target nucleotide sequence. The design and preparation of the oligonucleotide probes are generally dependent upon the sensitivity and specificity required, the sequence of the target polynucleotide and, in certain cases, the biological significance of certain portions of the target polynucleotide sequence.
Oligonucleotide primer(s)—an oligonucleotide that is usually employed in a chain extension on a polynucleotide template such as in, for example, an amplification of a nucleic acid. The oligonucleotide primer is usually a synthetic nucleotide that is single stranded, containing a sequence at its 3′-end that is capable of hybridizing with a defined sequence of the target polynucleotide. Normally, an oligonucleotide primer has at least 80%, preferably 90%, more preferably 95%, most preferably 100%, complementarity to a defined sequence or primer binding site. The number of nucleotides in the hybridizable sequence of an oligonucleotide primer should be such that stringency conditions used to hybridize the oligonucleotide primer will prevent excessive random non-specific hybridization. Usually, the number of nucleotides in the oligonucleotide primer will be at least as great as the defined sequence of the target polynucleotide, namely, at least ten nucleotides, preferably at least 15 nucleotides, and generally from about 10 to 200, preferably 20 to 50, nucleotides.
In general, in primer extension, amplification primers hybridize to, and are extended along (chain extended), at least the target nucleotide sequence within the target polynucleotide and, thus, the target sequence acts as a template. The extended primers are chain “extension products.” The target sequence usually lies between two defined sequences but need not. In general, the primers hybridize with the defined sequences or with at least a portion of such target polynucleotide, usually at least a ten-nucleotide segment at the 3′-end thereof and preferably at least 15, frequently a 20 to 50 nucleotide segment thereof.
Nucleoside triphosphates—nucleosides having a 5′-triphosphate substituent. The nucleosides are pentose sugar derivatives of nitrogenous bases of either purine or pyrimidine derivation, covalently bonded to the 1′-carbon of the pentose sugar, which is usually a deoxyribose or a ribose. The purine bases include adenine (A), guanine (G), inosine (I), and derivatives and analogs thereof. The pyrimidine bases include cytosine (C), thymine (T), uracil (U), and derivatives and analogs thereof. Nucleoside triphosphates include deoxyribonucleoside triphosphates such as the four common deoxyribonucleoside triphosphates dATP, dCTP, dGTP and dTTP and ribonucleoside triphosphates such as the four common triphosphates rATP, rCTP, rGTP and rUTP.
The term “nucleoside triphosphates” also includes derivatives and analogs thereof, which are exemplified by those derivatives that are recognized and polymerized in a similar manner to the underivatized nucleoside triphosphates.
Nucleotide—a base-sugar-phosphate combination that is the monomeric unit of nucleic acid polymers, i.e., DNA and RNA. The term “nucleotide” as used herein includes modified nucleotides as defined below.
DNA—deoxyribonucleic acid.
RNA—ribonucleic acid.
Modified nucleotide—a unit in a nucleic acid polymer that contains a modified base, sugar or phosphate group. The modified nucleotide can be produced by a chemical modification of the nucleotide either as part of the nucleic acid polymer or prior to the incorporation of the modified nucleotide into the nucleic acid polymer. For example, the methods mentioned above for the synthesis of an oligonucleotide may be employed. In another approach a modified nucleotide can be produced by incorporating a modified nucleoside triphosphate into the polymer chain during an amplification reaction. Examples of modified nucleotides, by way of illustration and not limitation, include dideoxynucleotides, derivatives or analogs that are biotinylated, amine modified, alkylated, fluorophore-labeled, and the like and also include phosphorothioate, phosphite, ring atom modified derivatives, and so forth.
Nucleoside—is a base-sugar combination or a nucleotide lacking a phosphate moiety.
Nucleotide polymerase—a catalyst, usually an enzyme, for forming an extension of a polynucleotide along a DNA or RNA template where the extension is complementary thereto. The nucleotide polymerase is a template dependent polynucleotide polymerase and utilizes nucleoside triphosphates as building blocks for extending the 3′-end of a polynucleotide to provide a sequence complementary with the polynucleotide template. Usually, the catalysts are enzymes, such as DNA polymerases, for example, prokaryotic DNA polymerase (I, II, or III), T4 DNA polymerase, T7 DNA polymerase, Klenow fragment, reverse transcriptase, Vent DNA polymerase, Pfu DNA polymerase, Tag DNA polymerase, and the like, or RNA polymerases, such as T3 and T7 RNA polymerases. Polymerase enzymes may be derived from any source such as cells, bacteria such as E. coli, plants, animals, virus, thermophilic bacteria, and so forth.
Amplification of nucleic acids or polynucleotides—any method that results in the formation of one or more copies of a nucleic acid or polynucleotide molecule (exponential amplification) or in the formation of one or more copies of only the complement of a nucleic acid or polynucleotide molecule (linear amplification).
Hybridization (hybridizing) and binding—in the context of nucleotide sequences these terms are used interchangeably herein. The ability of two nucleotide sequences to hybridize with each other is based on the degree of complementarity of the two nucleotide sequences, which in turn is based on the fraction of matched complementary nucleotide pairs. The more nucleotides in a given sequence that are complementary to another sequence, the more stringent the conditions can be for hybridization and the more specific will be the binding of the two sequences. Increased stringency is achieved by elevating the temperature, increasing the ratio of co-solvents, lowering the salt concentration, and the like.
Hybridization efficiency—the productivity of a hybridization reaction, measured as either the absolute or relative yield of oligonucleotide probe/polynucleotide target duplex formed under a given set of conditions in a given amount of time.
Homologous or substantially identical polynucleotides—In general, two polynucleotide sequences that are identical or can each hybridize to the same polynucleotide sequence are homologous. The two sequences are homologous or substantially identical where the sequences each have at least 90%, preferably 100%, of the same or analogous base sequence where thymine (T) and uracil (U) are considered the same. Thus, the ribonucleotides A, U, C and G are taken as analogous to the deoxynucleotides dA, dT, dC, and dG, respectively. Homologous sequences can both be DNA or one can be DNA and the other RNA.
Complementary—Two sequences are complementary when the sequence of one can bind to the sequence of the other in an anti-parallel sense wherein the 3′-end of each sequence binds to the 5′-end of the other sequence and each A, T(U), G, and C of one sequence is then aligned with a T(U), A, C, and G, respectively, of the other sequence. RNA sequences can also include complementary G/U or U/G basepairs.
Member of a specific binding pair (“sbp member”)—one of two different molecules, having an area on the surface or in a cavity that specifically binds to and is thereby defined as complementary with a particular spatial and polar organization of the other molecule. The members of the specific binding pair are referred to as cognates or as ligand and receptor (antiligand). These may be members of an immunological pair such as antigen-antibody, or may be operator-repressor, nuclease-nucleotide, biotin-avidin, hormones-hormone receptors, nucleic acid duplexes, IgG-protein A, DNA-DNA, DNA-RNA, and the like.
Ligand—any compound for which a receptor naturally exists or can be prepared.
Receptor (“antiligand”)—any compound or composition capable of recognizing a particular spatial and polar organization of a molecule, e.g., epitopic or determinant site. Illustrative receptors include naturally occurring receptors, e.g., thyroxine binding globulin, antibodies, enzymes, Fab fragments, lectins, nucleic acids, repressors, protection enzymes, protein A, complement component C1q, DNA binding proteins or ligands and the like.
Oligonucleotide Properties:
Potential of an oligonucleotide to hybridize—the combination of duplex formation rate and duplex dissociation rate that determines the amount of duplex nucleic acid hybrid that will form under a given set of experimental conditions in a given amount of time.
Parameter—a factor that provides information about the hybridization of an oligonucleotide with a target nucleotide sequence. Generally, the factor is one that is predictive of the ability of an oligonucleotide to hybridize with a target nucleotide sequence. Such factors include composition factors, thermodynamic factors, chemosynthetic efficiencies, kinetic factors, and the like.
Parameter predictive of the ability to hybridize—a parameter calculated from a set of oligonucleotide sequences wherein the parameter positively correlates with observed hybridization efficiencies of those sequences. The parameter is, therefore, predictive of the ability of those sequences to hybridize. “Positive correlation” can be rigorously defined in statistical terms. The correlation coefficient ρ_x,yof two experimentally measured discreet quantities x and y (N values in each set) is defined as $ρ_{x, y} = \frac{Covariance (x, y)}{\sqrt{Variance (x) Variance (y)}},$
where the Covariance (x,y) is defined by $Covariance (x, y) = \frac{1}{N} \sum_{j = 1}^{N} (x_{j} - μ_{x}) (y_{j} - μ_{y}) .$
The quantities μ_xand μ_yare the averages of the quantities x and y, while the variances are simply the squares of the standard deviations (defined below). The correlation coefficient is a dimensionless (unitless) quantity between −1 and 1. A correlation coefficient of 1 or −1 indicates that x and y have a linear relationship with a positive or negative slope, respectively. A correlation coefficient of zero indicates no relationship; for example, two sets of random numbers will yield a correlation coefficient near zero. Intermediate correlation coefficients indicate intermediate degrees of relatedness between two sets of numbers. The correlation coefficient is a good statistical measure of the degree to which one set of numbers predicts a second set of numbers.
Composition factor—a numerical factor based solely on the composition or sequence of an oligonucleotide without involving additional parameters, such as experimentally measured nearest-neighbor thermodynamic parameters. For instance, the fraction (G+C), given by the formula $f_{GC} = \frac{n_{G} + n_{C}}{n_{G} + n_{C} + n_{A} + n_{T or U}},$
where n_G, n_C, n_Aand n_{T or U}are the numbers of G, C, A and T (or U) bases in an oligonucleotide, is an example of a composition factor. Examples of composition factors, by way of illustration and not limitation, are mole fraction (G+C), percent (G+C), sequence complexity, sequence information content, frequency of occurrence of specific oligonucleotide sequences in a sequence database and so forth.
Thermodynamic factor—numerical factors that predict the behavior of an oligonucleotide in some process that has reached equilibrium. For instance, the free energy of duplex formation between an oligonucleotide and its complement is a thermodynamic factor. Thermodynamic factors for systems that can be subdivided into constituent parts are often estimated by summing contributions from the constituent parts. Such an approach is used to calculate the thermodynamic properties of oligonucleotides.
Examples of thermodynamic factors, by way of illustration and not limitation, are predicted duplex melting temperature, predicted enthalpy of duplex formation, predicted entropy of duplex formation, free energy of duplex formation, predicted melting temperature of the most stable intramolecular structure of the oligonucleotide or its complement, predicted enthalpy of the most stable intramolecular structure of the oligonucleotide or its complement, predicted entropy of the most stable intramolecular structure of the oligonucleotide or its complement, predicted free energy of the most stable intramolecular structure of the oligonucleotide or its complement, predicted melting temperature of the most stable hairpin structure of the oligonucleotide or its complement, predicted enthalpy of the most stable hairpin structure of the oligonucleotide or its complement, predicted entropy of the most stable hairpin structure of the oligonucleotide or its complement, predicted free energy of the most stable hairpin structure of the oligonucleotide or its complement, thermodynamic partition function for intramolecular structure of the oligonucleotide or its complement and the like.
Chemosynthetic efficiency—oligonucleotides and nucleotide sequences may both be made by sequential polymerization of the constituent nucleotides. However, the individual addition steps are not perfect; they instead proceed with some fractional efficiency that is less than unity. This may vary as a function of position in the sequence. Therefore, what is really produced is a family of molecules that consists of the desired molecule plus many truncated sequences. These “failure sequences” affect the observed efficiency of hybridization between an oligonucleotide and its complementary target. Examples of chemosynthetic efficiency factors, by way of illustration and not limitation, are coupling efficiencies, overall efficiencies of the synthesis of a target nucleotide sequence or an oligonucleotide probe, and so forth.
Kinetic factor—numerical factors that predict the rate at which an oligonucleotide hybridizes to its complementary sequence or the rate at which the hybridized sequence dissociates from its complement are called kinetic factors. Examples of kinetic factors are steric factors calculated via molecular modeling or measured experimentally, rate constants calculated via molecular dynamics simulations, associative rate constants, dissociative rate constants, enthalpies of activation, entropies of activation, free energies of activation, and the like.
Predicted duplex melting temperature—the temperature at which an oligonucleotide mixed with a hybridizable nucleotide sequence is predicted to form a duplex structure (double-helix hybrid) with 50% of the hybridizable sequence. At higher temperatures, the amount of duplex is less than 50%; at lower temperatures, the amount of duplex is greater than 50%. The melting temperature T_m(° C.) is calculated from the enthalpy (ΔH), entropy (ΔS) and C, the concentration of the most abundant duplex component (for hybridization arrays, the soluble hybridization target), using the equation $T_{m} = \frac{Δ H}{Δ S + R \ln C} - 273.15,$
where R is the gas constant, 1.987 cal/(mole-° K). For longer sequences (>100 nucleotides), T_mcan also be estimated from the mole fraction (G+C), X_G+C, using the equation
T _m=81.5+41.0χ_G+C.
Melting temperature corrected for salt concentration—polynucleotide duplex melting temperatures are calculated with the assumption that the concentration of sodium ion, Na⁺, is 1 M. Melting temperatures T′_mcalculated for duplexes formed at different salt concentrations are corrected via the semi-empirical equation
T′ _m([Na⁺])=T _m+16.6 log([Na⁺]).
Predicted enthalpy, entropy and free energy of duplex formation—the enthalpy (ΔH), entropy and free energy (ΔG) are thermodynamic state functions, related by the equation
ΔG=ΔH−TΔS,
where T is the temperature in ° K. In practice, the enthalpy and entropy are predicted via a thermodynamic model of duplex formation (the “nearest neighbor” model which is explained in more detail below), and used to calculate the free energy and melting temperature.
Predicted free energy of the most stable intramolecular structure of an oligonucleotide or its complement—single-stranded DNA and RNA molecules that contain self-complementary sequences can form intramolecular secondary structures. For instance, the oligonucleotide

5′-ACTGGCAATCACAATTGCCAGTAA-3′ (SEQ ID NO:1)
can base pair with itself, to form the structure

5′-ACTGGCAATCA (SEQ ID NO:1)

||||||||| C

3′-AATGACCGTTAA

where a vertical line indicates Watson-Crick base pair formation. Many such structures are possible for a given sequence; two are of particular interest. The first is the lowest energy “hairpin” structure (formed by folding a sequence back on itself with a connecting loop at least 3 nucleotides long). The second is the lowest energy structure that can be formed by including more complex topologies, such as “bulge loops” (unpaired duplexes between two regions of base-paired duplex) and cloverleaf structures, where 3 base-paired stretches meet at a triple-junction. A good example of a complex secondary structure is the structure of a tRNA molecule, an example of which, namely, yeast tRNA^Alais shown below.
For either type of structure, a value of the free energy of that structure can be calculated, relative to the unpaired strand, by means of a thermodynamic model similar to that used to calculate the free energy of a base-paired duplex structure. Again, the free energy ΔG is calculated from the enthalpy ΔH and the entropy ΔS at a given absolute temperature T via the equation
ΔG=ΔH−TΔS.
However, in this case there is the added difficulty that the lowest energy structure must be found. For a simple hairpin structure, this optimization can be performed via a relatively simple search algorithm. For more complex structures (such as a cloverleaf) a dynamic programming algorithm, such as that implemented in the program MFOLD, must be used.

Yeast tRNA^Ala—The RNA sequence includes many non-standard ribonucleotides, such as D (5,6 dihydrouridine), m¹G (1-methylguanosine), m²G (N²-dimethylguanosine), ψ (pseudouridine), I (inosine), m¹I (1-methylinosine) and T (ribothymidine). Dots (−) mark (non-standard) G=U base pairs. The structure is taken from A. L. Lehninger, et al., Principles of Biochemistry, 2^ndEd. (Worth Publishers, New York, N.Y., 1993).


	3′
	/
	A
	C

	5′ C
	\ A
	pG-C
	G-C
	G·U
	C-G
	G-C
	U U
	G-C UU
	DG U AGGCC A
	C AUGCG m¹G \|\|\|\|\| G	(SEQ ID NO:2)
	·\|\|\| UCCGG C
	G AGCGC C Tψ
	GD m²G D
	C-GAG
	U-A
	C-G
	C-G
	C-G
	U ψ
	U m¹I
	I C
	G

Coupling efficiencies—chemosynthetic efficiencies are called coupling efficiencies when the synthetic scheme involves successive attachment of different monomers to a growing oligomer; a good example is oligonucleotide synthesis via phosphoramidite coupling chemistry.
Algorithmic Operations:
Evaluating a parameter—determination of the numerical value of a numerical descriptor of a property of an oligonucleotide sequence by means of a formula, algorithm or look-up table.
Filter—a mathematical rule or formula that divides a set of numbers into two subsets. Generally, one subset is retained for further analysis while the other is discarded. If the division into two subsets is achieved by testing the numbers against a simple inequality, then the filter is referred to as a “cut-off”. In the context of the current invention, an example by way of illustration and not limitation is the statement “The predicted self structure free energy must be greater than or equal to −0.4 kcal/mole,” which can be used as a filter for oligonucleotide sequences; this particular filter is also an example of a cut-off.
Filter set—A set of rules or formulae that successively winnow a set of numbers by identifying and discarding subsets that do not meet specific criteria. In the context of the current invention, an example by way of illustration and not limitation is the compound statement “the predicted self structure free energy must be greater than or equal to −0.4 kcal/mole and the predicted RNA/DNA heteroduplex melting temperature must lie between 60° C. and 85° C.,” which can be used as a filter set for oligonucleotide sequences.
Examining a parameter—comparing the numerical value of a parameter to some cutoff-value or filter.
Statistical sampling of a cluster—extraction of a subset of oligonucleotides from a cluster of oligonucleotides based upon some statistical measure, such as rank by oligonucleotide starting position in the sequence complementary to the target sequence.
First quartile, median and third quartile—If a set of numbers is ranked by value, then the value that divides the lower ¼ from the upper ¾ of the set is the first quartile, the value that divides the set in half is the median and the value that divides the lower ¾ from the upper ¼ of the set is the third quartile.
Poorly correlated—If it is not possible to perform a “good” prediction, as defined via statistics, of one set of numbers from another set of numbers using a simple linear model, then the two sets of numbers are said to be poorly correlated.
Computer program—a written set of instructions that symbolically instructs an appropriately configured computer to execute an algorithm that will yield desired outputs from some set of inputs. The instructions may be written in one or several standard programming languages, such as C, C++, Visual BASIC, FORTRAN or the like. Alternatively, the instructions may be written by imposing a template onto a general-purpose numerical analysis program, such as a spreadsheet.
Experimental System Components:
Small organic molecule—a compound of molecular weight less than 1500, preferably 100 to 1000, more preferably 300 to 600 such as biotin, fluorescein, rhodamine and other dyes, tetracycline and other protein binding molecules, and haptens, etc. The small organic molecule can provide a means for attachment of a nucleotide sequence to a label or to a support.
Support or surface—a porous or non-porous water insoluble material. The surface can have any one of a number of shapes, such as strip, plate, disk, rod, particle, including bead, and the like. The support can be hydrophilic or capable of being rendered hydrophilic and includes inorganic powders such as glass, silica, magnesium sulfate, and alumina; natural polymeric materials, particularly cellulosic materials and materials derived from cellulose, such as fiber containing papers, e.g., filter paper, chromatographic paper, etc.; synthetic or modified naturally occurring polymers, such as nitrocellulose, cellulose acetate, poly (vinyl chloride), polyacrylamide, cross linked dextran, agarose, polyacrylate, polyethylene, polypropylene, poly(4-methylbutene), polystyrene, polymethacrylate, poly(ethylene terephthalate), nylon, poly(vinyl butyrate), etc.; either used by themselves or in conjunction with other materials; glass available as Bioglass, ceramics, metals, and the like. Natural or synthetic assemblies such as liposomes, phospholipid vesicles, and cells can also be employed.
Binding of oligonucleotides to a support or surface may be accomplished by well-known techniques, commonly available in the literature. See, for example, A. C. Pease, et al., Proc. Nat. Acad. Sci. USA, 91:5022-5026 (1994).
Label—a member of a signal producing system. Usually the label is part of a target nucleotide sequence or an oligonucleotide probe, either being conjugated thereto or otherwise bound thereto or associated therewith. The label is capable of being detected directly or indirectly. Labels include (i) reporter molecules that can be detected directly by virtue of generating a signal, (ii) specific binding pair members that may be detected indirectly by subsequent binding to a cognate that contains a reporter molecule, (iii) oligonucleotide primers that can provide a template for amplification or ligation or (iv) a specific polynucleotide sequence or recognition sequence that can act as a ligand such as for a repressor protein, wherein in the latter two instances the oligonucleotide primer or repressor protein will have, or be capable of having, a reporter molecule. In general, any reporter molecule that is detectable can be used.
The reporter molecule can be isotopic or nonisotopic, usually non-isotopic, and can be a catalyst, such as an enzyme, a polynucleotide coding for a catalyst, promoter, dye, fluorescent molecule, chemiluminescent molecule, coenzyme, enzyme substrate, radioactive group, a small organic molecule, amplifiable polynucleotide sequence, a particle such as latex or carbon particle, metal sol, crystallite, liposome, cell, etc., which may or may not be further labeled with a dye, catalyst or other detectable group, and the like. The reporter molecule can be a fluorescent group such as fluorescein, a chemiluminescent group such as luminol, a terbium chelator such as N-(hydroxyethyl) ethylenediaminetriacetic acid that is capable of detection by delayed fluorescence, and the like.
The label is a member of a signal producing system and can generate a detectable signal either alone or together with other members of the signal producing system. As mentioned above, a reporter molecule can be bound directly to a nucleotide sequence or can become bound thereto by being bound to an sbp member complementary to an sbp member that is bound to a nucleotide sequence. Examples of particular labels or reporter molecules and their detection can be found in U.S. Pat. No. 5,508,178 issued Apr. 16, 1996, at column 11, line 66, to column 14, line 33, the relevant disclosure of which is incorporated herein by reference. When a reporter molecule is not conjugated to a nucleotide sequence, the reporter molecule may be bound to an sbp member complementary to an sbp member that is bound to or part of a nucleotide sequence.
Signal Producing System—the signal producing system may have one or more components, at least one component being the label. The signal producing system generates a signal that relates to the presence or amount of a target polynucleotide in a medium. The signal producing system includes all of the reagents required to produce a measurable signal. Other components of the signal producing system may be included in a developer solution and can include substrates, enhancers, activators, chemiluminescent compounds, cofactors, inhibitors, scavengers, metal ions, specific binding substances required for binding of signal generating substances, and the like. Other components of the signal producing system may be coenzymes, substances that react with enzymic products, other enzymes and catalysts, and the like. The signal producing system provides a signal detectable by external means, by use of electromagnetic radiation, desirably by visual examination. Signal-producing systems that may be employed in the present invention are those described more fully in U.S. Pat. No. 5,508,178, the relevant disclosure of which is incorporated herein by reference.
Ancillary Materials—Various ancillary materials will frequently be employed in the methods and assays utilizing oligonucleotide probes designed in accordance with the present invention. For example, buffers and salts will normally be present in an assay medium, as well as stabilizers for the assay medium and the assay components. Frequently, in addition to these additives, proteins may be included, such as albumins, organic solvents such as formamide, quaternary ammonium salts, polycations such as spermine, surfactants, particularly non-ionic surfactants, binding enhancers, e.g., polyalkylene glycols, or the like.

DETAILED DESCRIPTION OF THE INVENTION

The invention is directed to methods or algorithms for predicting oligonucleotides specific for a nucleic acid target where the oligonucleotides exhibit a high potential for hybridization. The algorithm uses parameters of the oligonucleotide and the oligonucleotide/target nucleotide sequence duplex, which can be readily predicted from the primary sequences of the target polynucleotide and candidate oligonucleotides. In the methods of the present invention, oligonucleotides are filtered based on one or more of these parameters, then further filtered based on the sizes of clusters of oligonucleotides along the input polynucleotide sequence. The methods or algorithms of the present invention may be carried out using either relatively simple user-written subroutines or publicly available stand-alone software applications (e.g., dynamic programming algorithm for calculating self-structure free energies of oligonucleotides). The parameter calculations may be orchestrated and the filtering algorithms may be implemented using any of a number of commercially available computer programs as a framework such as, e.g., Microsoft® Excel spreadsheet, Microsoft® Access relational database and the like. The basic steps involved in the present methods involve parsing a sequence that is complementary to a target nucleotide sequence into a set of overlapping oligonucleotide sequences, evaluating one or more parameters for each of the oligonucleotide sequences, said parameter or parameters being predictive of probe hybridization to the target nucleotide sequence, filtering the oligonucleotide sequences based on the values for each parameter, filtering the oligonucleotide sequences based on the length of contiguous sequence elements and ranking the contiguous sequence elements based on their length. We have found that oligonucleotides in the longest contiguous sequence elements generally show the highest hybridization efficiencies.
The present methods are based on our recognition that oligonucleotides showing high hybridization efficiencies tend to form clusters. It is believed that this clustering reflects local regions of the target nucleotide sequence that are unstructured and accessible for oligonucleotide binding. Oligonucleotides that are contiguous along a region of the input nucleic acid sequence are identified. These oligonucleotides are sorted based on the length of the contiguous sequence elements. The sorting approach used in the present invention apparently serves as a surrogate for the calculation of local secondary structure of the target nucleotide sequence. This is supported by our observation that treatments intended to eliminate long-range nucleic acid structure (e.g., random fragmentation) do not eliminate the differences in hybridization yields across oligonucleotide probe arrays. This implies that major determinants of efficient hybridization are local regions of the target sequence. The identification of contiguous sequence elements is a simple and efficient method for recognizing clusters of such determinants and, thus, for identifying oligonucleotide probes that exhibit high hybridization efficiency for a target nucleotide sequence.
As mentioned above one embodiment of the present invention is a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides is identified. The length of the oligonucleotides may be the same or different. The oligonucleotides are unique in that no two of the oligonucleotides are identical. The unique oligonucleotides are chosen to sample the entire length of a nucleotide sequence that is hybridizable with the target nucleotide sequence. The actual number of oligonucleotides is generally determined by the length of the nucleotide sequence and the desired result. The number of oligonucleotides should be sufficient to achieve a consensus behavior. In other words, the oligonucleotide sequences should be sufficiently numerous that several possible probes overlap or fall within a given region that is expected to yield acceptable hybridization efficiency. Since the location of these regions is not known before hand, the best strategy is to equally space the probe sequences along the sequence that is hybridizable to the target sequence. Since regions of acceptable hybridization efficiency are generally on the order of 20 nucleotides in length, a practical strategy is to space the starting nucleotides of the oligonucleotide sequences no more than five basepairs apart. If computation time needed to calculate the predictive parameters is not an issue, then the best strategy is to space the starting nucleotides one nucleotide apart. An important feature of the present invention is to determine oligonucleotides that are clustered along a region of the nucleotide sequence. The individual predictions made for individual oligonucleotide sequences are not very good. However, we have found that the predictions that are experimentally observed tend to form contiguous clusters, while the spurious predictions tend to be solitary. Thus, the number of oligonucleotides should be sufficient to achieve the desired clustering.
Preferably, a set of overlapping sequences is chosen. To this end, the subsequences are chosen so that there is overlap of at least one nucleotide from one oligonucleotide to the next. More preferably, the overlap is two or more nucleotides. Most preferably, the oligonucleotides are spaced one nucleotide apart and the predetermined number is L-N+1 oligonucleotides where L is the length of the nucleotide sequence and N is the length of the oligonucleotides. In the latter situation, the unique oligonucleotides are of identical length N. Thus, a set of overlapping oligonucleotides is a set of oligonucleotides that are subsequences derived from some master sequence by subdividing that sequence in such a way that each subsequence contains either the start or end of at least one other subsequence in the set.

An example of the above for purposes of illustration and not limitation is presented by the sequence ATGGACTTAGCATTCG (SEQ ID NO:3), from which the following set of overlapping oligonucleotides can be identified:


	ATGGACTTAGCA	(SEQ ID NO:4)

	TGGACTTAGCAT	(SEQ ID NO:5)

	GGACTTAGCATT	(SEQ ID NO:6)

	GACTTAGCATTC	(SEQ ID NO:7)

	ACTTAGCATTCG	(SEQ ID NO:8)

In this example the overlapping oligonucleotides are spaced one nucleotide apart. In other words, there is overlap of all but one nucleotide from one oligonucleotide to the next. In the example above, the original nucleotide sequence is 16 nucleotides long (L=16). The length of each of the overlapping oligonucleotides is 12 nucleotides long (N=12) and there are L-N+1=5 oligonucleotides.

The length of the oligonucleotides may be the same or different and may vary depending on the length of the nucleotide sequence. The length of the oligonucleotides is determined by a practical compromise between the limits of current chemistries for oligonucleotide synthesis and the need for longer oligonucleotides, which exhibit greater binding affinity for the target sequence and are more likely to occur only once in complicated mixtures of polynucleotide targets. Usually, the length of the oligonucleotides is from about 10 to 50 nucleotides, more usually, from about 25 to 35 nucleotides.
In the next step of the method at least one parameter that is independently predictive of the ability of each of the oligonucleotides of the set to hybridize to the target nucleotide sequence is determined and evaluated for each of the above oligonucleotides. Examples of such a parameter, by way of illustration and not limitation, is a parameter selected from the group consisting of composition factors, thermodynamic factors, chemosynthetic efficiencies, kinetic factors and mathematical combinations of these quantities.
The determination of a parameter may be carried out by known methods. For example, melting temperature of the oligonucleotide/target duplex may be determined using the nearest neighbor method and parameters appropriate for the nucleotide acids involved. For DNA/DNA parameters, see J. SantaLucia Jr., et al., (1996) Biochemistry, 35:3555. For RNA/DNA parameters, see N. Sugimoto, et al., (1995) Biochemistry, 34:11211. Briefly, these methods are based on the observation that the thermodynamics of a nucleic acid duplex can be modeled as the sum of a term arising from the entire duplex and a set of terms arising from overlapping pairs of nucleotides (“nearest neighbor” model). For a discussion of the nearest neighbor see J. SantaLucia Jr., et al., (1996) Biochemistry, supra, and N. Sugimoto, et al., (1995) Biochemistry, supra. For example, the enthalpy ΔH of the duplex formed by the sequence

ATGGACTTAGCA (SEQ ID NO:4)

and its perfect complement can be approximated by the equation
ΔH≅H _init +H _AT +H _TG +H _GG +H _GA +H _AC +H _CT +H _TT +H _TA +H _AG +H _GC +H _CA.
In the above equation, the term H_initis the initiation enthalpy for the entire duplex, while the terms H_AT, . . . , H_CAare the so-called “nearest neighbor” enthalpies. Similar equations can be written for the entropy, for the corresponding quantities for RNA homoduplexes, or for DNA/RNA heteroduplexes. The free energy can then be calculated from the enthalpy, entropy and absolute temperature, as described previously.
Predicted free energy of the most stable intramolecular structure of an oligonucleotide (ΔG_MFOLD) may be determined using the nucleic acid folding algorithm MFOLD and parameters appropriate for the oligonucleotide, e.g., DNA or RNA. For MFOLD, see J. A. Jaeger, et al., (1989), supra. For DNA folding parameters, see J. SantaLucia Jr., et al., (1996), supra. Briefly, these methods operate in two steps. First, a map of all possible compatible intramolecular base pairs is made. Second, the global minimum of the free energy of the various possible base pairing configurations is found, using the nearest neighbor model to estimate the enthalpy and entropy, the user input temperature to complete the calculation of free energy, and a dynamic programming algorithm to find the global minimum. The algorithm is computationally intensive; calculation times scale as the third power of the sequence length.

The following Table 1 summarizes groups of parameters that are independently predictive of the ability of each of the oligonucleotides to hybridize to the target nucleotide sequence together with a reference to methods for their determination. Parameters within a given group are known or expected to be strongly correlated to one another, while parameters in different groups are known or expected to be poorly correlated with one another.

TABLE 1


Group	Parameter	Source or Reference

I	duplex enthalpy, ΔH	Santa Lucia et al., 1996; Sugimoto et al., 1995
	duplex entropy, ΔS	Santa Lucia et al., 1996; Sugimoto et al., 1995
	duplex free energy, ΔG	ΔG = ΔH − TΔS (see text)
	melting temperature, T_m	(see text)
	mole fraction (or percent) G + C	self-explanatory
	subsequence duplex enthalpy	Santa Lucia et al., 1996; Sugimoto et al., 1995
	subsequence duplex entropy	Santa Lucia et al., 1996; Sugimoto et al., 1995
	subsequence duplex free energy	ΔG = ΔH − TΔS (see text)
	subsequence duplex T_m	(see text)
	subsequence duplex mole fraction	self-explanatory
	(or percent) G + C
II	intramolecular enthalpy, ΔH_MFOLD	Jaeger et al., 1989; Santa Lucia et al., 1996
	intramolecular entropy, ΔS_MFOLD	Jaeger et al., 1989; Santa Lucia et al., 1996
	intramolecular free energy, ΔG_MFOLD	ΔG = ΔH − TΔS (see text)
	hairpin enthalpy, ΔH_hairpin	Jaeger et al., 1989; Santa Lucia et al., 1996
	hairpin entropy, ΔS_hairpin	Jaeger et al., 1989; Santa Lucia et al., 1996
	hairpin free energy, ΔG_hairpin	ΔG = ΔH − TΔS (see text)

	intramolecular partition function, Z	$Z = \sum_{k structures} \exp (- {ΔG}_{intramolecular}^{(k)} / RT)$

III	sequence complexity	Altschul et al., 1994
	sequence information content	Altschul et al., 1994
IV	steric factors	molecular modeling or experiment
	molecular dynamic simulation	Weber & Hefland, 1979
	enthalpy, entropy & free energy of	measured experimentally
	activation
	association & dissociation rates	Patzel & Sczakiel, 1998
V	oligonucleotide chemosynthetic	measured experimentally
	efficiencies
VI	target synthetic efficiencies	measured experimentally

In a next step of the present method, a subset of oligonucleotides within the predetermined number of unique oligonucleotides is identified based on the above evaluation of the parameter. A number of mathematical approaches may be followed to sort the oligonucleotides based on a parameter. In one approach a cut-off value is established. The cut-off value is adjustable and can be optimized relative to one or more training data sets. This is done by first establishing some metric for how well a cutoff value is performing; for example, one might use the normalized signal observed for each oligonucleotide in the training set. Once such a metric is established, the cutoff value can be numerically optimized to maximize the value of that metric, using optimization algorithms well known to the art. Alternatively, the cutoff value can be estimated using graphical methods, by graphing the value of the metric as a function of one or more parameters, and then establishing cutoff values that bracket the region of the graph where the chosen metric exceeds some chosen threshold value. In essence, the cut off values are chosen so that the rule set used yields training data that maximizes the inclusion of oligonucleotides that exhibit good hybridization efficiency and minimizes the inclusion of oligonucleotides that exhibit poor hybridization efficiency.
A preferred approach to performing such a graph-based optimization of filter parameters is shown in FIG. 3. In FIG. 3, hybridization data from several different genes have been used to prepare a contour plot of relative hybridization intensity as a function of DNA/RNA heteroduplex melting temperature and free energy of the most stable intramolecular structure of the probe. Contours are shown only for regions for which there are data; the white space outside of the outermost contour indicates that there are no experimental data for that region. The details of how the data were obtained can be found in Example 1 below. A summary of the sequences and number of data points employed is shown in Table 2 below. The measured hybridization intensities for each data set were normalized prior to construction of the contour plot depicted in FIG. 3 by dividing each observed intensity by the maximum intensity observed for that gene. In addition, differences in hybridization salt concentrations and hybridization temperatures were accounted for by using the salt concentration-corrected values of the melting temperatures and by subtracting the hybridization temperature from each predicted melting temperature, respectively. The filter set determined by examination of FIG. 3 is indicated by both the dotted open box in the figure and by the inequalities above the box.

One way in which such a contour plot may be prepared involves the use of an appropriate software application such as Microsoft® Excel® or the like. For example, the cross-tabulation tool may be used in the Microsoft® Excel® program. Data is accumulated into rectangular bins that are 0.5 kcal ΔG_MFOLDwide and 2.5° C. T_mwide. In each bin the average values of ΔG_MFOLD, T_m−T_hyb, and the normalized hybridization intensity are calculated. The data is output to the software application DeltaGraph® (Deltapoint, Inc., Monterey, Calif.) and the contour plot is prepared using the tools and instructions provided.

TABLE 2


Target (GenBank	Target	No. Data		[Na⁺]
Accession No.)	Strand	Points	T_hyb	Correction

HIV protease-reverse	Sense	1,022	35° C.	−1.4° C.
transcriptase (PRT)^a
(M15654)
HIV protease-reverse	antisense	1,041	30° C.	−1.4° C.
transcriptase (PRT)^a
(M15654)
HIV protease-reverse	Sense	88	35° C.	−1.4° C.
transcriptase (PRT)^b
(M15654)
Human G3PDH	antisense	93	35° C.	−1.4° C.
(glyceraldehyde-3-
dehydrogenase)^b(X01677)
Human p53^b(X02469)	antisense	93	35° C.	−1.4° C.
Rabbit β-globin^c(K03256)	antisense	106	30° C.	0° C.

^aData from Affymetrix GeneChip ™ Array
^bData from biotinylated probes bound to streptavidin-coated microtiter wells
^cLiterature data: see N. Milner, K.U. Mir & E.M. Southern (1997) Nature Biotech. 15, 537-541.

Once the cut-off value is selected, a subset of oligonucleotides having parameter values greater than or equal to the cut-off value is identified. This refers to the inclusion of oligonucleotides in a subset based on whether the value of a predictive parameter satisfies an inequality.
Examples of identifying a subset of oligonucleotides by establishing cut-off values for predictive parameters are as follows: for melting temperature an inequality might be 60° C.≦T_m; for predicted free energy an inequality, preferably, might be $Δ G_{MFOLD} \geq - 0.4 \frac{kcal}{mole} .$
In a variation of the above, both a maximum and a minimum cut-off value may be selected. A subset of oligonucleotides is identified whose values fall within the maximum and minimum values, i.e., values greater than or equal to the minimum cut-off value and less than or equal to the maximum cut-off value. An example of this approach for melting temperature might be the inequality 60° C.≦T_m≦85° C.
With regard to cut off values for T_mthe lower limit is most important, and is preferably T_m=T_hyb, more preferably, T_m=T_hyb+15° C. The upper cutoff is important when the sequence region under consideration is unusually rich in G and C, and is preferably T_m=T_hyb+40° C. With regard to ΔG_MFOLDthe cutoff value is usually greater than or equal to −1.0 kcal/mole. As mentioned above, the cutoff values preferably are determined from real data through experimental observations.
In another approach the parameter values may be converted into dimensionless numbers. The parameter value is converted into a dimensionless number by determining a dimensionless score for each parameter resulting in a distribution of scores having a mean value of zero and a standard deviation of one. The dimensionless score is a number that is used to rank some object (such as an oligonucleotide) to which that score relates. A score that has no units (i.e., a pure number) is called a dimensionless score.
In one approach the following equations are used for converting the values of said parameters into dimensionless numbers: $s_{i, x} = \frac{x_{i} - 〈 x 〉}{σ_{{x}}},$
where s_i,xis the dimensionless score derived from parameter x calculated for oligonucleotide i, x_iis the value of parameter x calculated for oligonucleotide i, <x> is the average of parameter x calculated for all of the oligonucleotides under consideration for a given nucleotide sequence target, and σ_{x} is the standard deviation of parameter x calculated for all of the oligonucleotides under consideration for a given nucleotide sequence target, and is given by the equation $σ_{{x}} = \sqrt{\frac{\sum_{j = 1}^{M} {(x_{j} - 〈 x 〉)}^{2}}{M - 1}},$
where M is the number of oligonucleotides. The resulting distribution of scores, {s} has a mean value of zero and a standard deviation of one. These properties can be important for a combination of the scores discussed below.
The use of a dimensionless number approach may further include calculating a combination score S_iby evaluating a weighted average of the individual values of the dimensionless scores s_i,xby the equation: $S_{i} = \sum_{{x}} q_{x} s_{i, x},$
where q_xis the weight assigned to the score derived from parameter x, the individual values of q_xare always greater than zero, and the sum of the weights q_xis unity.
In another variation of the above approach, the method of calculation of the composite parameter is optimized based on the correlation of the individual composite scores to real data, as explained more fully below.
In one approach the calculation of the composite score further involves determining a moving window-averaged combination score <S_i> for the ith probe by the equation: $〈 S_{i} 〉 = \frac{1}{w} \sum_{j = i - \frac{w - 1}{2}}^{i + \frac{w - 1}{2}} S_{j}, w = an odd integer,$
where w is the length of the window for averaging (i.e., w nucleotides long), and then applying a cutoff filter to the value of <S_i>. This procedure results in smoothing (smoothing procedure) by turning each score into a consensus metric for a set of w adjacent oligonucleotide probes. The score, referred to as the “smoothed score,” is essentially continuous rather than a few discrete values. The value of the smoothed score is strongly influenced by clustering of scores with high or low values; window averaging therefore provides a measurement of cluster size.
An advantage of the dimensionless score approach to the probe prediction algorithm is that it is easy to objectively optimize. In one approach to training the algorithm, optimization of the weights qx above may be performed by varying the values of the weights so that the correlation coefficient ρ_{<Si>},{Vi} between the set of window-averaged combination scores {<S_i>} and a set of calibration experimental measurements {V_i} is maximized. The correlation coefficient ρ_{<Si>},{Vi} is calculated from the equation $ρ_{{< S_{i} >}, {V_{i}}} = (\frac{1}{M}) \frac{Covariance (〈 S 〉, V)}{σ_{{< S_{i} >}} σ_{{V_{i}}}},$
where M is the number of window averaged, combination dimensionless scores and the number of corresponding measurements, the covariance is as defined earlier (see earlier equations) and σ_{<Si>} and σ_{Vi} are the standard deviations of {<S_i>} and {V_i}, as defined previously. An example of this approach is shown in Example 2, below.
In another approach the parameter is derived from one or more factors by mathematical transformation of the factors. This involves the calculation of a new predictive parameter from one or more existing predictive parameters, by means of an equation. For instance, the equilibrium constant K_openfor formation of an oligonucleotide with no intramolecular structure from its structured form can be calculated from the intramolecular structure free energy ΔG_MFOLD, using the equation: $K_{open} = \exp (\frac{Δ G_{MFOLD}}{R T}) .$
In a next step of the method oligonucleotides in the subset are then identified that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence. For example, consider a set of overlapping oligonucleotides identified by dividing a nucleotide sequence into subsequences. A subset of the oligonucleotides is obtained as described above. In general, this subset is obtained by applying a rule that rejects some members of the set. For the remaining members of the set, namely, the subset, there will be some average number of nucleotides in the nucleotide sequence between the first nucleotides of adjacent remaining subsequences. If, for some sub-region of the nucleotide sequence, the average number of nucleotides in the nucleotide sequence between the first nucleotides of adjacent remaining subsequences is less than the average for the entire nucleotide sequence, then the oligonucleotides are clustered. The smaller the average number of nucleotides between the first nucleotides of adjacent oligonucleotides, the stronger the clustering. The strongest clustering occurs when there are no intervening nucleotides between adjacent starting nucleotides. In this case, the oligonucleotides are said to be contiguous and may be referred to as contiguous sequence elements or “contigs.”
Accordingly, in this step oligonucleotides are sorted based on length of contiguous sequence elements. Oligonucleotides in the subset determined above are identified that are contiguous along a region of the input nucleic acid sequence. The length of each contig that is equal to the number of oligonucleotides in each contig, namely, oligonucleotides from the above step whose complement begin at positions m+1, m+2., m+k in the target sequence, form a contig of length k. Contigs can be identified and contig length can be calculated using, for example, a Visual Basic® module that can be incorporated into a Microsoft® Excel workbook.
Cluster size can be defined in several ways:

For contiguous clusters, the size is simply the number of adjacent oligonucleotides in the cluster. Again, this may also be referred to as contiguous sequence elements. The number may also be referred to as “contig length”. For example, consider the nucleotide sequence discussed above, namely, ATGGACTTAGCATTCG (SEQ ID NO:3) and the identified set of overlapping oligonucleotides

Suppose that, after calculation and evaluation of the predictive parameters, four nucleotides remain:


ATGGACTTAGCA	(SEQ ID NO:4)	▮
TGGACTTAGCAT	(SEQ ID NO:5)	▮	contig
GGACTTAGCATT	(SEQ ID NO:6)	▮

ACTTAGCATTCG	(SEQ ID NO:8)	▮	single
			oligonucleotide

A “contig” encompassing three of the oligonucleotides of the subset is present together with a single oligonucleotide. The contig length is 3 oligonucleotides.

Alternatively, cluster size at some position in the sequence hybridizable or complementary to the target sequence may be defined as the number of oligonucleotides whose center nucleotides fall inside a region of length M centered about the position in question, divided by M. This definition of clustering allows small gaps in clusters. In the example used above for contiguous clusters, if M was 10, then the cluster size would step through the values 0/10, . . . , 0/10, 1/10, 2/10, 3/10, 3/10, 4/10, 4/10, 4/10, 4/10, 4/10, 3/10, 2/10, 1/10, 1/10, 0/10 as the center of the window of length 10 passed through the cluster. In each fraction, the numerator is the number of oligonucleotide sequences that have satisfied the filter set and whose central nucleotides are within a window 10 nucleotides long, centered about the nucleotide under consideration. The denominator (10) is simply the window length.
Another alternative is to define the size of a cluster at some position in the sequence hybridizable or complementary to the target sequence as the number of oligonucleotide sequences overlapping that position. This definition is equivalent to the last definition with M set equal to the oligonucleotide probe length and omission of the division by M.
Finally, cluster size can be approximated at each position in a nucleotide sequence by dividing the sequence into oligonucleotides, evaluating a numerical score for each oligonucleotide, and then averaging the scores in the neighborhood of each position by means of a moving window average as described above. Window averaging has the effect of reinforcing clusters of high or low values around a particular position, while canceling varying values about that position. The window average, therefore, provides a score that is sensitive to both the hybridization potential of a given oligonucleotide and the hybridization potentials of its neighbors.
In a next step of the present method, the oligonucleotides in the subset are ranked. Generally, this ranking is based on the lengths of the clusters or contigs, sizes of the clusters or values of a window averaged score. Oligonucleotides found in the longest contigs or largest clusters, or possessing the highest window averaged scores usually show the highest hybridization efficiencies. Often, the highest signal intensity within the cluster corresponds to the median oligonucleotide of the cluster. However, the peak signal intensity within the contig can be determined experimentally, by sampling the cluster at its first quartile, midpoint and third quartile, measuring the hybridization efficiencies of the sampled oligonucleotides, interpolating or extrapolating the results, predicting the position of the optimal probe, and then iterating the probe design process.
FIG. 1 shows a diagram of an example of the above-described method by way of illustration and not limitation. Referring to FIG. 1 a target sequence of length L from, e.g., a database, is used to generate a sequence that is hybridizable to the target sequence from which candidate oligonucleotide probe sequences are generated. One or more parameters are calculated for each of the oligonucleotide probe sequences. The candidate oligonucleotide probe sequences are filtered based on the values of the parameters. Clustering of the filtered candidate probe sequences is evaluated and the clusters are ranked by size. Then, the oligonucleotide probes are statistically sampled and synthesized. Further evaluation may be made by evaluating the hybridization of the selected oligonucleotide probes in real hybridization experiments. The above process may be reiterated to further define the selection. In this way only a small fraction of the potential oligonucleotide probe candidates are synthesized and tested. This is in sharp contrast to the known method of synthesizing and testing all or a major portion of potential oligonucleotide probes for a given target sequence.
The methods of the present invention are preferably carried out at least in part with the aid of a computer. For example, an IBM® compatible personal computer (PC) may be utilized. The computer is driven by software specific to the methods described herein.
The preferred computer hardware capable of assisting in the operation of the methods in accordance with the present invention involves a system with at least the following specifications: Pentium® processor or better with a clock speed of at least 100 MHz, at least 32 megabytes of random access memory (RAM) and at least 80 megabytes of virtual memory, running under either the Windows 95 or Windows NT 4.0 operating system (or successor thereof).
As mentioned above, software that may be used to carry out the methods may be either Microsoft Excel or Microsoft Access, suitably extended via user-written functions and templates, and linked when necessary to stand-alone programs that calculate specific parameters (e.g., MFOLD for intramolecular thermodynamic parameters). Examples of software programs used in assisting in conducting the present methods may be written, preferably, in Visual BASIC, FORTRAN and C++, as exemplified below in the Examples. It should be understood that the above computer information and the software used herein are by way of example and not limitation. The present methods may be adapted to other computers and software. Other languages that may be used include, for example, PASCAL, PERL or assembly language.
FIG. 2 depicts a more specific approach to a method in accordance with the present invention. Referring to FIG. 2, a sequence of length L is obtained from a database such as GenBank, UniGene or a proprietary sequence database. Probe length N is determined by the user based on the requirements for sensitivity and specificity and the limitations of the oligonucleotide synthetic scheme employed. The probe length and sequence length are used to generate L-N+1 candidate oligonucleotide probes, i.e., from every possible starting position. An initial selection is made based on local sequence predicted thermodynamic properties. To this end, melting temperature T_mand the self-structure free energy ΔG_MFOLD, are calculated for each of the potential oligonucleotide probe: target nucleotide sequence complexes. Next, M probes that satisfy T_mand ΔG_MFOLDfilters are selected. A further selection can be made based on clustering of “good” parameters. Good parameters are parameters that satisfy all of the filters in the filter set. Clustering is defined by any of the methods described previously; in FIG. 2, the “contig length” definition of clustering is used.
For each of the M oligonucleotide sequences that satisfied all filters the question is asked whether the oligonucleotide sequence immediately following the sequence under consideration is also one of the sequences that satisfied all of the filters. If the answer to this question is NO, then one stores the current value of the contig length counter, resets the counter to zero and proceeds to the next oligonucleotide sequence that satisfied all filters. If the answer to the question is YES, then 1 is added to the contig length counter and, if the counter now equals 1 (i.e., this is the first oligonucleotide probe sequence in the contig), the starting position of the oligonucleotide is stored. One then moves to the next oligonucleotide that satisfied all filters, which, in this case, is the same as the next oligonucleotide before the application of the filter set. The process is repeated until all M filtered oligonucleotide sequences have been examined. In this way, a single pass through the set of M filtered oligonucleotide sequences generates the lengths and starting positions of all contigs.
Next, contigs are ranked based on the lengths of their contiguous sequence elements. Longer contig lengths generally correlate with higher hybridization efficiencies. All oligonucleotides of the higher-ranking contigs may be considered, or candidate oligonucleotide probes may be picked. For example, candidate oligonucleotide probes can be picked one quarter, one half and three quarters of the way through each contig. The latter approach provides local curvature determination after experimental determination of hybridization efficiencies, which allows either interpolation or extrapolation of the positions of the next probes to be synthesized in order to close in on the optimal probe in the region. If the contig brackets the actual peak of hybridization efficiency, the process will converge in 2-3 iterations. If the contig lies to one side of the actual peak, the process will converge in 34 iterations.
The above illustrative approach is further described with reference to the following DNA nucleotide sequence, which is the complement of the target RNA nucleotide sequence:

GTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAA (SEQ ID NO:9)

CTCATGTTCAAGA.

In the first step of the method, the nucleotide sequence is divided into overlapping oligonucleotides that are 25 nucleotides in length. This length is chosen because it is an effective compromise between the need for sensitivity (enhanced by longer oligonucleotides) and the chemosynthetic efficiency of schemes for synthesis of surface-bound arrays of oligonucleotide probes.

Next, the estimated duplex melting temperatures (T_m) and self-structure free energies (ΔG_MFOLD) are calculated for each oligonucleotide in the set of overlapping oligonucleotides. The values are obtained from a user-written function that calculates DNA/RNA heteroduplex thermodynamic parameters (see N. Sugimoto, et al., Biochemistry, 34:11211 (1995)) and a modified version of the program MFOLD that estimates the free energy of the most stable intramolecular structure of a single stranded DNA molecule (see J. A. Jaeger, et al., (1989), supra, respectively. The steps are illustrated below.

GTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGA (target complement sequence)

	T_m(° C.)	ΔG_MFOLD

GTCCAAAAAGGGTCAGTCTACCTCC	71.77	−1.20	SEQ ID NO:10

TCCAAAAAGGGTCAGTCTACCTCCC	71.99	−1.20	SEQ ID NO:11

CCAAAAAGGGTCAGTCTACCTCCCG	70.78	−1.20	SEQ ID NO:12

CAAAAAGGGTCAGTCTACCTCCCGC	71.23	−1.20	SEQ ID NO:13

AAAAAGGGTCAGTCTACCTCCCGCC	73.07	−1.20	SEQ ID NO:14

AAAAGGGTCAGTCTACCTCCCGCCA	75.68	−1.20	SEQ ID NO:15

AAAGGGTCAGTCTACCTCCCGCCAT	77.53	−1.20	SEQ ID NO:16

AAGGGTCAGTCTACCTCCCGCCATA	79.03	−1.20	SEQ ID NO:17

AGGGTCAGTCTACCTCCCGCCATAA	79.03	−1.20	SEQ ID NO:18

GGGTCAGTCTACCTCCCGCCATAAA	76.85	−1.20	SEQ ID NO:19

GGTCAGTCTACCTCCCGCCATAAAA	73.10	−0.80	SEQ ID NO:20

GTCAGTCTACCTCCCGCCATAAAAA	69.50	0.90	SEQ ID NO:21

TCAGTCTACCTCCCGCCATAAAAAA	65.60	0.90	SEQ ID NO:22

CAGTCTACCTCCCGCCATAAAAAAC	64.96	0.90	SEQ ID NO:23

AGTCTACCTCCCGCCATAAAAAACT	65.	1.10	SEQ ID NO:24

GTCTACCTCCCGCCATAAAAAACTC	66.36	2.40	SEQ ID NO:25

TCTACCTCCCGCCATAAAAAACTCA	64.97	2.90	SEQ ID NO:26

CTACCTCCCGCCATAAAAAACTCAT	63.96	2.70	SEQ ID NO:27

TACCTCCCGCCATAAAAAACTCATG	62.58	1.10	SEQ ID NO:28

ACCTCCCGCCATAAAAAACTCATGT	65.10	0.40	SEQ ID NO:29

CCTCCCGCCATAAAAAACTCATGTT	64.96	0.10	SEQ ID NO:30

CTCCCGCCATAAAAAACTCATGTTC	63.37	−0.10	SEQ ID NO:31

TCCCGCCATAAAAAACTCATGTTCA	62.86	−0.10	SEQ ID NO:32

CCCGCCATAAAAAACTCATGTTCAA	60.47	−0.10	SEQ ID NO:33

CCGCCATAAAAAACTCATGTTCAAG	57.98	−0.10	SEQ ID NO:34

CGCCATAAAAAACTCATGTTCAAGA	56.20	−0.10	SEQ ID NO:35

Next, the oligonucleotide sequences are filtered on the basis of T_m. A high and low cut-off value may be selected, for example, 60° C.≦T≦85° C. Thus, oligonucleotides having T_mvalues falling within the above range are retained. Those outside the range are discarded, which is indicated below by lining out of those oligonucleotides and parameter values.

Next, the oligonucleotide sequences remaining after the above exercise are filtered on the basis of ΔG_MFOLDand are retained if the value is greater than −0.4. Those oligonucleotides with a ΔG_MFOLDless than −0.4 are discarded, which is indicated below by double lining out of those oligonucleotides and parameter values.



	(target comple-
	ment sequence)

GTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGA	T_m(° C.)	ΔG_MFOLD

	71.77

	71.99

	70.78

	71.23

	73.07

	75.68

	77.53

	79.03

	79.03

	76.85

	73.10

GTCAGTCTACCTCCCGCCATAAAAA	69.50	0.90

TCAGTCTACCTCCCGCCATAAAAAA	65.60	0.90

CAGTCTACCTCCCGCCATAAAAAAC	64.96	0.90

AGTCTACCTCCCGCCATAAAAAACT	65.48	1.10

GTCTACCTCCCGCCATAAAAAACTC	66.36	2.40

TCTACCTCCCGCCATAAAAAACTCA	64.97	2.90

CTACCTCCCGCCATAAAAAACTCAT	63.96	2.70

TACCTCCCGCCATAAAAAACTCATG	62.58	1.10

ACCTCCCGCCATAAAAAACTCATGT	65.10	0.40

CCTCCCGCCATAAAAAACTCATGTT	64.96	0.10

CTCCCGCCATAAAAAACTCATGTTC	63.37	−0.10

TCCCGCCATAAAAAACTCATGTTCA	62.86	−0.10

CCCGCCATAAAAAACTCATGTTCAA	60.47	−0.10

		−0.10

		−0.10

Clusters of retained oligonucleotides are identified and ranked based on cluster size. In this example, a contiguous cluster of 13 retained oligonucleotides is identified by the vertical black bar on the left. Any or all of the oligonucleotides in this cluster may be evaluated experimentally.



	(target comple-
	ment sequence)

GTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGA	T_m(° C.)	ΔG_MFOLD

	71.77

	71.99

	70.78

	71.23

	73.07

	75.68

	77.53

	79.03

	79.03

	76.85

	73.10

\| GTCAGTCTACCTCCCGCCATAAAAA	69.50	0.90

\| TCAGTCTACCTCCCGCCATAAAAAA	65.60	0.90

\| CAGTCTACCTCCCGCCATAAAAAAC	64.96	0.90

\| AGTCTACCTCCCGCCATAAAAAACT	65.48	1.10

\| GTCTACCTCCCGCCATAAAAAACTC	66.36	2.40

\| TCTACCTCCCGCCATAAAAAACTCA	64.97	2.90

\| CTACCTCCCGCCATAAAAAACTCAT	63.96	2.70

\| TACCTCCCGCCATAAAAAACTCATG	62.58	1.10

\| ACCTCCCGCCATAAAAAACTCATGT	65.10	0.40

\| CCTCCCGCCATAAAAAACTCATGTT	64.96	0.10

\| CTCCCGCCATAAAAAACTCATGTTC	63.37	−0.10

\| TCCCGCCATAAAAAACTCATGTTCA	62.86	−0.10

\| CCCGCCATAAAAAACTCATGTTCAA	60.47	−0.10

		−0.10

		−0.10

Alternatively, in one approach the oligonucleotides at the first quartile, the median and the third quartile of the cluster may be selected for experimental evaluation, indicated below by bold print.



	(target comple-
	ment sequence)

GTCCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGA	T_m(° C.)	ΔG_MFOLD

	71.77

	71.99

	70.78

	71.23

	73.07

	75.68

	77.53

	79.03

	79.03

	76.85

	73.10

\| GTCAGTCTACCTCCCGCCATAAAAA	69.50	0.90

\| TCAGTCTACCTCCCGCCATAAAAAA	65.60	0.90

\| CAGTCTACCTCCCGCCATAAAAAAC	64.96	0.90

\| AGTCTACCTCCCGCCATAAAAAACT	65.48	1.10

\| GTCTACCTCCCGCCATAAAAAACTC	66.36	2.40

\| TCTACCTCCCGCCATAAAAAACTCA	64.97	2.90

\| CTACCTCCCGCCATAAAAAACTCAT	63.96	2.70

\| TACCTCCCGCCATAAAAAACTCATG	62.58	1.10

\| ACCTCCCGCCATAAAAAACTCATGT	65.10	0.40

\| CCTCCCGCCATAAAAAACTCATGTT	64.96	0.10

\| CTCCCGCCATAAAAAACTCATGTTC	63.37	−0.10

\| TCCCGCCATAAAAAACTCATGTTCA	62.86	−0.10

\| CCCGCCATAAAAAACTCATGTTCAA	60.47	−0.10

		−0.10

		−0.10

In one aspect of the present method, at least two parameters are determined wherein the parameters are poorly correlated with respect to one another. The reason for requiring that the different parameters chosen are poorly correlated with one another is that an additional parameter that is strongly correlated to the original parameter brings no additional information to the prediction process. The correlation to the original parameter is a strong indication that both parameters represent the same physical property of the system. Another way of stating this is that correlated parameters are linearly dependent on one another, while poorly correlated parameters are linearly independent of one another. In practice, the absolute value of the correlation coefficient between any two parameters should be less than 0.5, more preferably, less than 0.25, and, most preferably, as close to zero as possible.
In one preferred approach instead of T_m, for each oligonucleotide/target nucleotide sequence duplex, the difference between the predicted duplex melting temperature corrected for salt concentration and the temperature of hybridization of each of the oligonucleotides with the target nucleotide sequence is determined.
In one aspect the present method comprises determining two parameters at least one of the parameters being the association free energy between a subsequence within each of the oligonucleotides and its complementary sequence on the target nucleotide sequence, or some similar, strongly correlated parameter. The object of this approach is to identify a particularly stable subsequence of the oligonucleotide that might be capable of acting as a nucleation site for the beginning of the heteroduplex formation between the oligonucleotide and the target nucleotide sequence. Such nucleation is believed to be the rate-limiting step for process of heteroduplex formation.
The subsequence within the oligonucleotide is from about 3 to 9 nucleotides in length, usually, 5 to 7 nucleotides in length. The subsequence is at least three nucleotides from the terminus of the oligonucleotide. For support-bound oligonucleotides the subsequence is at least three nucleotides from the free end of the oligonucleotide, i.e., the end that is not attached to the support. Generally, this free end is the 5′ end of the oligonucleotide. When the oligonucleotide is attached to a support, the subsequence is at least three nucleotides from the end of the oligonucleotide that is bound to the surface of the support to which the oligonucleotide is attached. Generally, the 3′ end of the oligonucleotide is bound to the support.
The predictive parameter can be, for example, either melting temperature or duplex free energy of the subsequence with the target nucleotide sequence. The subsequence with the maximum (melting temperature) or minimum (free energy) value of one of the above parameters is chosen as the representative subsequence for that oligonucleotide probe. For example, if the oligonucleotide is 20 nucleotides in length and a subsequence of 5 nucleotides is chosen, i.e., a 5-mer, then parameter values are calculated for all 5-mer subsequences of the oligonucleotide that do not include the 2 nucleotides at the free end of the oligonucleotide. Where 5′ is the free end of the oligonucleotide with designated nucleotide number 1, the values are calculated for all 5-mer subsequences with starting nucleotides from position number 3 to position number 16. Thus, in this example, parameter values for 14 different subsequences are calculated. The subsequence with the maximum value for the parameter is then assigned as the stability subsequence for the oligonucleotide.
The inclusion of the above determination of a stability subsequence results in the following algorithm for determining the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides are identified within a nucleotide sequence that is hybridizable with said target nucleotide sequence. The oligonucleotides are chosen to sample the entire length of the nucleotide sequence. For each of the oligonucleotides, parameters that are independently predictive of the ability of each of said oligonucleotides to hybridize to said target nucleotide sequence are determined and evaluated. Two parameters that may be used are the thermodynamic parameters of T_mand ΔG_MFOLD. These parameters give rise to associated parameter filters. In one approach evaluation of the parameters involves establishing cut-off values as described above. Application of these cut-off values results in the identification of a subset of oligonucleotides for further scrutiny under the algorithm. In accordance with this embodiment of the present invention, there is included a stability subsequence limit in addition to the above. Cutoff values are determined either by means of objective optimization algorithms well known to the art or via graphical estimation methods; both approaches have been described previously in this document. In either case, the optimization of cutoff values involves comparison of predictions to known hybridization efficiency data sets. This process results in objective optimization as it looks at prediction versus experimental results and is otherwise referred to herein as “training the algorithm.” The experimental data used to train the algorithm is referred to herein as “training data.”
In the present approach filters are assigned to the T_moligonucleotide probe data. The T_mof each oligonucleotide probe needs to be greater than or equal to the assigned filter (T_mprobe limit) to be given a filter score of “1”; otherwise, the filter score is “0”. In addition, one can also impose a second filter for this parameter; that is, that the T_mof the oligonucleotide probe also has to be less than a defined upper limit. Filters are also assigned to the ΔG_MFOLDdata. The ΔG_MFOLDof each oligonucleotide probe should be greater than or equal to the assigned filter (ΔG_MFOLDlimit) to be given a filter score of “1”; otherwise, the filter score is “0”. The filter scores are added. Furthermore, one can also impose a second filter for this parameter; that is, that the ΔG_MFOLDalso has to be less than a defined upper limit. In accordance with the above discussion stability subsequences are identified. This leads to another filter. Accordingly, filters are assigned to the stability sequence data. The stability subsequence of each oligonucleotide probe needs to be greater than or equal to the assigned filter limit to be given a filter score of “1”; otherwise, the filter score is “0”. In addition, one can also impose a second filter for this parameter; that is, that the stability subsequence also has to be less than a defined upper limit. In all cases, the filter values are determined by objective optimization (algorithmic or graphical) of the predictions of the present method versus training data, as described previously.
On the basis of the above filter sets a subset of oligonucleotides within said predetermined number of unique oligonucleotides is identified. Oligonucleotides in the subset are identified that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence. The resulting number of oligonucleotide probe regions is examined. The above filters may then be loosened or tightened by changing the filter limits to obtain more or fewer clusters of oligonucleotides to match the goal, which is set by the needs of the investigator. For instance, a particular application might require that the investigator design 5 non-overlapping probes that efficiently hybridize to a given target sequence.
As mentioned above, the contigs may be selected on the basis of contig length. In another approach, the scores defined above may be summed for cluster size determination. To this end the probe score of the particular filter set (e.g., T_mprobe limit, ΔG_MFOLDlimit and stability sequence limit) is calculated for each oligonucleotide probe. The probe score is the sum of the filter scores. Thus, the probe score is 0 if no parameters pass their respective filters. The probe score is 1, 2 or 3 if one, two or three parameters, respectively, pass their filters for that oligonucleotide probe. This summing is continued for each parameter that is in the current filter set of the algorithm-used. For a given algorithm a minimum probe score limit is set. In the current example this limit will be at least 1 and could be 2 or 3 depending on the needs of the investigator, the number of probe clusters required and the results of objective optimizations of algorithm performance against training data. The probe score is compared to this probe score limit. If the probe score of oligonucleotide probe i is greater than or equal to the probe score limit, then oligonucleotide probe i is assigned a score passed value of 1. Next, a window is chosen for the evaluation of clustering (the “cluster window”). This will be the next filter applied. The cluster window (“w”) smoothes the score passed values by summing the values in a window w nucleotides long, centered about position i. The resulting sum is called the cluster sum. Usually, the cluster window is an odd integer, usually 7 or 9 nucleotides. The cluster sum values are then filtered, by comparing to a user-set threshold, cluster filter. If cluster sum is greater than or equal to cluster filter, this filter is passed, and the probe is predicted to hybridize efficiently to its target.
This window summing procedure converts the score for the passed value for each oligonucleotide into a consensus metric for a set of w adjacent probes. A “consensus metric” is a measurement that distills a number of values into one consensus value. In this case, the consensus value is calculated by simply summing the individual values. The window summing procedure therefore evaluates a property similar to the contig length metric discussed above. However, the summed score has the advantage of allowing for a few probes within a cluster to have not passed their individual probe score limits. We have found that this allows more observed hybridization peaks to be predicted.
It may be desired in some circumstances to combine the results of multiple algorithm versions. We refer to this operation as “tiling”. This may be explained more fully as follows. Tiling generally involves joining together the predicted oligonucleotide probe sets identified by multiple algorithm versions. In the context of the present invention, tiling multiple algorithm versions involves forming the union of multiple sets of predictions. These predictions may arise from different embodiments of the present invention. Alternatively, the different sets of predictions may arise from the same embodiment, but different filter sets. The different filter sets may additionally be restricted to different combinations of parameter values. For instance, one filter set might be used when the predicted duplex melting temperature T_mis greater than or equal to some value, while another might be used when T_mis below that value.
An example of the logical endpoint of tiling multiple filter sets across different regions of the possible combinations of predictive parameters and then forming the union of the resulting predictions is the contour plot shown in FIG. 3, with the associated rule that “the value of the normalized hybridization intensity associated with a particular combination of (T_m−T_hyb) and ΔG_MFOLDmust be greater than or equal to some threshold value.” In this case, the contour at the threshold value becomes the filter. This contour and its interior can be thought of as the union of many small rectangular regions (“tiles”), each of which is bracketed by low and high cutoff values for each of the parameters.
The predictions of different algorithm versions can also be combined by forming the intersection of two or more different predictions. The reliability of predictions within such intersection sets is enhanced because such sets are, by definition, insensitive to changes in the details of the predictive algorithm. Intersection is a useful method for reducing the number of predicted probes when a single algorithm version produces too many candidate probes for efficient experimental evaluation.
The most specific oligonucleotide probe set (i.e., the set least likely to include poor probes) will be the intersection set from multiple algorithms. Clusters that have overlapping oligonucleotide probes from multiple algorithms constitute the intersection set of oligonucleotide probes. The oligonucleotide probe that is in the center of an intersection cluster is chosen. This central oligonucleotide probe may have the highest probability of predicting a peak or, in other words, of binding well to the target nucleotide sequence. Oligonucleotide probes on either side of center, which are still within the intersection cluster, may also be selected. The distance of these “side” oligonucleotide probes from the center generally will be shorter or longer depending upon the length of the cluster.
The most sensitive set of oligonucleotide probes (i.e., the set most likely to include at least one good probe) is generally the union set from multiple algorithms. Clusters that are predicted by at least one type of algorithm constitute the union set of oligonucleotide probes. The oligonucleotide probe in the center of a union cluster is chosen. Oligonucleotide probes on either side of center, which are still within the union cluster, usually are also chosen. The distance of these side probes from the center will be shorter or longer depending upon the length of the cluster. In summary, the combination of using the stability subsequence parameter, tiling multiple filter sets, and making union and intersection cluster sets of oligonucleotide probes exhibits very high sensitivity and specificity in predicting oligonucleotide probes that effectively hybridize to a target nucleotide sequence of interest.
Another aspect of the present invention is a computer based method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. A predetermined number of unique oligonucleotides within a nucleotide sequence that is hybridizable with the target nucleotide sequence is identified under computer control. The oligonucleotides are chosen to sample the entire length of the nucleotide sequence. A value is determined and evaluated under computer control for each of the oligonucleotides for at least one parameter that is independently predictive of the ability of each of the oligonucleotides to hybridize to the target nucleotide sequence. The parameter values are stored. Based on the examination of the stored parameter values, a subset of oligonucleotides within the predetermined number of unique oligonucleotides is identified under computer control. Then, oligonucleotides in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence are identified under computer control.
A computer program is utilized to carry out the above method steps. The computer program provides for input of a target-hybridizable or target-complementary nucleotide sequence, efficient algorithms for computation of oligonucleotide sequences and their associated predictive parameters, efficient, versatile mechanisms for filtering sets of oligonucleotide sequences based on parameter values, mechanisms for computation of the size of clusters of oligonucleotide sequences that pass multiple filters, and mechanisms for outputting the final predictions of the method of the present invention in a versatile, machine-readable or human-readable form.
Another aspect of the present invention is a computer system for conducting a method for predicting the potential of an oligonucleotide to hybridize to a target nucleotide sequence. An input means for introducing a target nucleotide sequence into the computer system is provided. The input means may permit manual input of the target nucleotide sequence. The input means may also be a database or a standard format file such as GenBank. Also included in the system is means for determining a number of unique oligonucleotide sequences that are within a nucleotide sequence that is hybridizable with the target nucleotide sequence. The oligonucleotide sequences is chosen to sample the entire length of the nucleotide sequence. Suitable means is a computer program or software, which also provides memory means for storing the oligonucleotide sequences. The system also includes means for controlling the computer system to carry out a determination and evaluation for each of the oligonucleotide sequences a value for at least one parameter that is independently predictive of the ability of each of the oligonucleotide sequences to hybridize to the target nucleotide sequence. Suitable means is a computer program or software such as, for example, Microsoft® Excel spreadsheet, Microsoft® Access relational database or the like, which also provides memory means for storing the parameter values. The system further comprises means for controlling the computer to carry out an identification of a subset of oligonucleotide sequences within the number of unique oligonucleotide sequences based on the automated examination of the stored parameter values. Suitable means is a computer program or software, which also allocates memory means for storing the subset of oligonucleotides. The system also includes means for controlling the computer to carry out an identification of oligonucleotide sequences in the subset that are clustered along a region of the nucleotide sequence that is hybridizable to the target nucleotide sequence. Suitable means is a computer program or software, which also allocates memory means for storing the oligonucleotide sequences in the subset. The computer system also includes means for outputting data relating to the oligonucleotide sequences in the subset. Such means may be machine readable or human readable and may be software that communicates with a printer, electronic mail, another computer program, and the like. One particularly attractive feature of the present invention is that the outputting means may communicate directly with software that is part of an oligonucleotide synthesizer. In this way the results of the method of the present invention may be used directly to provide instruction for the synthesis of the desired oligonucleotides.
Another advantage of the present invention is that it may be used to predict efficient hybridization oligonucleotides for each of multiple target sequences. Thus, very large arrays may be constructed and tested with minimal synthesis of oligonucleotides.

EXAMPLES

The invention is demonstrated further by the following illustrative examples. Parts and percentages are by weight unless otherwise indicated. Temperatures are in degrees Centigrade (° C.) unless otherwise specified. The following preparations and examples illustrate the invention but are not intended to limit its scope. All reagents used herein were from Amresco, Inc., Solon, Ohio (buffers), Pharmacia Biotech, Piscataway, N.J. (nucleoside triphosphates) or Promega, Madison, Wis. (RNA polymerases) unless indicated otherwise.

Example 1

Synopsis: Data from labeled RNA target hybridizations to surface-bound DNA probes directed against 4 different gene sequences were compared to the predictions of the preferred version of the prediction algorithm illustrated by the flow chart in FIG. 2. The RNA targets were sequences derived from the human immunodeficiency virus protease-reverse transcriptase region (HIV PRT; sense-strand target polynucleotide), human glyceraldehyde-3-phosphate dehydrogenase gene (G3PDH; antisense-strand target polynucleotide), human tumor suppressor p53 gene (p53; antisense-strand target polynucleotide) and rabbit β-globin gene (β-globin; antisense-strand target polynucleotide). The GenBank accession numbers for the gene sequences, number of data points collected and temperature of hybridization have all been previously listed in Table 2.
Materials and Methods: Three different experimental systems and two different labeling schemes were used to collect data.
The sequence and hybridization data for β-globin were taken from the literature (see Milner et al., (1997), supra; in this experiment, ³²P-radiolabeled RNA target was used.
The hybridization data for HIV PRT were obtained using an Affymetrix GeneChip™ HIV PRT-sense probe array (i.e. sense strand target polynucleotide) (GeneChip™ HIV PRT 440s, Affymetrix Corporation, Santa Clara, Calif.) as specified by the manufacturer, except that the fluorescein-labeled RNA target was not fragmented prior to hybridization and that hybridization was performed for 24 hours. The concentration of fluorescein-labeled RNA used was 26.3 nM; label density was approximately 18 fluoresceinated uridyl nucleotides per 1 kilobase (kb) RNA transcript. The raw data were collected by scanning the array with a GeneChip™ Scanner 50 (Affymetrix Corporation, Santa Clara, Calif.), as specified by the manufacturer. The raw data were reduced to a feature-averaged (“.CEL”) file, using the GeneChip™ software supplied with the scanner. Finally, a table of hybridization intensities for perfect-complement 20-mer probes was constructed using the ASCII feature map file supplied with the GeneChip™ software to connect probe sequences to measured hybridization intensities. The resulting data set contained data for every overlapping 20-mer probe to the target sequence.
The data for G3PDH and p53 were measured using 93-feature arrays constructed using commercially available streptavidin-coated microtiter plates (Pierce Chemical Company, Rockford, Ill.). Every tenth possible 25-mer probe complementary to each target was synthesized and 3′-biotinylated by a contract synthesis vendor (Operon, Inc., Alameda, Calif.). The 3′-linked biotin was used to anchor individual probes to microtiter wells, via the well known, strong affinity of streptavidin for biotin. Biotinylated DNA probes were resuspended to a concentration of 10 μM in hybridization buffer (5× sodium chloride-sodium phosphate-disodium ethylenediaminetetraacetate (SSPE), 0.05% Triton X-100, filter-sterilized; 1×SSPE is 150 mM sodium chloride, 10 mM sodium phosphate, 1 mM disodium ethylenediaminetetraacetate (EDTA), pH 7.4). Individual probes were diluted 1:10 in hybridization buffer into specified wells (100 μl total volume per well) of a streptavidin-coated microtiter plate; probes were allowed to bind to the covered plates overnight at 35° C. The other 3 wells of the 96-well microtiter plate were probe-less controls. The coated plates were washed with 3×200 p, of wash buffer (6×SSPE, 0.005% Triton X-100, filter-sterilized). Fluorescein-labeled RNA (100 μl of a 10 nM solution in hybridization buffer) was added to each well. The plates were covered and hybridized at 35° C. for 20-24 hours. The hybridized plates were washed with 3×200 μl of wash buffer. Label was then released in each well by adding 100 μl of 20 μg/ml RNAase I (Sigma Chemical Company, St. Louis, Mo.) in Tris-EDTA (TE) (10 mM Tris(hydroxymethyl)aminomethane (Tris), 1 mM EDTA, pH 8.0, sterile) and incubating at 35° C. for at least 30 minutes. The fluorescence released from the surface of each well was quantitated with a PerSeptive Biosystems Cytofluor II microtiter plate fluorimeter (PerSeptive Biosystems, Inc., Framingham, Mass.) using the manufacturer's recommended excitation and emission filter sets for fluorescein. Each plate hybridization was performed in quadruplicate, and the data for each probe were averaged to obtain the hybridization intensity.
Labeled RNA targets specific for G3PDH and p53 were produced via T7 RNA polymerase transcription of DNA templates in the presence of fluorescein-UTP (Boehringer Mannheim Corporation, Indianapolis, Ind.), using the same method as that outlined by Affymetrix for their GeneChip™ HIV PRT sense probe array. The DNA template for G3PDH was purchased from a commercial source (Clontech, Inc., Palo Alto, Calif.). The DNA template for p53 was obtained by sub-cloning a PCR fragment from an ATCC-derived reference clone (No. 57254) of human p53 into the commercially-available PCR cloning vector pCR2.1-TOPO (Invitrogen, Inc., Carlsbad, Calif.), then linearizing the plasmid at the end of the polycloning site opposite the vector-derived T7 promoter.
Probe predictions were performed using a software application (referred to as “p5”) that was built atop Microsoft's Access relational database application, using added Visual Basic modules, the TrueDB Grid Pro 5.0 (Apex Software Corporation, Pittsburgh, Pa.) enhancement to Visual Basic, and a version of the FORTRAN application MFOLD, modified to run in a Windows NT 4.0 environment, as an ActiveX control. The Visual Basic source code for the p5 software application is found in the Microfiche appendix to this specification. The DNA target sequence complements that were input into p5 for division into potential oligonucleotide probe sequences are listed below:

Parent Sequence Accession No.: K03256
Locus: BUNGLOB.DNA (portion of rabbit β-globin)

Length: 122

1 TTCTTCCACA TTCACCTTGC CCCACAGGGC SEQ ID NO: 36

AGTGACCGCA GACTTCTCCT CACTGGACAG

61 ATGCACCATT CTGTCTGTTT TGGGGGATTG

CAAGTAAACA CAGTTGTGTC AAAAGCAAGT

121 GT

Parent Sequence Accession No.: M15654
Locus: HIV_PRTA.S (HIV PRT antisense; parses into probes specific for sense-strand target)

Length: 1040


1	TGTACTGTCC ATTTATCAGG ATGGAGTTCA	SEQ ID NO: 37
	TAACCCATCC AAAGGAATGG AGGTTCTTTC

61	TGATGTTTTT TGTCTGGTGT GGTAAGTCCC
	CACCTCAACA GATGTTGTCT CAGCTCCTCT

121	ATTTTTGTTC TATGCTGCCC TATTTCTAAG
	TCAGATCCTA CATACAAATC ATCCATGTAT

181	TGATAGATAA CTATGTCTGG ATTTTGTTTT
	TTAAAAGGCT CTAAGATTTT TGTCATGCTA

241	CTTTGGAATA TTGCTGGTGA TCCTTTCCAT
	CCCTGTGGAA GCACATTGTA CTGATATCTA

301	ATCCCTGGTG TCTCATTGTT TATACTAGGT
	ATGGTAAATG CAGTATACTT CCTGAAGTCT

361	TCATCTAAGG GAACTGAAAA ATATGCATCA
	CCCACATCCA GTACTGTTAC TGATTTTTTC

421	TTTTTTAACC CTGCGGGATG TGGTATTCCT
	AATTGAACTT CCCAGAAGTC TTGAGTTCTC

481	TTATTAAGTT CTCTGAAATC TACTAATTTT
	CTCCATTTAG TACTGTCTTT TTTCTTTATG

541	GCAAATACTG GAGTATTGTA TGGATTCTCA
	GGCCCAATTT TTGAAATTTT CCCTTCCTTT

601	TCCATTTCTG TACAAATTTC TACTAATGCT
	TTTATTTTTT CTTCTGTCAA TGGCCATTGT

661	TTAACTTTTG GGCCATCCAT TCCTGGCTTT
	AATTTTACTG GTACAGTCTC AATAGGGCTA

721	ATGGGAAAAT TTAAAGTGCA ACCAATCTGA
	GTCAACAGAT TTCTTCCAAT TATGTTGACA

781	GGTGTAGGTC CTACTAATAC TGTACCTATA
	GCTTTATGTC CACAGATTTC TATGAGTATC

841	TGATCATACT GTCTTACTTT GATAAAACCT
	CCAATTCCCC CTATCATTTT TGGTTTCCAT

901	CTTCCTGGCA AACTCATTTC TTCTAATACT
	GTATCATCTG CTCCTGTATC TAATAGAGCT

961	TCCTTTAGTT GCCCCCCTAT CTTTATTGTG
	ACGAGGGGTC GTTGCCAAAG AGTGATCTGA

1021	GGGAAGTTAA AGGATACAGT

Parent Sequence Accession No.: X01677
Locus: G3PDH (Clontech G3PDH template—parses into probes specific for antisense-strand target)

Length: 999


1	GAAGGTCGGA GTCAACGGAT TTGGTCGTAT	SEQ ID NO: 38
	TGGGCGCCTG GTCACCAGGG CTGCTTTTAA

61	CTCTGGTAAA GTGGATATTG TTGCCATCAA
	TGACCCCTTC ATTGACCTCA ACTACATGGT

121	TTACATGTTC CAATATGATT CCACCCATGG
	CAAATTCCAT GGCACCGTCA AGGCTGAGAA

181	CGGGAAGCTT GTCATCAATG GAAATCCCAT
	CACCATCTTC CAGGAGCGAG ATCCCTCCAA

241	AATCAAGTGG GGCGATGCTG GCGCTGAGTA
	CGTCGTGGAG TCCACTGGCG TCTTCACCAC

301	CATGGAGAAG GCTGGGGCTC ATTTGCAGGG
	GGGAGCCAAA AGGGTCATCA TCTCTGCCCC

361	CTCTGCTGAT GCCCCCATGT TCGTCATGGG
	TGTGAACCAT GAGAAGTATG ACAACAGCCT

421	CAAGATCATC AGCAATGCCT CCTGCACCAC
	CAACTGCTTA GCACCCCTGG CCAAGGTCAT

481	CCATGACAAC TTTGGTATCG TGGAAGGACT
	CATGACCACA GTCCATGCCA TCACTGCCAC

541	GCAGAAGACT GTGGATGGCC CCTCCGGGAA
	ACTGTGGCGT GATGGCCGCG GGGCTCTCCA

601	GAACATCATC CCTGCCTCTA CTGGCGCTGC
	CAAGGCTGTG GGCAAGGTCA TCCCTGAGCT

661	AGACGGGAAG CTCACTGGCA TGGCCTTCCG
	TGTCCCCACT GCCAACGTGT CAGTGGTGGA

721	CCTGACCTGC CGTCTAGAAA AACCTGCCAA
	ATATGATGAC ATCAAGAAGG TGGTGAAGCA

781	GGCGTCGGAG GGGCCCCTCA AAGGCATCCT
	GGGCTACACT GAGCACCAGG TGGTCTCCTC

841	TGACTTCAAC AGCGACACCC ACTCCTCCAC
	CTTTGACGCT GGGGCTGGCA TTGCCCTCAA

901	CGACCACTTT GTCAAGCTCA TTTCCTGGTA
	TGACAACGAA TTTGGCTACA GCAACAGGGT

961	GGTGGACCTC ATGGCCCACA TGCTATAGTG
	AGTCGTATT

Parent Sequence Accession No.: X54156
Locus: HSP53PCRa (p53 template—parses into probes specific for antisense-strand target)

Length: 1049


1	GAGGTGCGTG TTTGTGCCTG TCCTGGGAGA	SEQ ID NO: 39
	GACCGGCGCA CAGAGGAAGA GAATCTCCGC

61	AAGAAAGGGG AGCCTCACCA CGAGCTGCCC
	CCAGGGAGCA CTAAGCGAGC ACTGCCCAAC

121	AACACCAGCT CCTCTCCCCA GCCAAAGAAG
	AAACCACTGG ATGGAGAATA TTTCACCCTT

181	CAGATCCGTG GGCGTGAGCG CTTCGAGATG
	TTCCGAGAGC TGAATGAGGC CTTGGAACTC

241	AAGGATGCCC AGGCTGGGAA GGAGCCAGGG
	GGGAGCAGGG CTCACTCCAG CCACCTGAAG

301	TCCAAAAAGG GTCAGTCTAC CTCCCGCCAT
	AAAAAACTCA TGTTCAAGAC AGAAGGGCCT

361	GACTCAGACT GACATTCTCC ACTTCTTGTT
	CCCCACTGAC AGCCTCCCTC CCCCATCTCT

421	CCCTCCCCTG CGATTTTGGG TTTTGGGTCT
	TTGAACCCTT GCTTGCAATA GGTGTGCGTC

481	AGAAGCACCC AGGACTTCCA TTTGCTTTGT
	CCCGGGGCTC CACTGAACAA GTTGGCCTGC

541	ACTGGTGTTT TGTTGTGGGG AGGAGGATGG
	GGAGTAGGAC ATACCAGCTT AGATTTTAAG

601	GTTTTTACTG TGAGGGATGT TTGGGAGATG
	TAAGAAATGT TCTTGCAGTT AAGGGTTAGT

661	TTACAATCAG CCACATTCTA GGTAGGTAGG
	GGCCCACTTC ACCGTACTAA CCAGGGAAGC

721	TGTCCCTCAT GTTGAATTTT CTCTAACTTC
	AAGGCCCATA TCTGTGAAAT GCTGGCATTT

781	GCACCTACCT CACAGAGTGC ATTGTGAGGG
	TTAATGAAAT AATGTACATC TGGCCTTGAA

841	ACCACCTTTT ATTACATGGG GTCTAAAACT
	TGACCCCCTT GAGGGTGCCT GTTCCCTCTC

901	CCTCTCCCTG TTGGCTGGTG GGTTGGTAGT
	TTCTACAGTT GGGCAGCTGG TTAGGTAGAG

961	GGAGTTGTCA AGTCTTGCTG GCCCAGCCAA
	ACCCTGTCTG ACAACCTCTT GGTCGACCTT

1021	AGTACCTAAA AGGAAATCTC ACCCCATCC

The sequences indicated above, which are complements of the target sequences, were divided into overlapping oligonucleotide sequences with one nucleotide between starting positions. The oligonucleotide sequence lengths were 17 (rabbit β-globin), 20 (HIV PRT) or 25 (G3PDH; p53). The oligonucleotide sequence lengths were dictated by the probe lengths used in the experiments to which the predictions were compared. The RNA target concentrations used to calculate predicted RNA/DNA duplex melting temperatures were 100 pM (rabbit β-globin), 26.3 nM (HIV PRT) and 10 nM (G3PDH; p53). These were also dictated by experimental conditions for the comparison data. The cut-off filter used for the predicted free energy of the most stable probe sequence intramolecular structure, ΔG_MFOLD, was $Δ G_{MFOLD} \geq - 0.4 \frac{kcal}{mole} .$
The filter condition used for the predicted RNA/DNA duplex melting temperature was
25° C.≦T _m+16.6 log([Na⁺])−T _hyb≦50° C.,
where T_mis the target concentration-dependent value of the predicted RNA/DNA duplex melting temperature before correction for salt concentration, the term “16.6 log([Na⁺])” corrects the melting temperature for salt effects, and T_hybis the hybridization temperature. The values of the salt correction term and T_hybhave already been listed in Table 2. For convenient use within p5, the above condition was algebraically rearranged into the equivalent form
25° C.−16.6 log([Na⁺])+T _hyb ≦T _m≦50° C.−16.6 log([Na⁺])+T _hyb.
Clusters were ranked according to the number of contiguous oligonucleotide sequences that passed through the filter set (“contig” length).

Results: The detailed analysis results for rabbit β-globin are presented in Table 3; a graphical summary of the results is shown in FIG. 4. In Table 3, values of T_mand ΔG_MFOLDthat were excluded by the filter set are shown with a line through them, and table entries for contig length are shown in gray when the oligonucleotide sequence in question was not in a contig. The top 20% of the observed hybridization intensities are shown underlined.

TABLE 3


	Oligonucleotide	SEQ ID		ΔG_MFOLD	Contig	Hybridization Intensity
Position	Sequence	NO:	T_m(° C.)	(kcal/mole)	Length	(Milner et al., 1997)

1	TTCTTCCACATTCACCT	40		5.00		100

2	TCTTCCACATTCACCTT	41		5.00		130

3	CTTCCACATTCACCTTG	42		0.90		130

4	TTCCACATTCACCTTGC	43		0.50		200

5	TCCACATTCACCTTGCC	44	58.46	0.50	7	120

6	CCACATTCACCTTGCCC	45	61.10	0.50	7	180

7	CACATTCACCTTGCCCC	46	61.10	0.50	7	230

8	ACATTCACCTTGCCCCA	47	61.10	0.50	7	220

9	CATTCACCTTGCCCCAC	48	61.10	0.90	7	320

10	ATTCACCTTGCCCCACA	49	61.10	0.70	7	310

11	TTCACCTTGCCCCACAG	50	61.33	0.50	7	320

12	TCACCTTGCCCCACAGG	51	63.70			390

13	CACCTTGCCCCACAGGG	52	64.85			410

14	ACCTTGCCCCACAGGGC	53	68.01			240

15	CCTTGCCCCACAGGGCA	54	68.63			50

16	CTTGCCCCACAGGGCAG	55	64.95			20

17	TTGCCCCACAGGGCAGT	56	66.31			20

18	TGCCCCACAGGGCAGTG	57	65.79			20

19	GCCCCACAGGGCAGTGA	58	67.37			20

20	CGCCACAGGGCAGTGAC	59	63.42			40

21	CCCACAGGGCAGTGACC	60	63.42			20

22	CCACAGGGCAGTGACCG	61	59.85			20

23	CACAGGGCAGTGACCGC	62	60.14			20

24	ACAGGGCAGTGACCGCA	63	60.14			20

25	CAGGGCAGTGACCGCAG	64	59.76			30

26	AGGGCAGTGACCGCAGA	65	59.83			20

27	GGGCAGTGACCGCAGAC	66	60.22			30

28	GGCAGTGACCGCAGACT	67	59.53			30

29	GCAGTGACCGCAGACTT	68	57.06			30

30	CAGTGACCGCAGACTTC	69				40

31	AGTGACCGCAGACTTCT	70		−0.20		40

32	GTGACCGCAGACTTCTC	71	55.99	0.60	7	100

33	TGACCGCAGACTTCTCC	72	57.01	0.60	7	120

34	GACCGCAGACTTCTCCT	73	59.22	0.60	7	180

35	ACCGCAGACTTCTCCTC	74	59.28	0.60	7	210

36	CCGCAGACTTCTCCTCA	75	60.07	0.60	7	200

37	CGCAGACTTCTCCTCAC	76	56.34	0.60	7	190

38	GCAGACTTCTCCTCACT	77	57.79	0.60	7	240

39	CAGACTTCTCCTCACTG	78		0.60		240

40	AGACTTCTCCTCACTGG	79		0.00		340

41	GACTTCTCCTCACTGGA	80	55.77			340

42	ACTTCTCCTCACTGGAC	81				240

43	CTTCTCCTCACTGGACA	82	55.75			240

44	TTCTCCTCACTGGACAG	83				120

45	TCTCCTCACTGGACAGA	84				100

46	CTCCTCACTGGACAGAT	85				110

47	TCCTCACTGGACAGATG	86				80

48	CCTCACTGGACAGATGC	87		0.00		240

49	CTCACTGGACAGATGCA	88		0.20		90

50	TCACTGGACAGATGCAC	89		0.20		30

51	CACTGGACAGATGCACC	90		0.50		100

52	ACTGGACAGATGCACCA	91				80

53	CTGGACAGATGCACCAT	92				90

54	TGGACAGATGCACCATT	93				80

55	GGACAGATGCACCATTC	94		0.30		180

56	GACAGATGCACCATTCT	95		−0.10		220

57	ACAGATGCACCATTCTG	96				120

58	CAGATGCACCATTCTGT	97				120

59	AGATGCACCATTCTGTC	98		−0.10		250

60	GATGCACCATTCTGTCT	99		0.30		520

61	ATGCACCATTCTGTCTG	100		0.40		980

62	TGCACCATTCTGTCTGT	101	56.05	0.20	2	780

63	GCACCATTCTGTCTGTT	102	56.52	0.20	2	810

64	CACCATTCTGTCTGTTT	103		0.20		220

65	ACCATTCTGTCTGTTTT	104		0.20		120

66	CCATTCTGTCTGTTTTG	105		0.20		120

67	CATTCTGTCTGTTTTGG	106		0.60		160

68	ATTCTGTCTGTTTTGGG	107		1.70		310

69	TTCTGTCTGTTTTGGGG	108		1.70		250

70	TCTGTCTGTTTTGGGGG	109		1.70	2	80

71	CTGTCTGTTTTGGGGGA	110	55.91	1.40	2	30

72	TGTCTGTTTTGGGGGAT	111		0.90		50

73	GTCTGTTTTGGGGGATT	112		0.90		10

74	TCTGTTTTGGGGGATTG	113		1.10		10

75	CTGTTTTGGGGGATTGC	114		2.20		10

76	TGTTTTGGGGGATTGCA	115		1.20		10

77	GTTTTGGGGGATTGCAA	116		0.00		5

78	TTTTGGGGGATTGCAAG	117		−0.20		5

79	TTTGGGGGATTGCAAGT	118		−0.20		5

80	TTGGGGGATTGCAAGTA	119		0.00		5

81	TGGGGGATTGCAAGTAA	120		1.20		5

82	GGGGGATTGCAAGTAAA	121		1.40		5

83	GGGGATTGCAAGTAAAC	122		1.40		5

84	GGGATTGCAAGTAAACA	123		1.30		5

85	GGATTGGAAGTAAACAC	124		0.90		5

86	GATTGCAAGTAAACACA	125		0.50		5

87	ATTGCAAGTAAACACAG	126		0.50		5

88	TTGCAAGTAAACACAGT	127		0.50		5

89	TGCAAGTAAAGACAGTT	128		0.30		5

90	GCAAGTAAACACAGTTG	129		0.10		10

91	GAAGTAAACACAGTTGT	130		−0.30		5

92	AAGTAAACACAGTTGTG	131				5

93	AGTAAACACAGTTGTGT	132				5

94	GTAAACACAGTTGTGTC	133				5

95	TAAACACAGTTGTGTCA	134				5

96	AAACACAGTTGTGTCAA	135				5

97	AACACAGTTGTGTCAAA	136				5

98	ACACAGTTGTGTCAAAA	137				10

99	CACAGTTGTGTCAAAAG	138				15

100	ACAGTTGTGTCAAAAGC	139				30

101	CAGTTGTGTCAAAAGCA	140		0.20		25

102	AGTTGTGTCAAAAGCAA	141		−0.10		25

103	GTTGTGTCAAAAGCAAG	142		−0.30		20

104	TTGTGTCAAAAGCAAGT	143		−0.10		120

105	TGTGTCAAAAGCAAGTG	144		0.50		20

In FIG. 4, the hybridization intensity observed experimentally is plotted as a function of oligonucleotide starting position in the target-complementary sequence that was input into p5. The identified contigs are plotted as horizontal bars, with the contig rank (by length) shown in parentheses next to each bar. It is clear from Table 3 and FIG. 4 that the prediction algorithm identified contigs that overlap all of the “top 20%” hybridization intensity peaks observed. Iterative experimental improvement of these predictions would converge on each of the observed intensity maxima in 3-4 iterations.
Prediction worksheets for HIV PRT, G3PDH and p53 were prepared in a manner similar to that for rabbit P-globin as shown in Table 3, except that the probes were longer as indicated above and that approximately 1,000 probes were analyzed for each of these genes. The results of these analyses are shown in FIG. 5 (HIV PRT), FIG. 6 (G3PDH) and FIG. 7 (p53). In FIG. 5, data are plotted for all possible 20-mer oligonucleotide probes. In FIGS. 6 and 7, data were available for only every 10^th25-mer probe, and the actual data points are plotted as open diamonds.
It is clear from FIGS. 5-7 that the hybridization efficiency prediction algorithm of the present invention performed well in the task of identifying regions with observed high hybridization intensity. In each case, the 4 longest contigs point to good-to-excellent regions for experimental investigation. It should be noted that the contigs usually bracket observed intensity peaks; experimental iterative refinement would therefore be expected to converge in 2-3 iterations. By this is meant that certain oligonucleotides from the identified contigs are prepared and subjected to evaluation in actual hybridization experiments. Based on the results of such experiments, the observed signal is evaluated to determine whether the oligonucleotides are hybridizing to the left of, the right of, or on the center of a peak with respect to the graphed data. The next iteration is carried out to experimentally evaluate the hybridization efficiency of probes that are inferred to lie closer to the peak of hybridization efficiency, based on the data from the previous iteration. Iteration is continued until the signal level is deemed acceptable by the user, or the local hybridization efficiency maximum is reached (i.e. the best probe in the cluster identified by the method of the current invention has been experimentally identified). A detailed illustration of this process is shown in Example 3.
It should be noted that clusters of predictions that overlap the maxima of observed peaks of hybridization efficiency will often yield user-acceptable probes on the first iteration. Thus, the method of the present invention is much more efficient than current methods in which every potential probe is synthesized. For instance, in the HIV PRT example shown in FIG. 5, at least 3 good probes would be identified after synthesis of ˜10 test probes (i.e. statistical sampling of the 3 longest contigs). This is much more efficient than the ˜1,000 probes represented by the data in FIG. 5.

Example 2

Synopsis: Data from a labeled RNA target hybridization to an Affymetrix GeneChip™ HIV PRT-sense probe array (GeneChip™ HIV PRT 440s, Affymetrix Corporation, Santa Clara, Calif.) were compared to the predictions of the window-averaged composite dimensionless score version of the method of the present invention.
Materials and Methods: Data were obtained as described for the Affymetrix GeneChip™ HIV PRT-sense probe array (GeneChip™ HIV PRT 440s, Affymetrix Corporation, Santa Clara, Calif.) in Example 1. The DNA sequence (SEQ ID NO: 37) complementary to the fluorescein-labeled RNA target was divided into overlapping 20-mer oligonucleotide sequences spaced one nucleotide apart, using the prototype application p5; p5 was also used to calculate the predicted values of the RNA/DNA heteroduplex melting temperature (T_m) and the free energy of the most stable predicted probe intramolecular structure, ΔG_MFOLD, as described in Example 1. The probe sequences and parameter values were then transferred to a Microsoft Excel spreadsheet, which was used to complete the predictions of efficient and inefficient probes. The weight was obtained by optimizing the performance of the algorithm with the data of Milner et al., supra, as the training data using the Microsoft® Excel® spreadsheet software. The composite score was calculated using a weight of 0.62 for the dimensionless T_mscore and a weight of 0.38 for the ΔG_MFOLDdimensionless score. The windowed-averaging was performed using a window width of 7 and Microsoft® Excel® spreadsheet software. Finally, the oligonucleotide sequences having the top 10% of the window-averaged composite dimensionless scores were predicted to be efficient probes, while the oligonucleotide sequences having the bottom 10% of the window-averaged composite dimensionless scores were predicted to be inefficient probes.

Results: The calculated parameters and scores are shown in Table 4; the algorithm predictions are also shown diagrammatically in FIG. 8. In Table 4, window-averaged composite score values that were in the top 10% of the distribution of values are shown in bold type, values that were in the bottom 10% are shown in italics, and all other values are shown with a line through them. It is clear from both Table 4 and FIG. 8 that the window-averaged composite dimensionless score embodiment of the current invention correctly predicted both efficient and inefficient hybridization probes for HIV PRT sense-strand RNA. As in Example 1, statistical sampling of contiguous stretches of predicted “good” probes would lead to convergence of the design process to the best probes in each region in 2-4 design iterations.

TABLE 4


								Window-
				ΔG_MFOLD				Averaged	HIV PRT
p5 Probe		SEQ ID	RNA/DNA	(kcal/mole	T_m	ΔG_MFOLD	Composite	Composite	GeneChip ™
Position	DNA Probe Sequence	NO:	T_m(° C.)	@ 35° C.)	Score	Score	Score	Score	Data

1	GTACTGTCCATTTATCAGGA	145	64.16	−0.10	0.557	−0.199	0.269		1152.2

2	TACTGTCCATTTATCAGGAT	146	60.91	−0.40	0.080	−0.460	−0.125		1040.7

3	ACTGTCCATTTATCAGGATG	147	61.41	−0.90	0.152	−0.895	−0.246		291.9

4	CTGTCCATTTATCAGGATGG	148	63.46	−0.90	0.453	−0.895	−0.059		221.8

5	TGTCCATTTATCAGGATGGA	149	62.82	−0.90	0.360	−0.895	−0.117		148.3

6	GTCCATTTATCAGGATGGAG	150	63.15	−1.90	0.408	−1.764	−0.418		84.6

7	TCCATTTATCAGGATGGAGT	151	63.15	−2.10	0.408	−1.938	−0.484		128.7

8	CCATTTATCAGGATGGAGTT	152	62.03	−1.90	0.245	−1.764	−0.519		94.6

9	CATTTATCAGGATGGAGTTC	153	59.53	−0.60	−0.122	−0.634	−0.317		157.5

10	ATTTATCAGGATGGAGTTCA	154	59.53	0.80	−0.122	0.583	0.146		316.9

11	TTTATCAGGATGGAGTTCAT	155	59.53	0.40	−0.122	0.236	0.014		360.2

12	TTATCAGGATGGAGTTCATA	156	58.58	0.40	−0.262	0.236	−0.073		403.8

13	TATCAGGATGGAGTTCATAA	157	56.21	0.20	−0.609	0.062	−0.354		382.5

14	ATCAGGATGGAGTTCATAAC	158	57.34	0.20	−0.444	0.062	−0.252		324.4

15	TCAGGATGGAGTTCATAACC	159	61.25	0.20	0.129	0.062	0.104		320.5

16	CAGGATGGAGTTCATAACCC	160	63.57	0.20	0.470	0.062	0.315		238.9

17	AGGATGGAGTTCATAACCCA	161	63.57	−0.10	0.470	−0.199	0.216		202.3

18	GGATGGAGTTCATAACCCAT	162	63.34	−1.30	0.436	−1.243	−0.202		113.6

19	GATGGAGTTCATAACCCATC	163	62.24	−2.00	0.275	−1.851	−0.533		97.7

20	ATGGAGTTCATAACCCATCC	164	64.62	−3.30	0.624	−2.982	−0.746		143.3

21	TGGAGTTCATAACCCATCCC	165	68.18	−2.00	1.146	−1.851	0.007		484.6

22	GGAGTTCATAACCCATCCCA	166	69.39	−1.60	1.324	−1.504	0.249		857.6

23	GAGTTCATAACCCATCCCAA	167	64.93	−0.20	0.670	−0.286	0.307		991.4

24	AGTTCATAACCCATCCCAAA	168	61.82	0.20	0.213	0.062	0.155		907.0

25	GTTCATAACCCATCCCAAAG	169	61.82	0.20	0.213	0.062	0.155		887.9

26	TTCATAACCCATCCCAAAGG	170	61.36	0.60	0.145	0.410	0.246		1015.3

27	TCATAACCCATCCCAAAGGA	171	62.21	−0.10	0.270	−0.199	0.092		279.7

28	CATAACCCATCCCAAAGGAA	172	59.26	−0.30	−0.163	−0.373	−0.243		210.7

29	ATAACCCATCCCAAAGGAAT	173	58.19	−0.30	−0.320	−0.373	−0.340		179.9

30	TAACCCATCCCAAAGGAATG	174	58.13	−0.30	−0.328	−0.373	−0.345		91.8

31	AACCCATCCCAAAGGAATGG	175	60.78	−1.30	0.061	−1.243	−0.435		44.6

32	ACCCATCCCAAAGGAATGGA	176	63.69	−2.00	0.487	−1.851	−0.401		42.9

33	CCCATCCCAAAGGAATGGAG	177	63.40	−2.20	0.445	−2.025	−0.494		45.0

34	CCATCCCAAAGGAATGGAGG	178	62.34	−2.30	0.290	−2.112	−0.623		45.3

35	CATCCCAAAGGAATGGAGGT	179	61.72	−2.60	0.199	−2.373	−0.778		47.9

36	ATCCCAAAGGAATGGAGGTT	180	60.90	−2.20	0.079	−2.025	−0.721		49.2

37	TCCCAAAGGAATGGAGGTTC	181	62.24	−2.20	0.274	−2.025	−0.600		74.2

38	CCCAAAGGAATGGAGGTTCT	182	62.71	−2.00	0.344	−1.851	−0.490		125.5

39	CCAAAGGAATGGAGGTTCTT	183	59.47	−0.70	−0.132	−0.721	−0.356		183.3

40	CAAAGGAATGGAGGTTCTTT	184	56.10	−0.30	−0.627	−0.373	−0.530		261.4

41	AAAGGAATGGAGGTTCTTTC	185	56.11	−0.30	−0.625	−0.373	−0.529		518.3

42	AAGGAATGGAGGTTCTTTCT	186	60.05	−0.30	−0.046	−0.373	−0.170		716.5

43	AGGAATGGAGGTTCTTTCTG	187	62.09	−0.30	0.253	−0.373	0.015		1056.0

44	GGAATGGAGGTTCTTTCTGA	188	63.23	−0.30	0.420	−0.373	0.119		1084.3

45	GAATGGAGGTTCTTTCTGAT	189	60.56	0.10	0.028	−0.025	0.008		1241.1

46	AATGGAGGTTCTTTCTGATG	190	59.12	0.30	−0.183	0.149	−0.057		1278.8

47	ATGGAGGTTCTTTCTGATGT	191	64.58	0.30	0.618	0.149	0.440		1616.0

48	TGGAGGTTCTTTCTGATGTT	192	64.98	0.30	0.677	0.149	0.476		1677.5

49	GGAGGTTCTTTCTGATGTTT	193	65.49	0.30	0.751	0.149	0.522		1963.1

50	GAGGTTCTTTCTGATGTTTT	194	63.04	0.30	0.392	0.149	0.300		2126.1

51	AGGTTCTTTCTGATGTTTTT	195	61.97	0.30	0.235	0.149	0.202		2143.3

52	GGTTCTTTCTGATGTTTTTT	196	62.11	0.30	0.256	0.149	0.215		3540.6

53	GTTCTTTCTGATGTTTTTTG	197	59.21	0.30	−0.170	0.149	−0.049		1728.7

54	TTCTTTCTGATGTTTTTTGT	198	59.21	0.30	−0.170	0.149	−0.049		1364.3

55	TCTTTCTGATGTTTTTTGTC	199	60.35	0.50	−0.002	0.323	0.121		1788.4

56	CTTTCTGATGTTTTTTGTCT	200	60.96	1.20	0.086	0.931	0.407		2670.9

57	TTTCTGATGTTTTTTGTCTG	201	58.76	1.20	−0.235	0.931	0.208		3336.2

58	TTCTGATGTTTTTTGTCTGG	202	61.17	1.20	0.118	0.931	0.427		6683.6

59	TCTGATGTTTTTTGTCTGGT	203	64.20	1.20	0.562	0.931	0.702		10227.0

60	CTGATGTTTTTTGTCTGGTG	204	62.51	1.20	0.315	0.931	0.549		10965.0

61	TGATGTTTTTTGTCTGGTGT	205	63.80	1.20	0.504	0.931	0.666		11133.0

62	GATGTTTTTTGTCTGGTGTG	206	63.80	1.60	0.504	1.279	0.798	0.894	11503.0

63	ATGTTTTTTGTCTGGTGTGG	207	65.18	1.90	0.705	1.540	1.023	0.894	9492.8

64	TGTTTTTTGTCTGGTGTGGT	208	68.78	1.70	1.234	1.366	1.284	0.914	10704.0

65	GTTTTTTGTCTGGTGTGGTA	209	68.28	1.70	1.161	1.366	1.239	0.933	10741.0

66	TTTTTTGTCTGGTGTGGTAA	210	62.37	1.70	0.294	1.366	0.701	0.950	9187.5

67	TTTTTGTCTGGTGTGGTAAG	211	62.23	1.70	0.273	1.366	0.689	0.941	7871.0

68	TTTTGTCTGGTGTGGTAAGT	212	65.28	1.20	0.721	0.931	0.801	0.921	7209.1

69	TTTGTCTGGTGTGGTAAGTC	213	66.56	1.20	0.908	0.931	0.917	0.959	8052.3

70	TTGTCTGGTGTGGTAAGTCC	214	70.25	0.30	1.449	0.149	0.955	1.022	7230.6

71	TGTCTGGTGTGGTAAGTCCC	215	73.77	−0.10	1.966	−0.199	1.143	0.998	6809.5

72	GTCTGGTGTGGTAAGTCCCC	216	77.74	−0.10	2.549	−0.199	1.504	0.913	7442.8

73	TCTGGTGTGGTAAGTCCCCA	217	75.28	−0.50	2.187	−0.547	1.148		2627.7

74	CTGGTGTGGTAAGTCCCCAC	218	74.18	−2.10	2.026	−1.938	0.519		1315.0

75	TGGTGTGGTAAGTCCCCACC	219	75.80	−3.50	2.263	−3.156	0.204		4182.3

76	GGTGTGGTAAGTCCCCACCT	220	77.89	−3.80	2.571	−3.417	0.296		474.7

77	GTGTGGTAAGTCCCCACCTC	221	77.05	−2.50	2.448	−2.286	0.649		682.4

78	TGTGGTAAGTCCCCACCTCA	222	74.71	−2.50	2.105	−2.286	0.436		679.1

79	GTGGTAAGTCCCCACCTCAA	223	72.54	−2.10	1.785	−1.938	0.370		924.0

80	TGGTAAGTCCCCACCTCAAC	224	69.94	−0.90	1.404	−0.895	0.531		835.5

81	GGTAAGTCCCCACCTCAACA	225	71.14	−0.50	1.580	−0.547	0.772		1213.6

82	GTAAGTCCCCACCTCAACAG	226	68.97	0.90	1.262	0.670	1.037		1106.1

83	TAAGTCCCCACCTCAACAGA	227	67.18	0.90	0.999	0.670	0.874	0.872	1009.0

84	AAGTCCCCACCTCAACAGAT	228	67.68	0.50	1.073	0.323	0.788	0.908	1656.2

85	AGTCCCCACCTCAACAGATG	229	69.68	0.50	1.366	0.323	0.970		2178.3

86	GTCCCCACCTCAACAGATGT	230	72.56	0.20	1.789	0.062	1.132		2567.0

87	TCCCCACCTCAACAGATGTT	231	69.77	−0.10	1.379	−0.199	0.779		3000.5

88	CCCCACCTCAACAGATGTTG	232	68.19	−1.30	1.148	−1.243	0.240		2025.4

89	CCCACCTCAACAGATGTTGT	233	67.78	−2.00	1.087	−1.851	−0.030		429.2

90	CCACCTCAACAGATGTTGTC	234	65.65	−2.00	0.775	−1.851	−0.223		157.9

91	CACCTCAACAGATGTTGTCT	235	63.85	−2.00	0.511	−1.851	−0.387		135.3

92	ACCTCAACAGATGTTGTCTC	236	64.11	−2.00	0.549	−1.851	−0.363		330.8

93	CCTCAACAGATGTTGTCTCA	237	64.77	−2.00	0.646	−1.851	−0.303		900.0

94	CTCAACAGATGTTGTCTCAG	238	61.08	−2.00	0.104	−1.851	−0.639		1177.0

95	TCAACAGATGTTGTCTCAGC	239	63.40	−2.00	0.444	−1.851	−0.428		795.1

96	CAACAGATGTTGTCTCAGCT	240	63.91	−1.60	0.520	−1.504	−0.249		889.2

97	AACAGATGTTGTCTCAGCTC	241	64.19	−0.10	0.560	−0.199	0.272		1703.6

98	ACAGATGTTGTCTCAGCTCC	242	70.61	0.00	1.503	−0.112	0.889		3115.2

99	CAGATGTTGTCTCAGCTCCT	243	72.08	0.00	1.719	−0.112	1.023	0.847	4445.0

100	AGATGTTGTCTCAGCTCCTC	244	72.66	0.20	1.803	0.062	1.141	1.070	6762.8

101	GATGTTGTCTCAGCTCCTCT	245	74.49	0.90	2.071	0.670	1.539	1.227	8845.0

102	ATGTTGTCTCAGCTCCTCTA	246	72.38	0.80	1.763	0.583	1.314	1.253	9010.6

103	TGTTGTCTCAGCTCCTCTAT	247	72.38	0.80	1.763	0.583	1.314	1.260	19941.0

104	GTTGTCTCAGCTCCTCTATT	248	72.97	0.80	1.849	0.583	1.368	1.257	12577.0

105	TTGTCTCAGCTCCTCTATTT	249	69.70	0.80	1.369	0.583	1.071	1.149	7503.3

106	TGTCTCAGCTCCTCTATTTT	250	69.70	0.80	1.369	0.583	1.071	1.098	7033.8

107	GTCTCAGCTCCTCTATTTTT	251	70.26	0.80	1.451	0.583	1.121	1.024	8276.7

108	TCTCAGCTCCTCTATTTTTG	252	66.57	0.80	0.910	0.583	0.786	0.942	2899.0

109	CTCAGCTCCTCTATTTTTGT	253	68.39	0.80	1.177	0.583	0.952	0.923	2935.0

110	TCAGCTCCTCTATTTTTGTT	254	66.69	0.80	0.927	0.583	0.796	0.930	1512.8

111	CAGCTCCTCTATTTTTGTTC	255	66.69	0.80	0.927	0.583	0.796	0.872	1708.8

112	AGCTCCTCTATTTTTGTTCT	256	67.52	1.00	1.050	0.757	0.939	0.833	1977.3

113	GCTCCTCTATTTTTGTTCTA	257	66.63	1.80	0.919	1.453	1.122		2114.8

114	CTCCTCTATTTTTGTTCTAT	258	62.13	1.80	0.259	1.453	0.713		1527.3

115	TCCTCTATTTTTGTTCTATG	259	59.97	1.80	−0.058	1.453	0.516		1536.8

116	CCTCTATTTTTGTTCTATGC	260	62.84	1.80	0.363	1.453	0.777		1824.5

117	CTCTATTTTTGTTCTATGCT	261	60.87	1.50	0.074	1.192	0.499		1169.2

118	TCTATTTTTGTTCTATGCTG	262	58.71	1.50	−0.244	1.192	0.302		683.7

119	CTATTTTTGTTCTATGCTGC	263	61.60	1.50	0.181	1.192	0.565		1306.8

120	TATTTTTGTTCTATGCTGCC	264	63.53	1.50	0.464	1.192	0.741		2523.6

121	ATTTTTGTTCTATGCTGCCC	265	67.96	1.50	1.113	1.192	1.143	0.931	6682.0

122	TTTTTGTTCTATGCTGCCCT	266	69.96	1.50	1.407	1.192	1.325	1.060	9417.4

123	TTTTGTTCTATGCTGCCCTA	267	69.01	1.50	1.267	1.192	1.239	1.151	10339.0

124	TTTGTTCTATGCTGCCCTAT	268	68.62	1.50	1.210	1.192	1.203	1.254	10750.0

125	TTGTTCTATGCTGCCCTATT	269	68.62	1.50	1.210	1.192	1.203	1.282	11180.0

126	TGTTCTATGCTGCCCTATTT	270	68.62	1.50	1.210	1.192	1.203	1.271	11060.0

127	GTTCTATGCTGCCCTATTTC	271	70.37	1.80	1.468	1.453	1.462	1.221	16074.0

128	TTCTATGCTGCCCTATTTCT	272	69.00	1.80	1.266	1.453	1.337	1.144	9183.8

129	TCTATGCTGCCCTATTTCTA	273	68.05	1.80	1.127	1.453	1.251	1.082	8617.8

130	CTATGCTGCCCTATTTCTAA	274	64.38	1.70	0.589	1.366	0.884	1.040	7286.8

131	TATGCTGCCCTATTTCTAAG	275	62.71	1.50	0.344	1.192	0.666	0.978	3642.4

132	ATGCTGCCCTATTTCTAAGT	276	66.39	0.80	0.883	0.583	0.769	0.883	3799.7

133	TGCTGCCCTATTTCTAAGTC	277	67.95	0.80	1.112	0.583	0.911		3408.3

134	GCTGCCCTATTTCTAAGTCA	278	69.25	0.80	1.303	0.583	1.030		4017.4

135	CTGCCCTATTTCTAAGTCAG	279	65.26	0.80	0.718	0.583	0.667		2197.2

136	TGCCCTATTTCTAAGTCAGA	280	64.63	−0.10	0.626	−0.199	0.312		1125.0

137	GCCCTATTTCTAAGTCAGAT	281	64.73	−0.60	0.639	−0.634	0.156		1306.3

138	CCCTATTTCTAAGTCAGATC	282	61.98	−0.60	0.236	−0.634	−0.094		1019.5

139	CCTATTTCTAAGTCAGATCC	283	61.98	−0.60	0.236	−0.634	−0.094		1852.3

140	CTATTTCTAAGTCAGATCCT	284	60.05	−0.60	−0.046	−0.634	−0.270		3159.3

141	TATTTCTAAGTCAGATCCTA	285	57.43	−0.60	−0.430	−0.634	−0.508		2604.8

142	ATTTCTAAGTCAGATCCTAC	286	58.59	−0.60	−0.261	−0.634	−0.402		3986.1

143	TTTCTAAGTCAGATCCTACA	287	59.91	−0.60	−0.068	−0.634	−0.283		4500.7

144	TTCTAAGTCAGATCCTACAT	288	59.55	−0.60	−0.120	−0.634	−0.315		4754.5

145	TCTAAGTCAGATCCTACATA	289	58.62	−0.40	−0.257	−0.460	−0.334		3802.1

146	CTAAGTCAGATCCTACATAC	290	57.80	1.20	−0.377	0.931	0.120		5069.4

147	TAAGTCAGATCCTACATACA	291	57.13	1.30	−0.476	1.018	0.092		3965.2

148	AAGTCAGATCCTACATACAA	292	55.78	1.30	−0.673	1.018	−0.030		3862.3

149	AGTCAGATCCTACATACAAA	293	55.78	1.30	−0.673	1.018	−0.030		2868.9

150	GTCAGATCCTACATACAAAT	294	55.62	1.70	−0.697	1.366	0.087		3542.9

151	TCAGATCCTACATACAAATC	295	54.02	1.50	−0.932	1.192	−0.125		2477.1

152	CAGATCCTACATACAAATCA	296	54.07	1.10	−0.924	0.844	−0.252		2522.4

153	AGATCCTACATACAAATCAT	297	52.83	1.10	−1.106	0.844	−0.365		2554.6

154	GATCCTACATACAAATCATC	298	53.87	1.50	−0.953	1.192	−0.138		3580.0

155	ATCCTACATACAAATCATCC	299	56.33	1.80	−0.591	1.453	0.185		5937.7

156	TCCTACATACAAATCATCCA	300	57.54	1.80	−0.415	1.453	0.295		4606.7

157	CCTACATACAAATCATCCAT	301	56.32	1.80	−0.594	1.453	0.184		4877.2

158	CTACATACAAATCATCCATG	302	52.68	1.10	−1.128	0.844	−0.379		2608.6

159	TACATACAAATCATCCATGT	303	53.56	0.30	−0.999	0.149	−0.563		1491.7

160	ACATACAAATCATCCATGTA	304	53.56	−0.10	−0.999	−0.199	−0.695		1364.3

161	CATACAAATCATCCATGTAT	305	53.07	−0.80	−1.071	−0.808	−0.971	−0.751	1089.8

162	ATACAAATCATCCATGTATT	306	52.11	−1.10	−1.211	−1.069	−1.157	−0.818	1008.6

163	TACAAATCATCCATGTATTG	307	52.08	−0.40	−1.215	−0.460	−0.928	−0.891	624.8

164	ACAAATCATCCATGTATTGA	308	53.86	0.20	−0.955	0.062	−0.568	−0.921	535.8

165	CAAATCATCCATGTATTGAT	309	53.36	−0.50	−1.027	−0.547	−0.845	−0.860	3019.6

166	AAATCATCCATGTATTGATA	310	51.57	−0.70	−1.291	−0.721	−1.074	−0.753	214.0

167	AATCATCCATGTATTGATAG	311	53.47	−0.70	−1.012	−0.721	−0.901		212.7

168	ATCATCCATGTATTGATAGA	312	56.66	−0.50	−0.543	−0.547	−0.545		165.2

169	TCATCCATGTATTGATAGAT	313	56.66	−0.10	−0.543	−0.199	−0.412		166.0

170	CATCCATGTATTGATAGATA	314	54.80	0.30	−0.817	0.149	−0.450		151.0

171	ATCCATGTATTGATAGATAA	315	51.69	0.30	−1.273	0.149	−0.733		101.8

172	TCCATGTATTGATAGATAAC	316	52.19	0.30	−1.199	0.149	−0.687		84.0

173	CCATGTATTGATAGATAACT	317	52.89	0.30	−1.097	0.149	−0.623	−0.850	130.3

174	CATGTATTGATAGATAACTA	318	48.47	0.70	−1.746	0.496	−0.894	−0.937	67.8

175	ATGTATTGATAGATAACTAT	319	47.12	0.00	−1.944	−0.112	−1.248	−1.006	65.7

176	TGTATTGATAGATAACTATG	320	47.11	−0.20	−1.945	−0.286	−1.315	−1.048	90.0

177	GTATTGATAGATAACTATGT	321	49.90	−0.20	−1.536	−0.286	−1.061	−1.099	125.9

178	TATTGATAGATAACTATGTC	322	48.24	−0.20	−1.779	−0.286	−1.212	−1.083	132.6

179	ATTGATAGATAACTATGTCT	323	50.78	−0.20	−1.407	−0.286	−0.981	−0.998	167.4

180	TTGATAGATAACTATGTCTG	324	50.75	−0.20	−1.411	−0.286	−0.984	−0.916	219.0

181	TGATAGATAACTATGTCTGG	325	53.01	−0.20	−1.080	−0.286	−0.778	−0.866	722.6

182	GATAGATAACTATGTCTGGA	326	54.36	−0.20	−0.881	−0.286	−0.655	−0.774	825.1

183	ATAGATAACTATGTCTGGAT	327	53.04	−0.10	−1.074	−0.199	−0.742		844.4

184	TAGATAACTATGTCTGGATT	328	53.37	−0.10	−1.027	−0.199	−0.712		912.6

185	AGATAACTATGTCTGGATTT	329	54.27	0.10	−0.895	−0.025	−0.565		1301.8

186	GATAACTATGTCTGGATTTT	330	54.43	0.80	−0.870	0.583	−0.318		1367.4

187	ATAACTATGTCTGGATTTTG	331	53.08	1.50	−1.070	1.192	−0.210		1284.2

188	TAACTATGTCTGGATTTTGT	332	56.05	1.50	−0.634	1.192	0.060		1162.5

189	AACTATGTCTGGATTTTGTT	333	56.97	1.50	−0.499	1.192	0.144		1396.7

190	ACTATGTCTGGATTTTGTTT	334	59.38	1.50	−0.145	1.192	0.363		1348.3

191	CTATGTCTGGATTTTGTTTT	335	59.16	1.50	−0.177	1.192	0.343		1092.8

192	TATGTCTGGATTTTGTTTTT	336	57.45	1.50	−0.428	1.192	0.188		912.6

193	ATGTCTGGATTTTGTTTTTT	337	58.41	1.70	−0.287	1.366	0.341		994.3

194	TGTCTGGATTTTGTTTTTTA	338	57.81	2.00	−0.375	1.627	0.386		840.7

195	GTCTGGATTTTGTTTTTTAA	339	55.82	1.00	−0.667	0.757	−0.126		941.9

196	TCTGGATTTTGTTTTTTAAA	340	50.98	0.80	−1.377	0.583	−0.632		84.9

197	CTGGATTTTGTTTTTTAAAA	341	48.16	0.30	−1.790	0.149	−1.054		78.6

198	TGGATTTTGTTTTTTAAAAG	342	46.41	0.10	−2.048	−0.025	−1.279	−0.851	93.2

199	GGATTTTGTTTTTTAAAAGG	343	48.87	0.10	−1.686	−0.025	−1.055	−0.933	56.0

200	GATTTTGTTTTTTAAAAGGC	344	50.22	0.10	−1.488	−0.025	−0.932	−0.912	49.9

201	ATTTTGTTTTTTAAAAGGCT	345	50.84	0.10	−1.397	−0.025	−0.876	−0.843	55.0

202	TTTTGTTTTTTAAAAGGCTC	346	52.03	0.30	−1.223	0.149	−0.702	−0.768	64.6

203	TTTGTTTTTTAAAAGGCTCT	347	53.64	0.50	−0.987	0.323	−0.489		162.8

204	TTGTTTTTTAAAAGGCTCTA	348	52.76	0.50	−1.115	0.323	−0.569		265.8

205	TGTTTTTTAAAAGGCTCTAA	349	50.71	0.50	−1.417	0.323	−0.756		288.5

206	GTTTTTTAAAAGGCTCTAAG	350	50.86	0.50	−1.395	0.323	−0.742		548.4

207	TTTTTTAAAAGGCTCTAAGA	351	49.40	0.70	−1.609	0.496	−0.809		524.7

208	TTTTTAAAAGGCTCTAAGAT	352	49.11	1.20	−1.651	0.931	−0.670	−0.746	937.9

209	TTTTAAAAGGCTCTAAGATT	353	49.11	1.20	−1.651	0.931	−0.670	−0.790	1440.3

210	TTTAAAAGGCTCTAAGATTT	354	49.11	1.20	−1.651	0.931	−0.670	−0.820	1633.3

211	TTAAAAGGCTCTAAGATTTT	355	49.11	0.50	−1.651	0.323	−0.901	−0.735	1987.4

212	TAAAAGGCTCTAAGATTTTT	356	49.11	0.00	−1.651	−0.112	−1.067		1792.3

213	AAAAGGCTCTAAGATTTTTG	357	49.63	0.20	−1.575	0.062	−0.953		2218.9

214	AAAGGCTCTAAGATTTTTGT	358	54.13	1.20	−0.914	0.931	−0.213		2371.4

215	AAGGCTCTAAGATTTTTGTC	359	57.38	1.20	−0.439	0.931	0.082		3308.9

216	AGGCTCTAAGATTTTTGTCA	360	60.78	0.80	0.061	0.583	0.260		4070.5

217	GGCTCTAAGATTTTTGTCAT	361	60.56	0.80	0.028	0.583	0.239		5394.5

218	GCTCTAAGATTTTTGTCATG	362	57.81	0.80	−0.376	0.583	−0.011		2025.5

219	CTCTAAGATTTTTGTCATGC	363	57.81	0.80	−0.376	0.583	−0.011		1741.9

220	TCTAAGATTTTTGTCATGCT	364	57.81	0.80	−0.376	0.583	−0.011		1707.6

221	CTAAGATTTTTGTCATGCTA	365	55.87	0.80	−0.660	0.583	−0.187		1783.0

222	TAAGATTTTTGTCATGCTAC	366	54.43	0.80	−0.872	0.583	−0.319		3131.4

223	AAGATTTTTGTCATGCTACT	367	56.99	0.60	−0.495	0.410	−0.151		4892.5

224	AGATTTTTGTCATGCTACTT	368	59.39	0.60	−0.144	0.410	0.067		5856.4

225	GATTTTTGTCATGCTACTTT	369	59.54	0.60	−0.122	0.410	0.080		6439.0

226	ATTTTTGTCATGCTACTTTG	370	58.09	0.60	−0.334	0.410	−0.051		5820.3

227	TTTTTGTCATGCTACTTTGG	371	60.78	0.60	0.060	0.410	0.193		5189.6

228	TTTTGTCATGCTACTTTGGA	372	61.79	0.60	0.209	0.410	0.285		4721.7

229	TTTGTCATGCTACTTTGGAA	373	59.35	0.60	−0.149	0.410	0.063		4221.0

230	TTGTCATGCTACTTTGGAAT	374	59.00	0.60	−0.200	0.410	0.032		4279.0

231	TGTCATGCTACTTTGGAATA	375	58.10	0.60	−0.333	0.410	−0.051		4102.0

232	GTCATGCTACTTTGGAATAT	376	58.16	0.90	−0.324	0.670	0.054		5069.8

233	TCATGCTACTTTGGAATATT	377	55.52	0.90	−0.711	0.670	−0.186		2407.9

234	CATGCTACTTTGGAATATTG	378	54.23	1.30	−0.900	1.018	−0.171		2443.0

235	ATGCTACTTTGGAATATTGC	379	56.90	1.40	−0.508	1.105	0.105		2324.3

236	TGCTACTTTGGAATATTGCT	380	58.82	0.90	−0.227	0.670	0.114		1894.1

237	GCTACTTTGGAATATTGCTG	381	58.82	1.30	−0.227	1.018	0.246		2363.8

238	CTACTTTGGAATATTGCTGG	382	57.35	1.70	−0.443	1.366	0.244		1363.0

239	TACTTTGGAATATTGCTGGT	383	58.39	1.70	−0.290	1.366	0.339		1217.5

240	ACTTTGGAATATTGCTGGTG	384	58.88	1.70	−0.217	1.366	0.384		1621.8

241	CTTTGGAATATTGCTGGTGA	385	59.64	1.70	−0.106	1.366	0.453		1438.2

242	TTTGGAATATTGCTGGTGAT	386	57.72	1.80	−0.388	1.453	0.311		1608.0

243	TTGGAATATTGCTGGTGATC	387	58.73	1.80	−0.241	1.453	0.403		2334.6

244	TGGAATATTGCTGGTGATCC	388	62.18	0.50	0.266	0.323	0.288		3776.7

245	GGAATATTGCTGGTGATCCT	389	64.19	−0.20	0.561	−0.286	0.239		5648.7

246	GAATATTGCTGGTGATCCTT	390	61.99	−0.20	0.238	−0.286	0.039		5358.8

247	AATATTGCTGGTGATCCTTT	391	61.03	−0.20	0.097	−0.286	−0.049		5517.2

248	ATATTGCTGGTGATCCTTTC	392	64.63	−0.20	0.625	−0.286	0.279		6246.4

249	TATTGCTGGTGATCCTTTCC	393	68.48	−0.20	1.190	−0.286	0.629		9975.1

250	ATTGCTGGTGATCCTTTCCA	394	70.22	−0.20	1.446	−0.286	0.788		11990.0

251	TTGCTGGTGATCCTTTCCAT	395	70.22	−0.60	1.446	−0.634	0.655		11543.0

252	TGCTGGTGATCCTTTCCATC	396	71.48	−0.60	1.631	−0.634	0.770	0.862	14125.0

253	GCTGGTGATCCTTTCCATCC	397	75.32	−0.60	2.193	−0.634	1.119	0.936	23489.0

254	CTGGTGATCCTTTCCATCCC	398	74.58	−0.60	2.085	−0.634	1.052	1.022	15975.0

255	TGGTGATCCTTTCCATCCCT	399	74.58	−0.70	2.085	−0.721	1.019	1.082	16053.0

256	GGTGATCCTTTCCATCCCTG	400	74.58	−0.30	2.085	−0.373	1.151	1.136	19205.0

257	GTGATCCTTTCCATCCCTGT	401	75.40	0.20	2.206	0.062	1.391	1.080	17872.0

258	TGATCCTTTCCATCCCTGTG	402	71.89	0.20	1.691	0.062	1.072	0.955	12871.0

259	GATCCTTTCCATCCCTGTGG	403	74.58	−0.30	2.085	−0.373	1.151		8792.7

260	ATCCTTTCCATCCCTGTGGA	404	74.58	−1.60	2.085	−1.504	0.721		5609.6

261	TCCTTTCCATCCCTGTGGAA	405	72.27	−2.60	1.746	−2.373	0.181		3018.0

262	CCTTTCCATCCCTGTGGAAG	406	71.00	−2.80	1.559	−2.547	−0.001		1802.6

263	CTTTCCATCCCTGTGGAAGC	407	71.60	−2.80	1.648	−2.547	0.054		1074.0

264	TTTCCATCCCTGTGGAAGCA	408	70.81	−2.80	1.532	−2.547	−0.018		1132.5

265	TTCCATCCCTGTGGAAGCAC	409	71.02	−2.60	1.562	−2.373	0.067		1454.5

266	TCCATCCCTGTGGAAGCACA	410	71.74	−1.70	1.669	−1.591	0.430		1676.8

267	CCATCCCTGTGGAAGCACAT	411	70.20	−2.20	1.443	−2.025	0.125		2268.9

268	CATCCCTGTGGAAGCACATT	412	67.07	−2.20	0.983	−2.025	−0.160		1682.6

269	ATCCCTGTGGAAGCACATTG	413	65.82	−2.20	0.801	−2.025	−0.273		1753.9

270	TCCCTGTGGAAGCACATTGT	414	68.98	−2.20	1.263	−2.025	0.014		1281.8

271	CCCTGTGGAAGCACATTGTA	415	66.92	−2.20	0.962	−2.025	−0.173		1227.8

272	CCTGTGGAAGCACATTGTAC	416	63.84	−2.20	0.509	−2.025	−0.454		700.3

273	CTGTGGAAGCACATTGTACT	417	62.01	−2.20	0.241	−2.025	−0.620		618.7

274	TGTGGAAGCACATTGTACTG	418	59.99	−2.00	−0.056	−1.851	−0.738		771.5

275	GTGGAAGCACATTGTACTGA	419	61.39	−0.50	0.149	−0.547	−0.115		1180.6

276	TGGAAGCACATTGTACTGAT	420	58.35	0.50	−0.296	0.323	−0.061		1160.5

277	GGAAGCACATTGTACTGATA	421	57.86	0.50	−0.368	0.323	−0.106		1314.7

278	GAAGCACATTGTACTGATAT	422	55.32	0.50	−0.740	0.323	−0.336		1102.5

279	AAGCACATTGTACTGATATC	423	55.30	0.50	−0.744	0.323	−0.339		1222.1

280	AGCACATTGTACTGATATCT	424	59.26	0.50	−0.162	0.323	0.022		1893.2

281	GCACATTGTACTGATATCTA	425	58.48	0.50	−0.277	0.323	−0.049		2097.7

282	CACATTGTACTGATATCTAA	426	52.51	0.50	−1.152	0.323	−0.592		1237.8

283	ACATTGTACTGATATCTAAT	427	51.20	0.50	−1.345	0.323	−0.711		959.5

284	CATTGTACTGATATCTAATC	428	51.89	0.10	−1.244	−0.025	−0.781		1149.1

285	ATTGTACTGATATCTAATCC	429	54.53	−0.30	−0.856	−0.373	−0.672		2351.3

286	TTGTACTGATATCTAATCCC	430	58.41	−0.30	−0.287	−0.373	−0.320		4191.6

287	TGTACTGATATCTAATCCCT	431	59.99	−0.30	−0.055	−0.373	−0.176		5565.8

288	GTACTGATATCTAATCCCTG	432	59.99	−0.30	−0.055	−0.373	−0.176		9980.2

289	TACTGATATCTAATCCCTGG	433	59.52	−0.30	−0.124	−0.373	−0.218		6318.9

290	ACTGATATCTAATCCCTGGT	434	63.07	−0.30	0.397	−0.373	0.104		7749.5

291	CTGATATCTAATCCCTGGTG	435	62.43	−0.30	0.303	−0.373	0.046		8165.3

292	TGATATCTAATCCCTGGTGT	436	63.60	−0.30	0.474	−0.373	0.152		9107.6

293	GATATCTAATCCCTGGTGTC	437	65.19	0.10	0.707	−0.025	0.429		13914.0

294	ATATCTAATCCCTGGTGTCT	438	65.82	1.50	0.800	1.192	0.949		15093.0

295	TATCTAATCCCTGGTGTCTC	439	67.41	1.50	1.033	1.192	1.093		18647.0

296	ATCTAATCCCTGGTGTCTCA	440	69.20	1.30	1.296	1.018	1.190	0.904	21810.0

297	TCTAATCCCTGGTGTCTCAT	441	69.20	0.80	1.296	0.583	1.025	0.996	20102.0

298	CTAATCCCTGGTGTCTCATT	442	67.98	0.80	1.117	0.583	0.914	1.052	20967.0

299	TAATCCCTGGTGTCTCATTG	443	65.90	0.80	0.811	0.583	0.725	1.092	18200.0

300	AATCCCTGGTGTCTCATTGT	444	69.78	0.80	1.380	0.583	1.077	1.088	19845.0

301	ATCCCTGGTGTCTCATTGTT	445	72.61	0.80	1.797	0.583	1.336	1.057	19231.0

302	TCCCTGGTGTCTCATTGTTT	446	73.04	0.80	1.860	0.583	1.375	0.981	17629.0

303	CCCTGGTGTCTCATTGTTTA	447	70.72	0.80	1.519	0.583	1.164	0.918	17009.0

304	CCTGGTGTCTCATTGTTTAT	448	66.82	0.80	0.946	0.583	0.808		11580.0

305	CTGGTGTCTCATTGTTTATA	449	62.17	0.80	0.264	0.583	0.386		8374.6

306	TGGTGTCTCATTGTTTATAC	450	60.65	0.90	0.042	0.670	0.281		6153.3

307	GGTGTCTCATTGTTTATACT	451	62.88	0.20	0.369	0.062	0.252		7134.0

308	GTGTCTCATTGTTTATACTA	452	59.43	0.20	−0.138	0.062	−0.062		4435.2

309	TGTCTCATTGTTTATACTAG	453	56.35	0.20	−0.589	0.062	−0.342		2035.5

310	GTCTCATTGTTTATACTAGG	454	59.21	0.20	−0.170	0.062	−0.082		2466.6

311	TCTCATTGTTTATACTAGGT	455	59.21	0.20	−0.170	0.062	−0.082		1080.9

312	CTCATTGTTTATACTAGGTA	456	57.15	0.20	−0.472	0.062	−0.269		956.0

313	TCATTGTTTATACTAGGTAT	457	55.08	0.20	−0.776	0.062	−0.458		529.4

314	CATTGTTTATACTAGGTATG	458	53.70	0.20	−0.978	0.062	−0.583		471.4

315	ATTGTTTATACTAGGTATGG	459	55.01	0.20	−0.785	0.062	−0.463		510.4

316	TTGTTTATACTAGGTATGGT	460	58.17	0.20	−0.322	0.062	−0.176		531.0

317	TGTTTATACTAGGTATGGTA	461	57.21	0.20	−0.463	0.062	−0.264		613.3

318	GTTTATACTAGGTATGGTAA	462	55.23	0.00	−0.753	−0.112	−0.510		685.1

319	TTTATACTAGGTATGGTAAA	463	50.42	0.00	−1.459	−0.112	−0.947		300.0

320	TTATACTAGGTATGGTAAAT	464	50.12	0.00	−1.504	−0.112	−0.975		316.1

321	TATACTAGGTATGGTAAATG	465	49.79	0.00	−1.551	−0.112	−1.004		387.5

322	ATACTAGGTATGGTAAATGC	466	54.30	0.00	−0.889	−0.112	−0.594		685.7

323	TACTAGGTATGGTAAATGCA	467	55.59	0.20	−0.700	0.062	−0.411		759.6

324	ACTAGGTATGGTAAATGCAG	468	56.32	0.80	−0.593	0.583	−0.146		1050.2

325	CTAGGTATGGTAAATGCAGT	469	58.78	1.10	−0.232	0.844	0.177		1020.4

326	TAGGTATGGTAAATGCAGTA	470	56.24	1.10	−0.605	0.844	−0.054		742.6

327	AGGTATGGTAAATGCAGTAT	471	56.81	1.10	−0.521	0.844	−0.002		889.6

328	GGTATGGTAAATGCAGTATA	472	56.07	1.10	−0.631	0.844	−0.070		858.8

329	GTATGGTAAATGCAGTATAC	473	54.02	1.10	−0.931	0.844	−0.256		379.0

330	TATGGTAAATGCAGTATACT	474	53.06	0.40	−1.071	0.236	−0.575		166.7

331	ATGGTAAATGCAGTATACTT	475	53.94	0.40	−0.943	0.236	−0.495		215.3

332	TGGTAAATGCAGTATACTTC	476	55.21	0.40	−0.757	0.236	−0.380		103.2

333	GGTAAATGCAGTATACTTCC	477	59.15	0.40	−0.178	0.236	−0.021		246.3

334	GTAAATGCAGTATACTTCCT	478	58.53	0.80	−0.269	0.583	0.055		163.4

335	TAAATGCAGTATACTTCCTG	479	55.54	0.10	−0.708	−0.025	−0.448		294.1

336	AAATGCAGTATACTTCCTGA	480	57.36	−0.30	−0.441	−0.373	−0.415		531.4

337	AATGCAGTATACTTCCTGAA	481	57.36	−0.30	−0.441	−0.373	−0.415		1995.5

338	ATGCAGTATACTTCCTGAAG	482	59.50	−0.30	−0.128	−0.373	−0.221		510.1

339	TGCAGTATACTTCCTGAAGT	483	62.63	−0.90	0.332	−0.895	−0.134		555.4

340	GCAGTATACTTCCTGAAGTC	484	64.24	−1.10	0.568	−1.069	−0.054		1214.0

341	CAGTATACTTCCTGAAGTCT	485	61.94	−1.10	0.230	−1.069	−0.263		825.7

342	AGTATACTTCCTGAAGTCTT	486	61.00	−1.10	0.094	−1.069	−0.348		1582.6

343	GTATACTTCCTGAAGTCTTC	487	62.28	−1.10	0.281	−1.069	−0.232		2391.8

344	TATACTTCCTGAAGTCTTCA	488	60.34	−1.10	−0.004	−1.069	−0.409		2276.3

345	ATACTTCCTGAAGTCTTCAT	489	60.91	−1.20	0.080	−1.156	−0.389		2702.8

346	TACTTCCTGAAGTCTTCATC	490	62.40	−1.20	0.299	−1.156	−0.254		3781.7

347	ACTTCCTGAAGTCTTCATCT	491	65.05	−1.20	0.686	−1.156	−0.014		5343.4

348	CTTCCTGAAGTCTTCATCTA	492	63.86	−1.20	0.512	−1.156	−0.122		6309.0

349	TTCCTGAAGTCTTCATCTAA	493	59.70	−1.20	−0.098	−1.156	−0.500		6372.4

350	TCCTGAAGTCTTCATCTAAG	494	59.55	−1.20	−0.120	−1.156	−0.513		3835.3

351	CCTGAAGTCTTCATCTAAGG	495	60.76	−1.20	0.057	−1.156	−0.404		8925.5

352	CTGAAGTCTTCATCTAAGGG	496	59.48	−1.20	−0.130	−1.156	−0.520		1211.8

353	TGAAGTCTTCATCTAAGGGA	497	58.84	−1.00	−0.224	−0.982	−0.512		609.4

354	GAAGTCTTCATCTAAGGGAA	498	56.91	−0.10	−0.507	−0.199	−0.390		629.1

355	AAGTCTTCATCTAAGGGAAC	499	56.13	−0.10	−0.622	−0.199	−0.461		749.3

356	AGTCTTCATCTAAGGGAACT	500	60.12	−0.10	−0.036	−0.199	−0.098		805.6

357	GTCTTCATCTAAGGGAACTG	501	59.84	−0.10	−0.077	−0.199	−0.124		817.0

358	TCTTCATCTAAGGGAACTGA	502	58.11	−0.10	−0.331	−0.199	−0.281		327.1

359	CTTCATCTAAGGGAACTGAA	503	54.95	−0.60	−0.794	−0.634	−0.733		320.0

360	TTCATCTAAGGGAACTGAAA	504	51.39	−0.60	−1.316	−0.634	−1.057	−0.822	84.1

361	TCATCTAAGGGAACTGAAAA	505	49.50	0.10	−1.595	−0.025	−0.998	−1.002	67.7

362	CATCTAAGGGAACTGAAAAA	506	46.98	0.10	−1.963	−0.025	−1.227	−1.171	62.2

363	ATCTAAGGGAACTGAAAAAT	507	45.78	0.10	−2.140	−0.025	−1.336	−1.298	78.9

364	TCTAAGGGAACTGAAAAATA	508	45.27	0.10	−2.214	−0.025	−1.382	−1.328	43.2

365	CTAAGGGAACTGAAAAATAT	509	44.36	0.10	−2.349	−0.025	−1.466	−1.322	50.4

366	TAAGGGAACTGAAAAATATG	510	42.71	0.10	−2.591	−0.025	−1.616	−1.242	43.7

367	AAGGGAACTGAAAAATATGC	511	46.54	0.10	−2.028	−0.025	−1.267	−1.163	45.6

368	AGGGAACTGAAAAATATGCA	512	49.21	0.30	−1.637	0.149	−0.958	−1.119	49.8

369	GGGAACTGAAAAATATGCAT	513	49.11	1.20	−1.651	0.931	−0.670	−1.082	53.2

370	GGAACTGAAAAATATGCATC	514	47.87	1.20	−1.834	0.931	−0.783	−0.958	56.6

371	GAACTGAAAAATATGCATCA	515	46.82	0.60	−1.987	0.410	−1.076	−0.844	45.3

372	AACTGAAAAATATGCATCAC	516	46.12	0.40	−2.090	0.236	−1.206	−0.773	56.3

373	ACTGAAAAATATGCATCACC	517	51.18	0.40	−1.347	0.236	−0.746		61.7

374	CTGAAAAATATGCATCACCC	518	54.20	0.40	−0.905	0.236	−0.471		224.5

375	TGAAAAATATGCATCACCCA	519	53.65	0.60	−0.985	0.410	−0.455		413.0

376	GAAAAATATGCATCACCCAC	520	54.14	1.30	−0.913	1.018	−0.179		1584.0

377	AAAAATATGCATCACCCACA	521	54.14	1.30	−0.913	1.018	−0.179		1846.7

378	AAAATATGCATCACCCACAT	522	55.78	1.10	−0.673	0.844	−0.096		2445.8

379	AAATATGCATCACCCACATC	523	58.72	0.90	−0.241	0.670	0.105		3709.4

380	AATATGCATCACCCACATCC	524	64.13	0.90	0.552	0.670	0.597		4548.4

381	ATATGCATCACCCACATCCA	525	67.27	0.90	1.013	0.670	0.883		5254.1

382	TATGCATCACCCACATCCAG	526	67.53	0.90	1.051	0.670	0.906	0.864	5527.2

383	ATGCATCACCCACATCCAGT	527	71.21	0.90	1.590	0.670	1.241	0.991	6916.9

384	TGCATCACCCACATCCAGTA	528	70.68	0.70	1.513	0.496	1.127	1.030	5861.4

385	GCATCACCCACATCCAGTAC	529	71.39	0.70	1.617	0.496	1.191	1.043	8078.4

386	CATCACCCACATCCAGTACT	530	69.16	0.70	1.290	0.496	0.988	1.013	4148.8

387	ATCACCCACATCCAGTACTG	531	67.91	0.70	1.107	0.496	0.875	0.913	3317.1

388	TCACCCACATCCAGTACTGT	532	71.15	0.10	1.582	−0.025	0.971		2486.4

389	CACCCACATCCAGTACTGTT	533	69.94	−0.40	1.404	−0.460	0.696		2746.4

390	ACCCACATCCAGTACTGTTA	534	68.25	−0.40	1.157	−0.460	0.543		2133.0

391	CCCACATCCAGTACTGTTAC	535	68.25	−0.40	1.157	−0.460	0.543		2197.0

392	CCACATCCAGTACTGTTACT	536	66.50	−0.40	0.900	−0.460	0.383		1824.0

393	CACATCCAGTACTGTTACTG	537	62.61	−1.90	0.329	−1.764	−0.467		1675.2

394	ACATCCAGTACTGTTACTGA	538	62.71	−2.30	0.344	−2.112	−0.590		1219.8

395	CATCCAGTACTGTTACTGAT	539	62.12	−2.30	0.258	−2.112	−0.643		1414.0

396	ATCCAGTACTGTTACTGATT	540	61.21	−2.30	0.124	−2.112	−0.726		1710.7

397	TCCAGTACTGTTACTGATTT	541	61.58	−2.30	0.178	−2.112	−0.692		2280.7

398	CCAGTACTGTTACTGATTTT	542	60.48	−2.30	0.017	−2.112	−0.792		2847.7

399	CAGTACTGTTACTGATTTTT	543	56.84	−1.90	−0.518	−1.764	−0.992		2830.2

400	AGTACTGTTACTGATTTTTT	544	55.82	−0.30	−0.666	−0.373	−0.555		4336.3

401	GTACTGTTACTGATTTTTTC	545	57.04	0.40	−0.488	0.236	−0.213		6581.1

402	TACTGTTACTGATTTTTTCT	546	55.95	−0.10	−0.649	−0.199	−0.478		5406.6

403	ACTGTTACTGATTTTTTCTT	547	56.89	−0.10	−0.510	−0.199	−0.392		6083.1

404	CTGTTACTGATTTTTTCTTT	548	56.67	−0.10	−0.542	−0.199	−0.412		6585.7

405	TGTTACTGATTTTTTCTTTT	549	54.96	−0.10	−0.793	−0.199	−0.567		3923.2

406	GTTACTGATTTTTTCTTTTT	550	55.36	−0.10	−0.734	−0.199	−0.531		4093.5

407	TTACTGATTTTTTCTTTTTT	551	52.62	−0.10	−1.136	−0.199	−0.780		1381.5

408	TACTGATTTTTTCTTTTTTA	552	51.70	−0.10	−1.272	−0.199	−0.864	−0.784	1194.3

409	ACTGATTTTTTCTTTTTTAA	553	50.45	−0.10	−1.454	−0.199	−0.977	−0.746	2371.3

410	CTGATTTTTTCTTTTTTAAC	554	50.45	−0.10	−1.454	−0.199	−0.977		395.9

411	TGATTTTTTCTTTTTTAACC	555	52.50	−0.10	−1.155	−0.199	−0.792		230.7

412	GATTTTTTCTTTTTTAACCC	556	56.43	0.30	−0.578	0.149	−0.302		314.9

413	ATTTTTTCTTTTTTAACCCT	557	57.05	0.80	−0.487	0.583	−0.080		276.1

414	TTTTTTCTTTTTTAACCCTG	558	56.99	0.80	−0.495	0.583	−0.085		273.3

415	TTTTTCTTTTTTAACCCTGC	559	60.68	0.80	0.045	0.583	0.250		628.4

416	TTTTCTTTTTTAACCCTGCG	560	60.85	0.80	0.071	0.583	0.265		4661.4

417	TTTCTTTTTTAACCCTGCGG	561	62.93	0.70	0.377	0.496	0.422		411.2

418	TTCTTTTTTAACCCTGCGGG	562	65.01	−0.60	0.681	−0.634	0.181		289.5

419	TCTTTTTTAACCCTGCGGGA	563	65.91	−1.00	0.813	−0.982	0.131		244.8

420	CTTTTTTAACCCTGCGGGAT	564	64.52	−1.00	0.610	−0.982	0.005		250.7

421	TTTTTTAACCCTGCGGGATG	565	62.66	−1.00	0.337	−0.982	−0.164		207.8

422	TTTTTAACCCTGCGGGATGT	566	65.23	−1.00	0.713	−0.982	0.069		255.8

423	TTTTAACCCTGCGGGATGTG	567	64.80	−1.00	0.651	−0.982	0.030		356.8

424	TTTAACCCTGCGGGATGTGG	568	66.83	−1.00	0.949	−0.982	0.215		497.8

425	TTAACCCTGCGGGATGTGGT	569	69.50	−1.00	1.339	−0.982	0.457		754.3

426	TAACCCTGCGGGATGTGGTA	570	68.63	−1.00	1.212	−0.982	0.378		902.4

427	AACCCTGCGGGATGTGGTAT	571	69.14	−1.00	1.286	−0.982	0.424		1186.6

428	ACCCTGCGGGATGTGGTATT	572	71.66	−1.00	1.657	−0.982	0.654		1514.9

429	CCCTGCGGGATGTGGTATTC	573	72.66	−0.60	1.804	−0.634	0.878		2407.6

430	CCTGCGGGATGTGGTATTCC	574	72.66	−0.60	1.804	−0.634	0.878		3019.4

431	CTGCGGGATGTGGTATTCCT	575	71.02	−1.30	1.563	−1.243	0.497		3275.3

432	TGCGGGATGTGGTATTCCTA	576	68.54	−1.30	1.199	−1.243	0.271		2830.8

433	GCGGGATGTGGTATTCCTAA	577	66.48	−1.30	0.896	−1.243	0.083		2620.5

434	CGGGATGTGGTATTCCTAAT	578	62.46	−1.30	0.307	−1.243	−0.282		1827.8

435	GGGATGTGGTATTCCTAATT	579	62.37	−1.30	0.294	−1.243	−0.290		1957.4

436	GGATGTGGTATTCCTAATTG	580	59.71	−0.90	−0.097	−0.895	−0.400		1686.2

437	GATGTGGTATTCCTAATTGA	581	58.45	−0.20	−0.281	−0.286	−0.283		1395.0

438	ATGTGGTATTCCTAATTGAA	582	55.24	−0.20	−0.752	−0.286	−0.575		1245.7

439	TGTGGTATTCCTAATTGAAC	583	55.76	−0.30	−0.675	−0.373	−0.561		1314.0

440	GTGGTATTCCTAATTGAACT	584	57.73	−0.30	−0.387	−0.373	−0.382		1818.7

441	TGGTATTCCTAATTGAACTT	585	55.15	−0.30	−0.765	−0.373	−0.616		880.3

442	GGTATTCCTAATTGAACTTC	586	56.47	−0.30	−0.572	−0.373	−0.496		1419.0

443	GTATTCCTAATTGAACTTCC	587	57.76	−0.30	−0.383	−0.373	−0.379		1567.9

444	TATTCCTAATTGAACTTCCC	588	58.57	−0.30	−0.264	−0.373	−0.306		1959.4

445	ATTCCTAATTGAACTTCCCA	589	60.26	−0.30	−0.016	−0.373	−0.152		2971.8

446	TTCCTAATTGAACTTCCCAG	590	60.45	−0.10	0.013	−0.199	−0.068		1898.5

447	TCCTAATTGAACTTCCCAGA	591	61.36	0.70	0.146	0.496	0.279		1392.3

448	CCTAATTGAACTTCCCAGAA	592	58.27	0.70	−0.308	0.496	−0.002		1143.2

449	CTAATTGAACTTCCCAGAAG	593	54.92	−0.70	−0.800	−0.721	−0.770		427.7

450	TAATTGAACTTCCCAGAAGT	594	55.84	−1.90	−0.664	−1.764	−1.082		148.5

451	AATTGAACTTCCCAGAAGTC	595	57.61	−2.10	−0.404	−1.938	−0.987		259.1

452	ATTGAACTTCCCAGAAGTCT	596	61.42	−2.10	0.154	−1.938	−0.641	−0.751	241.9

453	TTGAACTTCCCAGAAGTCTT	597	61.76	−2.10	0.205	−1.938	−0.609	−0.730	808.1

454	TGAACTTCCCAGAAGTCTTG	598	61.34	−2.10	0.143	−1.938	−0.648		351.6

455	GAACTTCCCAGAAGTCTTGA	599	62.71	−2.10	0.344	−1.938	−0.523		499.7

456	AACTTCCCAGAAGTCTTGAG	600	61.63	−2.10	0.186	−1.938	−0.621		407.4

457	ACTTCCCAGAAGTCTTGAGT	601	66.97	−1.90	0.969	−1.764	−0.069		492.1

458	CTTCCCAGAAGTCTTGAGTT	602	66.75	−1.00	0.937	−0.982	0.208		736.1

459	TTCCCAGAAGTCTTGAGTTC	603	66.31	−0.20	0.872	−0.286	0.432		815.2

460	TCCCAGAAGTCTTGAGTTCT	604	67.98	−1.20	1.116	−1.156	0.253		888.8

461	CCCAGAAGTCTTGAGTTCTC	605	67.98	−1.40	1.116	−1.330	0.187		2021.6

462	CCAGAAGTCTTGAGTTCTCT	606	66.10	−1.40	0.842	−1.330	0.017		1988.5

463	CAGAAGTCTTGAGTTCTCTT	607	62.41	−1.40	0.300	−1.330	−0.319		2008.8

464	AGAAGTCTTGAGTTCTCTTA	608	60.43	−1.20	0.009	−1.156	−0.434		2631.8

465	GAAGTCTTGAGTTCTCTTAT	609	60.20	−0.50	−0.025	−0.547	−0.223		3052.8

466	AAGTCTTGAGTTCTCTTATT	610	59.12	0.30	0.183	0.149	−0.057		3509.3

467	AGTCTTGAGTTCTCTTATTA	611	60.75	0.30	0.056	0.149	0.091		3221.6

468	GTCTTGAGTTCTCTTATTAA	612	58.29	0.30	−0.305	0.149	−0.132		3677.1

469	TCTTGAGTTCTCTTATTAAG	613	55.25	0.30	−0.751	0.149	−0.409		1176.6

470	CTTGAGTTCTCTTATTAAGT	614	57.04	0.10	−0.488	−0.025	−0.312		1168.1

471	TTGAGTTCTCTTATTAAGTT	615	55.29	0.10	−0.745	−0.025	−0.471		666.3

472	TGAGTTCTCTTATTAAGTTC	616	56.35	0.10	−0.589	−0.025	−0.375		674.0

473	GAGTTCTCTTATTAAGTTCT	617	58.57	0.10	−0.263	−0.025	−0.173		1471.4

474	AGTTCTCTTATTAAGTTCTC	618	58.61	0.10	−0.257	−0.025	−0.169		1493.5

475	GTTCTCTTATTAAGTTCTCT	619	60.59	0.10	0.032	−0.025	0.011		2191.5

476	TTCTCTTATTAAGTTCTCTG	620	57.16	0.10	−0.471	−0.025	−0.301		1410.3

477	TCTCTTATTAAGTTCTCTGA	621	58.23	0.10	−0.314	−0.025	−0.204		1262.8

478	CTCTTATTAAGTTCTCTGAA	622	54.79	0.10	−0.817	−0.025	−0.516		1072.9

479	TCTTATTAAGTTCTCTGAAA	623	50.95	0.10	−1.382	−0.025	−0.866		540.9

480	CTTATTAAGTTCTCTGAAAT	624	49.77	0.50	−1.554	0.323	−0.841		539.2

481	TTATTAAGTTCTCTGAAATC	625	48.99	0.50	−1.668	0.323	−0.912	−0.768	709.0

482	TATTAAGTTCTCTGAAATCT	626	50.64	0.50	−1.427	0.323	−0.762	−0.775	978.1

483	ATTAAGTTCTCTGAAATCTA	627	50.64	0.50	−1.427	0.323	−0.762	−0.732	1217.7

484	TTAAGTTCTCTGAAATCTAC	628	51.15	0.50	−1.352	0.323	−0.716		1748.1

485	TAAGTTCTCTGAAATCTACT	629	52.79	0.50	−1.112	0.323	−0.567		2511.5

486	AAGTTCTCTGAAATCTACTA	630	52.79	0.50	−1.112	0.323	−0.567		2997.2

487	AGTTCTCTGAAATCTACTAA	631	52.79	0.50	−1.112	0.323	−0.567		2887.6

488	GTTCTCTGAAATCTACTAAT	632	52.65	0.50	−1.133	0.323	−0.580		4421.3

489	TTCTCTGAAATCTACTAATT	633	50.14	0.70	−1.500	0.496	−0.741	−0.832	1937.7

490	TCTCTGAAATCTACTAATTT	634	50.14	0.20	−1.500	0.062	−0.906	−0.962	1773.3

491	CTCTGAAATCTACTAATTTT	635	49.31	−0.30	−1.622	−0.373	−1.147	−1.102	1491.1

492	TCTGAAATCTACTAATTTTC	636	48.55	−0.60	−1.734	−0.634	−1.316	−1.171	376.6

493	CTGAAATCTACTAATTTTCT	637	49.31	−1.30	−1.622	−1.243	−1.478	−1.178	371.9

494	TGAAATCTACTAATTTTCTC	638	48.55	−1.30	−1.734	−1.243	−1.547	−1.092	415.2

495	GAAATCTACTAATTTTCTCC	639	52.45	−0.90	−1.161	−0.895	−1.060	−0.938	1097.9

496	AAATCTACTAATTTTCTCCA	640	52.47	−0.10	−1.158	−0.199	−0.794	−0.778	1429.1

497	AATCTACTAATTTTCTCCAT	641	54.25	0.90	−0.897	0.670	−0.301		1812.5

498	ATCTACTAATTTTCTCCATT	642	56.46	1.00	−0.572	0.757	−0.067		1943.4

499	TCTACTAATTTTCTCCATTT	643	56.80	0.50	−0.523	0.323	−0.202		1506.1

500	CTACTAATTTTCTCCATTTA	644	54.93	0.50	−0.797	0.323	−0.372		1694.7

501	TACTAATTTTCTCCATTTAG	645	53.14	0.30	−1.060	0.149	−0.600		946.7

502	ACTAATTTTCTCCATTTAGT	646	56.69	−0.70	−0.539	−0.721	−0.608		1114.3

503	CTAATTTTCTCCATTTAGTA	647	55.57	0.00	−0.704	−0.112	−0.479		963.9

504	TAATTTTCTCCATTTAGTAC	648	54.12	0.50	−0.917	0.323	−0.446		1347.9

505	AATTTTCTCCATTTAGTACT	649	56.69	0.70	−0.539	0.496	−0.145		2067.7

506	ATTTTCTCCATTTAGTACTG	650	58.66	0.80	−0.250	0.583	0.067		2724.2

507	TTTTCTCCATTTAGTACTGT	651	61.92	0.60	0.228	0.410	0.297		3367.9

508	TTTCTCCATTTAGTACTGTC	652	63.10	0.60	0.401	0.410	0.404		5235.8

509	TTCTCCATTTAGTACTGTCT	653	64.84	0.60	0.656	0.410	0.562		6423.5

510	TCTCCATTTAGTACTGTCTT	654	64.84	0.60	0.656	0.410	0.562		7758.9

511	CTCCATTTAGTACTGTCTTT	655	63.63	0.60	0.479	0.410	0.453		8001.5

512	TCCATTTAGTACTGTCTTTT	656	61.92	0.60	0.228	0.410	0.297		5512.4

513	CCATTTAGTACTGTCTTTTT	657	60.78	0.60	0.061	0.410	0.194		5300.0

514	CATTTAGTACTGTCTTTTTT	658	57.04	0.80	−0.489	0.583	−0.081		3902.1

515	ATTTAGTACTGTCTTTTTTC	659	57.08	0.80	−0.482	0.583	−0.077		4641.8

516	TTTAGTACTGTCTTTTTTCT	660	59.26	0.80	−0.162	0.583	0.121		4888.4

517	TTAGTACTGTCTTTTTTCTT	661	59.26	0.80	−0.162	0.583	0.121		5477.3

518	TAGTACTGTCTTTTTTCTTT	662	59.26	0.80	−0.162	0.583	0.121		5064.9

519	AGTACTGTCTTTTTTCTTTA	663	59.26	1.00	−0.162	0.757	0.187		5580.3

520	GTACTGTCTTTTTTCTTTAT	664	59.04	2.70	−0.195	2.236	0.729		5478.3

521	TACTGTCTTTTTTCTTTATG	665	55.71	2.90	−0.683	2.410	0.492		2275.5

522	ACTGTCTTTTTTCTTTATGG	666	59.07	1.70	−0.190	1.366	0.402		1730.8

523	CTGTCTTTTTTCTTTATGGC	667	62.92	1.70	0.374	1.366	0.751		2405.5

524	TGTCTTTTTTCTTTATGGCA	668	62.14	1.70	0.260	1.366	0.680		1942.0

525	GTCTTTTTTCTTTATGGCAA	669	60.05	1.50	−0.047	1.192	0.424		2085.6

526	TCTTTTTTCTTTATGGCAAA	670	54.99	0.60	−0.788	0.410	−0.333		493.2

527	CTTTTTTCTTTATGGCAAAT	671	53.75	0.10	−0.971	−0.025	−0.612		532.7

528	TTTTTTCTTTATGGCAAATA	672	51.30	0.10	−1.331	−0.025	−0.835		280.0

529	TTTTTCTTTATGGCAAATAC	673	51.49	0.10	−1.302	−0.025	−0.817		440.8

530	TTTTCTTTATGGCAAATACT	674	53.08	0.10	−1.069	−0.025	−0.672		463.1

531	TTTCTTTATGGCAAATACTG	675	52.74	0.10	−1.119	−0.025	−0.704		579.0

532	TTCTTTATGGCAAATACTGG	676	54.90	0.10	−0.802	−0.025	−0.507		673.7

533	TCTTTATGGCAAATACTGGA	677	55.85	0.10	−0.663	−0.025	−0.421		837.0

534	CTTTATGGCAAATACTGGAG	678	54.78	0.10	−0.820	−0.025	−0.518		1061.9

535	TTTATGGCAAATACTGGAGT	679	55.74	0.30	−0.679	0.149	−0.365		855.0

536	TTATGGCAAATACTGGAGTA	680	54.87	0.60	−0.806	0.410	−0.344		775.0

537	TATGGCAAATACTGGAGTAT	681	54.56	0.00	−0.852	−0.112	−0.571		773.6

535	ATGGCAAATACTGGAGTATT	682	55.42	−1.00	−0.726	−0.982	−0.823		702.5

539	TGGCAAATACTGGAGTATTG	683	55.37	−1.20	−0.733	−1.156	−0.893	−0.775	387.5

540	GGCAAATACTGGAGTATTGT	684	58.33	−1.20	−0.298	−1.156	−0.624	−0.924	435.3

541	GCAAATACTGGAGTATTGTA	685	55.24	−1.20	−0.753	−1.156	−0.906	−0.974	93.7

542	CAAATACTGGAGTATTGTAT	686	51.30	−1.20	−1.331	−1.156	−1.264	−0.913	50.0

543	AAATACTGGAGTATTGTATG	687	49.96	−1.20	−1.527	−1.156	−1.386	−0.809	50.4

544	AATACTGGAGTATTGTATGG	688	54.30	−1.00	−0.890	−0.982	−0.925		64.7

545	ATACTGGAGTATTGTATGGA	689	57.60	−0.30	−0.406	−0.373	−0.394		76.0

546	TACTGGAGTATTGTATGGAT	690	57.60	0.40	−0.406	0.236	−0.162		86.0

547	ACTGGAGTATTGTATGGATT	691	58.53	1.30	−0.269	1.018	0.220		123.4

545	CTGGAGTATTGTATGGATTC	692	59.39	2.00	−0.144	1.627	0.529		121.5

549	TGGAGTATTGTATGGATTCT	693	59.39	1.80	−0.144	1.453	0.463		641.3

550	GGAGTATTGTATGGATTCTC	694	60.95	0.60	0.086	0.410	0.209		161.5

551	GAGTATTGTATGGATTCTCA	695	59.52	0.60	−0.124	0.410	0.079		129.9

552	AGTATTGTATGGATTCTCAG	696	58.31	1.10	−0.302	0.844	0.134		88.7

553	GTATTGTATGGATTCTCAGG	697	60.87	1.10	0.074	0.844	0.367		112.5

554	TATTGTATGGATTCTCAGGC	698	61.97	1.10	0.236	0.844	0.467		134.6

555	ATTGTATGGATTCTCAGGCC	699	66.52	1.10	0.902	0.844	0.880		191.6

556	TTGTATGGATTCTCAGGCCC	700	70.34	0.70	1.463	0.496	1.096		254.5

557	TGTATGGATTCTCAGGCCCA	701	71.11	0.20	1.577	0.062	1.001		332.2

558	GTATGGATTCTCAGGCCCAA	702	68.95	0.00	1.259	−0.112	0.738		415.6

559	TATGGATTCTCAGGCCCAAT	703	65.78	0.00	0.795	−0.112	0.450		285.0

560	ATGGATTCTCAGGCCCAATT	704	66.68	0.00	0.925	−0.112	0.531		464.0

561	TGGATTCTCAGGCCCAATTT	705	67.04	0.20	0.979	0.062	0.630		492.5

562	GGATTCTCAGGCCCAATTTT	706	67.51	1.10	1.048	0.844	0.970		639.7

563	GATTCTCAGGCCCAATTTTT	707	65.34	1.30	0.729	1.018	0.839		512.4

564	ATTCTCAGGCCCAATTTTTG	708	63.94	0.60	0.524	0.410	0.481		393.4

565	TTCTCAGGCCCAATTTTTGA	709	65.24	0.20	0.716	0.062	0.467		334.3

566	TCTCAGGCCCAATTTTTGAA	710	62.85	0.20	0.364	0.062	0.249		308.2

567	CTCAGGCCCAATTTTTGAAA	711	59.62	0.20	−0.109	0.062	−0.044		199.2

568	TCAGGCCCAATTTTTGAAAT	712	57.85	0.20	−0.369	0.062	−0.205		164.3

569	CAGGCCCAATTTTTGAAATT	713	56.95	−0.50	−0.501	−0.547	−0.518		125.6

570	AGGCCCAATTTTTGAAATTT	714	56.09	−1.00	−0.627	−0.982	−0.762		102.6

571	GGCCCAATTTTTGAAATTTT	715	56.23	−1.00	−0.606	−0.982	−0.749		91.6

572	GCCCAATTTTTGAAATTTTC	716	55.07	−1.00	−0.777	−0.982	−0.855	−0.806	76.2

573	CCCAATTTTTGAAATTTTCC	717	54.96	−1.00	−0.792	−0.982	−0.864	−0.881	78.8

574	CCAATTTTTGAAATTTTCCC	718	54.96	−1.00	−0.792	−0.982	−0.864	−0.841	84.8

575	CAATTTTTGAAATTTTCCCT	719	53.17	−1.00	−1.055	−0.982	−1.027	−0.755	162.0

576	AATTTTTGAAATTTTCCCTT	720	52.25	−0.80	−1.190	−0.808	−1.045		539.5

577	ATTTTTGAAATTTTCCCTTC	721	55.17	0.10	−0.762	−0.025	−0.482		1787.3

578	TTTTTGAAATTTTCCCTTCC	722	58.88	0.10	−0.219	−0.025	−0.145		6354.2

579	TTTTGAAATTTTCCCTTCCT	723	60.39	0.10	0.004	−0.025	−0.007		9513.6

580	TTTGAAATTTTCCCTTCCTT	724	60.39	0.10	0.004	−0.025	−0.007		10660.0

581	TTGAAATTTTCCCTTCCTTT	725	60.39	0.10	0.004	−0.025	−0.007		11202.0

582	TGAAATTTTCCCTTCCTTTT	726	60.39	0.10	0.004	−0.025	−0.007		11543.0

583	GAAATTTTCCCTTCCTTTTC	727	61.81	0.40	0.212	0.236	0.221		14774.0

584	AAATTTTCCCTTCCTTTTCC	728	64.17	1.20	0.557	0.931	0.699	0.952	18197.0

585	AATTTTCCCTTCCTTTTCCA	729	67.39	1.70	1.030	1.366	1.158	1.307	21410.0

586	ATTTTCCCTTCCTTTTCCAT	730	69.58	4.00	1.351	3.366	2.117	1.679	22869.0

587	TTTTCCCTTCCTTTTCCATT	731	69.96	5.00	1.408	4.236	2.482	2.039	21818.0

588	TTTCCCTTCCTTTTCCATTT	732	69.96	5.00	1.408	4.236	2.482	2.113	21341.0

589	TTCCCTTCCTTTTCCATTTC	733	71.19	5.00	1.588	4.236	2.594	2.085	22063.0

590	TCCCTTCCTTTTCCATTTCT	734	72.77	5.00	1.820	4.236	2.738	1.863	22152.0

591	CCCTTCCTTTTCCATTTCTG	735	71.01	0.90	1.561	0.670	1.223	1.571	20764.0

592	CCTTCCTTTTCCATTTCTGT	736	70.68	0.20	1.513	0.062	0.961	1.289	12579.0

593	CTTCCTTTTCCATTTCTGTA	737	66.30	0.20	0.870	0.062	0.563	0.945	9036.3

594	TTCCTTTTCCATTTCTGTAC	738	64.87	0.20	0.660	0.062	0.433		8251.8

595	TCCTTTTCCATTTCTGTACA	739	65.74	0.20	0.788	0.062	0.512		20788.0

596	CCTTTTCCATTTCTGTACAA	740	62.11	0.20	0.256	0.062	0.182		7073.9

597	CTTTTCCATTTCTGTACAAA	741	56.39	0.20	−0.583	0.062	−0.338		2932.4

598	TTTTCCATTTCTGTACAAAT	742	54.49	0.20	−0.862	0.062	−0.511		1897.3

599	TTTCCATTTCTGTACAAATT	743	54.49	−0.30	−0.862	−0.373	−0.676		2158.1

600	TTCCATTTCTGTACAAATTT	744	54.49	−0.30	−0.862	−0.373	−0.676		2215.9

601	TCCATTTCTGTACAAATTTC	745	55.43	−0.30	−0.724	−0.373	−0.591		2168.6

602	CCATTTCTGTACAAATTTCT	746	56.07	−0.30	−0.631	−0.373	−0.533		2025.8

603	CATTTCTGTACAAATTTCTA	747	51.65	−0.30	−1.278	−0.373	−0.934		1277.2

604	ATTTCTGTACAAATTTCTAC	748	50.83	−0.10	−1.398	−0.199	−0.943	−0.736	1944.8

605	TTTCTGTACAAATTTCTACT	749	52.78	0.40	−1.112	0.236	−0.600	−0.790	2504.3

606	TTCTGTACAAATTTCTACTA	750	51.90	0.40	−1.242	0.236	−0.681	−0.876	2941.5

607	TCTGTACAAATTTCTACTAA	751	49.84	0.40	−1.544	0.236	−0.868	−0.846	2694.8

608	CTGTACAAATTTCTACTAAT	752	48.73	0.40	−1.707	0.236	−0.969	−0.827	2610.7

609	TGTACAAATTTCTACTAATG	753	46.88	0.40	−1.979	0.236	−1.137	−0.845	1678.1

610	GTACAAATTTCTACTAATGC	754	50.66	0.60	−1.424	0.410	−0.727	−0.854	5877.3

611	TACAAATTTCTACTAATGCT	755	49.82	0.60	−1.547	0.410	−0.803	−0.849	4461.0

612	ACAAATTTCTACTAATGCTT	756	50.65	0.60	−1.425	0.410	−0.728	−0.816	5943.2

613	CAAATTTCTACTAATGCTTT	757	50.46	0.60	−1.453	0.410	−0.745	−0.753	6492.9

614	AAATTTCTACTAATGCTTTT	758	49.47	0.60	−1.599	0.410	−0.836	−0.745	6875.0

615	AATTTCTACTAATGCTTTTA	759	50.61	0.60	−1.431	0.410	−0.731		7950.3

616	ATTTCTACTAATGCTTTTAT	760	52.40	0.20	−1.169	0.062	−0.701		8314.8

617	TTTCTACTAATGCTTTTATT	761	52.72	0.20	−1.122	0.062	−0.672		6885.8

618	TTCTACTAATGCTTTTATTT	762	52.72	0.20	−1.122	0.062	−0.672		6443.2

619	TCTACTAATGCTTTTATTTT	763	52.72	0.20	−1.122	0.062	−0.672	−0.731	6331.0

620	CTACTAATGCTTTTATTTTT	764	51.81	0.20	−1.255	0.062	−0.755		5952.5

621	TACTAATGCTTTTATTTTTT	765	50.18	0.20	−1.494	0.062	−0.903		2662.8

622	ACTAATGCTTTTATTTTTTC	766	51.96	0.20	−1.233	0.062	−0.741		3034.0

623	CTAATGCTTTTATTTTTTCT	767	53.41	0.20	−1.021	0.062	−0.609		2198.5

624	TAATGCTTTTATTTTTTCTT	768	51.76	0.40	−1.263	0.236	−0.694		1670.1

625	AATGCTTTTATTTTTTCTTC	769	53.61	1.10	−0.992	0.844	−0.294		3039.4

626	ATGCTTTTATTTTTTCTTCT	770	57.66	2.10	−0.397	1.714	0.405		3873.8

627	TGCTTTTATTTTTTCTTCTG	771	57.60	2.80	−0.406	2.323	0.631		3609.7

628	GCTTTTATTTTTTCTTCTGT	772	60.96	3.10	0.087	2.583	1.036		4891.4

629	CTTTTATTTTTTCTTCTGTC	773	57.96	3.10	−0.353	2.583	0.763		3071.6

630	TTTTATTTTTTCTTCTGTCA	774	57.22	3.10	−0.461	2.583	0.696		2667.2

631	TTTATTTTTTCTTCTGTCAA	775	54.81	1.70	−0.816	1.366	0.013		2293.1

632	TTATTTTTTCTTCTGTCAAT	776	54.46	1.20	−0.866	0.931	−0.183		2123.0

633	TATTTTTTCTTCTGTCAATG	777	54.08	1.20	−0.922	0.931	−0.218		1914.7

634	ATTTTTTCTTCTGTCAATGG	778	57.36	1.20	−0.442	0.931	0.080		2174.1

635	TTTTTTCTTCTGTCAATGGC	779	61.67	1.20	0.192	0.931	0.473		3659.7

636	TTTTTCTTCTGTCAATGGCC	780	65.26	1.20	0.717	0.931	0.799		5217.7

637	TTTTCTTCTGTCAATGGCCA	781	66.11	1.20	0.843	0.931	0.877		4559.7

638	TTTCTTCTGTCAATGGCCAT	782	65.73	1.00	0.787	0.757	0.776		4347.7

639	TTCTTCTGTCAATGGCCATT	783	65.73	1.00	0.787	0.757	0.776		5267.4

640	TCTTCTGTCAATGGCCATTG	784	65.26	−0.60	0.718	−0.634	0.204		3922.8

641	CTTCTGTCAATGGCCATTGT	785	66.97	−1.30	0.968	−1.243	0.128		3608.6

642	TTCTGTCAATGGCCATTGTT	786	65.36	−1.30	0.733	−1.243	−0.018		1881.6

643	TCTGTCAATGGCCATTGTTT	787	65.36	−1.30	0.733	−1.243	−0.018		1658.0

644	CTGTCAATGGCCATTGTTTA	788	63.32	−1.30	0.433	−1.243	−0.204		1369.8

645	TGTCAATGGCCATTGTTTAA	789	59.38	−1.30	−0.144	−1.243	−0.562		605.8

646	GTCAATGGCCATTGTTTAAC	790	59.99	−1.30	−0.055	−1.243	−0.506		933.2

647	TCAATGGCCATTGTTTAACT	791	58.93	−1.30	−0.211	−1.243	−0.603		441.8

648	CAATGGCCATTGTTTAACTT	792	57.97	−0.90	−0.352	−0.895	−0.558		545.6

649	AATGGCCATTGTTTAACTTT	793	57.07	0.90	−0.483	0.670	−0.045		781.4

650	ATGGCCATTGTTTAACTTTT	794	59.31	0.90	−0.156	0.670	0.158		1027.3

651	TGGCCATTGTTTAACTTTTG	795	59.24	0.90	−0.165	0.670	0.152		1102.5

652	GGCCATTGTTTAACTTTTGG	796	61.84	0.30	0.216	0.149	0.190		935.7

653	GCCATTGTTTAACTTTTGGG	797	61.84	−0.10	0.216	−0.199	0.058		403.7

654	CCATTGTTTAACTTTTGGGC	798	61.84	0.30	0.216	0.149	0.190		269.3

655	CATTGTTTAACTTTTGGGCC	799	61.84	0.90	0.216	0.670	0.389		296.8

656	ATTGTTTAACTTTTGGGCCA	800	61.84	0.90	0.216	0.670	0.389		449.4

657	TTGTTTAACTTTTGGGCCAT	801	61.84	0.90	0.216	0.670	0.389		448.1

658	TGTTTAACTTTTGGGCCATC	802	62.91	0.90	0.373	0.670	0.486		584.9

659	GTTTAACTTTTGGGCCATCC	803	66.73	0.40	0.934	0.236	0.669		1032.4

660	TTTAACTTTTGGGCCATCCA	804	64.79	−0.70	0.649	−0.721	0.128		737.8

661	TTAACTTTTGGGCCATCCAT	805	64.44	−1.20	0.598	−1.156	−0.069		950.2

662	TAACTTTTGGGCCATCCATT	806	64.44	−1.20	0.598	−1.156	−0.069		1308.0

663	AACTTTTGGGCCATCCATTC	807	66.42	−1.20	0.888	−1.156	0.111		2360.1

664	ACTTTTGGGCCATCCATTCC	808	72.21	−1.20	1.738	−1.156	0.638		4946.0

665	CTTTTGGGCCATCCATTCCT	809	73.53	−1.20	1.930	−1.156	0.758		6789.2

666	TTTTGGGCCATCCATTCCTG	810	71.49	−1.20	1.632	−1.156	0.573		8150.6

667	TTTGGGCCATCCATTCCTGG	811	73.62	−1.20	1.945	−1.156	0.766		7589.0

668	TTGGGCCATCCATTCCTGGC	812	77.43	−2.80	2.504	−2.547	0.584		13914.0

669	TGGGCCATCCATTCCTGGCT	813	78.94	−3.50	2.725	−3.156	0.490		17513.0

670	GGGCCATCCATTCCTGGCTT	814	79.51	−3.50	2.809	−3.156	0.542		19883.0

671	GGCCATCCATTCCTGGCTTT	815	77.37	−3.50	2.494	−3.156	0.347		20103.0

672	GCCATCCATTCCTGGCTTTA	816	74.28	−3.10	2.040	−2.808	0.198		18622.0

673	CCATCCATTCCTGGCTTTAA	817	67.92	−1.30	1.109	−1.243	0.215		16915.0

674	CATCCATTCCTGGCTTTAAT	818	64.36	−1.30	0.585	−1.243	−0.109		13910.0

675	ATCCATTCCTGGCTTTAATT	819	63.53	−1.30	0.464	−1.243	−0.185		12524.0

676	TCCATTCCTGGCTTTAATTT	820	63.88	−1.30	0.516	−1.243	−0.152		11890.0

677	CCATTCCTGGCTTTAATTTT	821	62.81	−0.90	0.359	−0.895	−0.118		12839.0

678	CATTCCTGGCTTTAATTTTA	822	58.55	0.90	−0.266	0.670	0.090		9726.8

679	ATTCCTGGCTTTAATTTTAC	823	57.84	1.50	−0.371	1.192	0.223		8499.7

680	TTCCTGGCTTTAATTTTACT	824	59.78	1.90	−0.086	1.540	0.532		6800.4

681	TCCTGGCTTTAATTTTACTG	825	59.37	1.90	−0.146	1.540	0.494		5445.6

682	CCTGGCTTTAATTTTACTGG	826	60.53	1.90	0.024	1.540	0.600		2901.6

683	CTGGCTTTAATTTTACTGGT	827	59.77	1.90	−0.087	1.540	0.531		1174.2

684	TGGCTTTAATTTTACTGGTA	828	57.25	1.90	−0.458	1.540	0.301		521.3

685	GGCTTTAATTTTACTGGTAC	829	57.86	1.90	−0.368	1.540	0.357		611.1

686	GCTTTAATTTTACTGGTACA	830	56.55	1.80	−0.560	1.453	0.205		287.6

687	CTTTAATTTTACTGGTACAG	831	52.66	0.40	−1.130	0.236	−0.611		109.5

688	TTTAATTTTACTGGTACAGT	832	53.62	−0.80	−0.989	−0.808	−0.920		59.5

689	TTAATTTTACTGGTACAGTC	833	54.59	−1.00	−0.847	−0.982	−0.898		62.1

690	TAATTTTACTGGTACAGTCT	834	56.28	−1.00	−0.599	−0.982	−0.745		59.4

691	AATTTTACTGGTACAGTCTC	835	58.27	−1.00	−0.308	−0.982	−0.564		68.0

692	ATTTTACTGGTACAGTCTCA	836	61.78	−1.00	0.207	−0.982	−0.245		72.9

693	TTTTACTGGTACAGTCTCAA	837	59.61	−1.00	−0.111	−0.982	−0.442		62.2

694	TTTACTGGTACAGTCTCAAT	838	59.25	−1.00	−0.164	−0.982	−0.475		64.5

695	TTACTGGTACAGTCTCAATA	839	58.30	−1.00	−0.303	−0.982	−0.561		53.5

696	TACTGGTACAGTCTCAATAG	840	58.15	−1.00	−0.326	−0.982	−0.575		57.8

697	ACTGGTACAGTCTCAATAGG	841	61.44	−0.80	0.157	−0.808	−0.210		341.0

698	CTGGTACAGTCTCAATAGGG	842	63.55	0.10	0.467	−0.025	0.280		54.8

699	TGGTACAGTCTCAATAGGGC	843	65.89	1.10	0.810	0.844	0.823		47.1

700	GGTACAGTCTCAATAGGGCT	844	68.08	0.90	1.131	0.670	0.956		59.7

701	GTACAGTCTCAATAGGGCTA	845	64.73	0.70	0.640	0.496	0.586		47.0

702	TACAGTCTCAATAGGGCTAA	846	59.35	0.70	−0.149	0.496	0.096		49.3

703	ACAGTCTCAATAGGGCTAAT	847	59.91	0.70	−0.067	0.496	0.147		55.0

704	CAGTCTCAATAGGGCTAATG	848	59.29	0.70	−0.158	0.496	0.091		49.0

705	AGTCTCAATAGGGCTAATGG	849	60.62	0.90	0.037	0.670	0.278		45.7

706	GTCTCAATAGGGCTAATGGG	850	63.00	1.10	0.386	0.844	0.560		115.6

707	TCTCAATAGGGCTAATGGGA	851	61.22	0.40	0.125	0.236	0.167		50.6

708	CTCAATAGGGCTAATGGGAA	852	57.97	1.40	−0.352	1.105	0.202		48.0

709	TCAATAGGGCTAATGGGAAA	853	54.39	1.40	−0.877	1.105	−0.124		50.5

710	CAATAGGGCTAATGGGAAAA	854	51.64	1.80	−1.281	1.453	−0.242		44.1

711	AATAGGGCTAATGGGAAAAT	855	50.45	1.90	−1.454	1.540	−0.316		43.1

712	ATAGGGCTAATGGGAAAATT	856	52.34	1.00	−1.178	0.757	−0.442		45.2

713	TAGGGCTAATGGGAAAATTT	857	52.63	0.50	−1.135	0.323	−0.581		47.4

714	AGGGCTAATGGGAAAATTTA	858	52.63	0.50	−1.135	0.323	−0.581		50.0

715	GGGCTAATGGGAAAATTTAA	859	50.89	0.50	−1.390	0.323	−0.739	−0.867	47.8

716	GGCTAATGGGAAAATTTAAA	860	47.14	0.50	−1.940	0.323	−1.080	−1.022	50.2

717	GCTAATGGGAAAATTTAAAG	861	45.00	0.50	−2.254	0.323	−1.275	−1.096	43.0

718	CTAATGGGAAAATTTAAAGT	862	43.95	0.50	−2.408	0.323	−1.371	−1.088	57.0

719	TAATGGGAAAATTTAAAGTG	863	42.27	0.50	−2.655	0.323	−1.524	−1.072	58.7

720	AATGGGAAAATTTAAAGTGC	864	46.18	0.70	−2.081	0.496	−1.102	−1.011	183.6

721	ATGGGAAAATTTAAAGTGCA	865	48.90	1.70	−1.682	1.366	−0.524	−0.924	303.4

722	TGGGAAAATTTAAAGTGCAA	866	47.39	1.80	−1.903	1.453	−0.628	−0.837	135.7

723	GGGAAAATTTAAAGTGCAAC	867	47.84	1.60	−1.838	1.279	−0.653	−0.766	241.7

724	GGAAAATTTAAAGTGCAACC	868	49.12	1.20	−1.649	0.931	−0.669	−0.737	132.5

725	GAAAATTTAAAGTGCAACCA	869	48.09	1.20	−1.801	0.931	−0.763	−0.758	128.8

726	AAAATTTAAAGTGCAACCAA	870	45.57	1.10	−2.171	0.844	−1.025		141.0

727	AAATTTAAAGTGCAACCAAT	871	46.97	1.10	−1.965	0.844	−0.897		282.0

728	AATTTAAAGTGCAACCAATC	872	49.46	1.10	−1.599	0.844	−0.671		948.6

729	ATTTAAAGTGCAACCAATCT	873	52.84	1.10	−1.104	0.844	−0.363		1815.1

730	TTTAAAGTGCAACCAATCTG	874	52.81	1.10	−1.109	0.844	−0.366		3188.2

731	TTAAAGTGCAACCAATCTGA	875	53.71	1.00	−0.976	0.757	−0.317		3566.1

732	TAAAGTGCAACCAATCTGAG	876	53.56	1.00	−0.999	0.757	−0.331		2925.1

733	AAAGTGCAACCAATCTGAGT	877	56.81	1.00	−0.522	0.757	−0.036		3233.2

734	AAGTGCAACCAATCTGAGTC	878	59.99	1.00	−0.055	0.757	0.254		3615.6

735	AGTGCAACCAATCTGAGTCA	879	63.25	1.00	0.422	0.757	0.550		3994.8

736	GTGCAACCAATCTGAGTCAA	880	61.00	1.00	0.093	0.757	0.345		4033.0

737	TGCAACCAATCTGAGTCAAC	881	58.62	1.00	−0.257	0.757	0.128		3380.2

738	GCAACCAATCTGAGTCAACA	882	59.87	1.00	−0.073	0.757	0.242		4288.7

739	CAACCAATCTGAGTCAACAG	883	56.22	−0.30	−0.608	−0.373	−0.519		744.1

740	AACCAATCTGAGTCAACAGA	884	56.24	−1.60	−0.605	−1.504	−0.946	−0.757	392.2

741	ACCAATCTGAGTCAACAGAT	885	58.10	−2.30	−0.332	−2.112	−1.009	−1.030	158.1

742	CCAATCTGAGTCAACAGATT	886	57.90	−3.30	−0.362	−2.982	−1.357	−1.219	70.8

743	CAATCTGAGTCAACAGATTT	887	54.41	−3.80	−0.874	−3.417	−1.840	−1.262	190.0

744	AATCTGAGTCAACAGATTTC	888	54.37	−3.60	−0.880	−3.243	−1.778	−1.168	87.7

745	ATCTGAGTCAACAGATTTCT	889	58.37	−2.60	−0.293	−2.373	−1.084	−1.017	152.7

746	TCTGAGTCAACAGATTTCTT	890	58.73	−1.90	−0.241	−1.764	−0.820	−0.797	270.5

747	CTGAGTCAACAGATTTCTTC	891	58.73	−0.30	−0.241	−0.373	−0.291		498.7

748	TGAGTCAACAGATTTCTTCC	892	60.70	0.20	0.049	0.062	0.054		891.0

749	GAGTCAACAGATTTCTTCCA	893	62.06	0.20	0.248	0.062	0.177		1509.8

750	AGTCAACAGATTTCTTCCAA	894	58.66	0.20	−0.250	0.062	−0.132		1009.3

751	GTCAACAGATTTCTTCCAAT	895	58.47	0.20	−0.279	0.062	−0.149		1198.0

752	TCAACAGATTTCTTCCAATT	896	55.86	0.20	−0.661	0.062	−0.387		680.5

753	CAACAGATTTCTTCCAATTA	897	54.08	0.20	−0.922	0.062	−0.548		762.5

754	AACAGATTTCTTCCAATTAT	898	52.82	0.20	−1.107	0.062	−0.663		689.8

755	ACAGATTTCTTCCAATTATG	899	54.58	0.20	−0.849	0.062	−0.503		715.1

756	CAGATTTCTTCCAATTATGT	900	56.99	0.20	−0.496	0.062	−0.284		833.8

757	AGATTTCTTCCAATTATGTT	901	56.02	0.20	−0.638	0.062	−0.372		1067.7

758	GATTTCTTCCAATTATGTTG	902	55.80	0.30	−0.670	0.149	−0.359		1225.9

759	ATTTCTTCCAATTATGTTGA	903	55.80	−0.10	−0.670	−0.199	−0.491		1028.7

760	TTTCTTCCAATTATGTTGAC	904	56.34	−0.10	−0.591	−0.199	−0.442		1419.0

761	TTCTTCCAATTATGTTGACA	905	57.29	−0.10	−0.452	−0.199	−0.356		1437.4

762	TCTTCCAATTATGTTGACAG	906	57.14	−0.10	−0.474	−0.199	−0.369		1518.3

763	CTTCCAATTATGTTGACAGG	907	58.36	−0.10	−0.295	−0.199	−0.259		1560.3

764	TTCCAATTATGTTGACAGGT	908	59.43	−0.10	−0.138	−0.199	−0.161		1100.0

765	TCCAATTATGTTGACAGGTG	909	59.02	−0.10	−0.198	−0.199	−0.198		1096.4

766	CCAATTATGTTGACAGGTGT	910	60.68	−0.10	0.046	−0.199	−0.047		1103.4

767	CAATTATGTTGACAGGTGTA	911	56.24	0.30	−0.605	0.149	−0.319		738.1

768	AATTATGTTGACAGGTGTAG	912	55.09	1.10	−0.774	0.844	−0.159		596.7

769	ATTATGTTGACAGGTGTAGG	913	59.83	1.10	−0.079	0.844	0.272		548.1

770	TTATGTTGACAGGTGTAGGT	914	63.16	1.10	0.409	0.844	0.575		701.1

771	TATGTTGACAGGTGTAGGTC	915	64.38	−0.20	0.588	−0.286	0.256		724.7

772	ATGTTGACAGGTGTAGGTCC	916	69.08	−0.60	1.278	−0.634	0.551		1129.8

773	TGTTGACAGGTGTAGGTCCT	917	71.21	−0.60	1.591	−0.634	0.745		1214.0

774	GTTGACAGGTGTAGGTCCTA	918	70.75	−0.60	1.523	−0.634	0.703		1425.4

775	TTGACAGGTGTAGGTCCTAC	919	67.83	−0.60	1.095	−0.634	0.438		838.8

776	TGACAGGTGTAGGTCCTACT	920	69.52	−0.90	1.343	−0.895	0.493		1173.1

777	GACAGGTGTAGGTCCTACTA	921	69.06	−0.90	1.275	−0.895	0.450		1367.0

778	ACAGGTGTAGGTCCTACTAA	922	65.30	−0.90	0.723	−0.895	0.108		872.0

779	CAGGTGTAGGTCCTACTAAT	923	64.69	−0.90	0.634	−0.895	0.053		897.6

780	AGGTGTAGGTCCTACTAATA	924	62.84	−0.90	0.362	−0.895	−0.115		962.2

781	GGTGTAGGTCCTACTAATAC	925	63.19	−0.90	0.414	−0.895	−0.083		1382.6

782	GTGTAGGTCCTACTAATACT	926	62.53	−0.90	0.317	−0.895	−0.143		1132.9

783	TGTAGGTCCTACTAATACTG	927	59.27	−0.90	−0.160	−0.895	−0.439		1180.7

784	GTAGGTCCTACTAATACTGT	928	62.53	−0.50	0.317	−0.547	−0.011		1932.9

785	TAGGTCCTACTAATACTGTA	929	58.77	0.70	−0.234	0.496	0.043		1634.4

786	AGGTCCTACTAATACTGTAC	930	59.91	0.50	−0.067	0.323	0.081		2488.1

787	GGTCCTACTAATACTGTACC	931	63.54	0.50	0.466	0.323	0.411		3560.9

788	GTCCTACTAATACTGTACCT	932	62.91	0.50	0.373	0.323	0.354		3850.1

789	TCCTACTAATACTGTACCTA	933	59.31	0.50	−0.155	0.323	0.026		1879.0

790	CCTACTAATACTGTACCTAT	934	57.99	0.50	−0.348	0.323	−0.093		1920.4

791	CTACTAATACTGTACCTATA	935	53.68	0.50	−0.981	0.323	−0.486		1131.2

792	TACTAATACTGTACCTATAG	936	51.92	0.70	−1.240	0.496	−0.580		756.5

793	ACTAATACTGTACCTATAGC	937	56.45	1.20	−0.574	0.931	−0.002		1881.3

794	CTAATACTGTACCTATAGCT	938	57.85	1.20	−0.369	0.931	0.125		2033.6

795	TAATACTGTACCTATAGCTT	939	56.25	1.20	−0.604	0.931	−0.021		1853.9

796	AATACTGTACCTATAGCTTT	940	57.14	1.20	−0.473	0.931	0.060		2462.6

797	ATACTGTACCTATAGCTTTA	941	58.55	1.20	−0.266	0.931	0.189		2436.8

798	TACTGTACCTATAGCTTTAT	942	58.55	1.20	−0.266	0.931	0.189		1865.2

799	ACTGTACCTATAGCTTTATG	943	59.06	1.20	−0.192	0.931	0.235		1682.1

800	CTGTACCTATAGCTTTATGT	944	61.64	1.30	0.187	1.018	0.503		1551.3

801	TGTACCTATAGCTTTATGTC	945	61.08	1.10	0.105	0.844	0.386		1600.1

802	GTACCTATAGCTTTATGTCC	946	65.16	1.10	0.703	0.844	0.757		4094.6

803	TACCTATAGCTTTATGTCCA	947	63.16	1.10	0.409	0.844	0.575		2794.2

804	ACCTATAGCTTTATGTCCAC	948	64.30	1.30	0.577	1.018	0.745		4754.9

805	CCTATAGCTTTATGTCCACA	949	64.94	1.30	0.671	1.018	0.803		4185.4

806	CTATAGCTTTATGTCCACAG	950	61.34	1.10	0.143	0.844	0.409		3284.3

807	TATAGCTTTATGTCCACAGA	951	60.70	1.10	0.048	0.844	0.351		2819.7

808	ATAGCTTTATGTCCACAGAT	952	61.27	0.60	0.132	0.410	0.238		3545.1

809	TAGCTTTATGTCCACAGATT	953	61.63	0.60	0.186	0.410	0.271		4232.6

810	AGCTTTATGTCCACAGATTT	954	62.57	0.60	0.324	0.410	0.356		5252.8

811	GCTTTATGTCCACAGATTTC	955	63.85	0.60	0.511	0.410	0.472		6823.9

812	CTTTATGTCCACAGATTTCT	956	61.56	0.60	0.176	0.410	0.265		4829.8

813	TTTATGTCCACAGATTTCTA	957	58.97	0.60	−0.205	0.410	0.029		4333.7

814	TTATGTCCACAGATTTCTAT	958	58.62	0.60	−0.257	0.410	−0.004		3801.0

815	TATGTCCACAGATTTCTATG	959	58.20	0.60	−0.318	0.410	−0.041		3528.2

816	ATGTCCACAGATTTCTATGA	960	60.12	0.60	−0.036	0.410	0.134		2080.0

817	TGTCCACAGATTTCTATGAG	961	60.34	0.60	−0.004	0.410	0.153		913.8

818	GTCCACAGATTTCTATGAGT	962	63.68	0.60	0.486	0.410	0.457		1228.3

819	TCCACAGATTTCTATGAGTA	963	59.83	0.80	−0.078	0.583	0.173		238.1

820	CCACAGATTTCTATGAGTAT	964	58.43	1.10	−0.285	0.844	0.144		219.4

821	CACAGATTTCTATGAGTATC	965	55.78	0.90	−0.673	0.670	−0.162		138.6

822	ACAGATTTCTATGAGTATCT	966	56.48	−0.10	−0.571	−0.199	−0.430		112.7

823	CAGATTTCTATGAGTATCTG	967	55.85	−1.30	−0.663	−1.243	−0.883		133.8

824	AGATTTCTATGAGTATCTGA	968	55.87	−0.10	−0.659	−0.199	−0.485		296.8

825	GATTTCTATGAGTATCTGAT	969	55.69	0.60	−0.686	0.410	−0.270		279.7

826	ATTTCTATGAGTATCTGATC	970	55.67	0.80	−0.689	0.583	−0.206		484.4

827	TTTCTATGAGTATCTGATCA	971	57.06	0.20	−0.485	0.062	−0.277		502.0

828	TTCTATGAGTATCTGATCAT	972	56.70	−0.50	−0.538	−0.547	−0.541		637.3

829	TCTATGAGTATCTGATCATA	973	55.75	−1.10	−0.678	−1.069	−0.826		489.0

830	CTATGAGTATCTGATCATAC	974	54.95	−1.30	−0.794	−1.243	−0.965		808.7

831	TATGAGTATCTGATCATACT	975	54.95	−1.10	−0.794	−1.069	−0.899	−0.783	903.2

832	ATGAGTATCTGATCATACTG	976	55.49	−1.20	−0.715	−1.156	−0.883		1709.3

833	TGAGTATCTGATCATACTGT	977	58.64	−1.20	−0.254	−1.156	−0.597		2103.9

834	GAGTATCTGATCATACTGTC	978	60.20	−1.20	−0.025	−1.156	−0.455		3973.4

835	AGTATCTGATCATACTGTCT	979	60.88	−1.00	0.076	−0.982	−0.326		6462.3

836	GTATCTGATCATACTGTCTT	980	61.03	−0.30	0.097	−0.373	−0.081		9749.0

837	TATCTGATCATACTGTCTTA	981	57.16	0.90	−0.470	0.670	−0.037		7817.2

838	ATCTGATCATACTGTCTTAC	982	58.34	0.90	−0.298	0.670	0.070		9683.1

839	TCTGATCATACTGTCTTACT	983	60.42	0.90	0.008	0.670	0.259		8089.0

840	CTGATCATACTGTCTTACTT	984	59.32	0.90	−0.154	0.670	0.159		8696.8

841	TGATCATACTGTCTTACTTT	985	57.63	0.90	−0.401	0.670	0.006		6880.5

842	GATCATACTGTCTTACTTTG	986	57.63	0.90	−0.401	0.670	0.006		7033.7

843	ATCATACTGTCTTACTTTGA	987	57.63	0.90	−0.401	0.670	0.006		5406.5

844	TCATACTGTCTTACTTTGAT	988	57.63	0.70	−0.401	0.496	−0.060		4239.4

845	CATACTGTCTTACTTTGATA	989	55.68	0.70	−0.688	0.496	−0.238		3727.4

846	ATACTGTCTTACTTTGATAA	990	52.44	0.70	−1.163	0.496	−0.533		2665.5

847	TACTGTCTTACTTTGATAAA	991	50.65	0.70	−1.426	0.496	−0.696		1817.8

848	ACTGTCTTACTTTGATAAAA	992	49.49	−0.30	−1.595	−0.373	−1.131	−0.809	1335.9

849	CTGTCTTACTTTGATAAAAC	993	49.49	−0.50	−1.595	−0.547	−1.197	−0.916	1526.2

850	TGTCTTACTTTGATAAAACC	994	51.45	−0.50	−1.309	−0.547	−1.019	−0.949	822.7

851	GTCTTACTTTGATAAAACCT	995	53.32	−0.50	−1.034	−0.547	−0.849	−0.966	1227.4

852	TCTTACTTTGATAAAACCTC	996	51.75	−0.50	−1.264	−0.547	−0.991	−0.946	503.0

853	CTTACTTTGATAAAACCTCC	997	54.28	−0.50	−0.894	−0.547	−0.762	−0.910	1174.3

854	TTACTTTGATAAAACCTCCA	998	53.70	−0.50	−0.978	−0.547	−0.814	−0.901	885.5

855	TACTTTGATAAAACCTCCAA	999	51.79	−0.50	−1.259	−0.547	−0.988	−0.916	650.6

856	ACTTTGATAAAACCTCCAAT	1000	52.29	−0.50	−1.185	−0.547	−0.943	−0.826	615.4

857	CTTTGATAAAACCTCCAATT	1001	52.11	−0.50	−1.212	−0.547	−0.959		563.4

858	TTTGATAAAACCTCCAATTC	1002	51.46	−0.30	−1.307	−0.373	−0.952		420.9

859	TTGATAAAACCTCCAATTCC	1003	54.68	0.60	−0.834	0.410	−0.362		536.6

860	TGATAAAACCTCCAATTCCC	1004	57.79	0.60	−0.378	0.410	−0.079		1417.8

861	GATAAAACCTCCAATTCCCC	1005	61.15	1.00	0.114	0.757	0.359		4351.2

862	ATAAAACCTCCAATTCCCCC	1006	63.24	1.90	0.421	1.540	0.846		7738.7

863	TAAAACCTCCAATTCCCCCT	1007	64.88	1.90	0.663	1.540	0.996		11136.0

864	AAAACCTCCAATTCCCCCTA	1008	64.88	1.90	0.663	1.540	0.996	1.074	14811.0

865	AAACCTCCAATTCCCCCTAT	1009	66.73	1.90	0.933	1.540	1.164	1.261	15751.0

866	AACCTCCAATTCCCCCTATC	1010	70.07	1.80	1.424	1.453	1.435	1.330	19661.0

867	ACCTCCAATTCCCCCTATCA	1011	73.21	1.80	1.883	1.453	1.720	1.335	20301.0

868	CCTCCAATTCCCCCTATCAT	1012	72.64	1.80	1.801	1.453	1.669	1.327	19376.0

869	CTCCAATTCCCCCTATCATT	1013	69.66	1.60	1.364	1.279	1.332	1.254	17642.0

870	TCCAATTCCCCCTATCATTT	1014	68.21	1.10	1.150	0.844	1.034	1.093	13751.0

871	CCAATTCCCCCTATCATTTT	1015	67.12	1.10	0.991	0.844	0.935	0.931	12669.0

872	CAATTCCCCCTATCATTTTT	1016	64.02	1.10	0.536	0.844	0.653		9255.9

873	AATTCCCCCTATCATTTTTG	1017	62.80	0.40	0.357	0.236	0.311		8929.1

874	ATTCCCCCTATCATTTTTGG	1018	67.28	0.00	1.014	−0.112	0.586		6148.2

875	TTCCCCCTATCATTTTTGGT	1019	70.46	0.00	1.480	−0.112	0.875		5468.0

876	TCCCCCTATCATTTTTGGTT	1020	70.46	0.00	1.480	−0.112	0.875		5803.7

877	CCCCCTATCATTTTTGGTTT	1021	69.27	0.00	1.307	−0.112	0.768		5192.0

878	CCCCTATCATTTTTGGTTTC	1022	67.18	0.00	1.000	−0.112	0.577		3557.4

879	CCCTATCATTTTTGGTTTCC	1023	67.18	0.00	1.000	−0.112	0.577		5274.3

880	CCTATCATTTTTGGTTTCCA	1024	64.63	0.00	0.625	−0.112	0.345		3787.9

881	CTATCATTTTTGGTTTCCAT	1025	60.77	−0.50	0.059	−0.547	−0.171		2726.8

882	TATCATTTTTGGTTTCCATC	1026	60.20	−0.50	−0.025	−0.547	−0.223		3249.9

883	ATCATTTTTGGTTTCCATCT	1027	62.83	−0.50	0.361	−0.547	0.016		5548.9

884	TCATTTTTGGTTTCCATCTT	1028	63.21	−0.50	0.416	−0.547	0.050		5290.0

885	CATTTTTGGTTTCCATCTTC	1029	63.21	−0.50	0.416	−0.547	0.050		7451.0

886	ATTTTTGGTTTCCATCTTCC	1030	65.88	−0.50	0.809	−0.547	0.293		11578.0

887	TTTTTGGTTTCCATCTTCCT	1031	67.93	−0.50	1.109	−0.547	0.480		13722.0

888	TTTTGGTTTCCATCTTCCTG	1032	67.42	−0.50	1.035	−0.547	0.434		15064.0

889	TTTGGTTTCCATCTTCCTGG	1033	69.71	−0.90	1.370	−0.895	0.509		10869.0

890	TTGGTTTCCATCTTCCTGGC	1034	73.74	−1.30	1.962	−1.243	0.744		16035.0

891	TGGTTTCCATCTTCCTGGCA	1035	74.48	−1.30	2.071	−1.243	0.812		16304.0

892	GGTTTCCATCTTCCTGGCAA	1036	72.21	−1.30	1.737	−1.243	0.605		14885.0

893	GTTTCCATCTTCCTGGCAAA	1037	67.37	−1.30	1.027	−1.243	0.165		11910.0

894	TTTCCATCTTCCTGGCAAAC	1038	64.82	−1.30	0.653	−1.243	−0.067		11929.0

895	TTCCATCTTCCTGGCAAACT	1039	66.34	−1.30	0.877	−1.243	0.071		11517.0

896	TCCATCTTCCTGGCAAACTC	1040	67.47	−1.30	1.042	−1.243	0.174		11822.0

897	CCATCTTCCTGGCAAACTCA	1041	67.12	−0.90	0.991	−0.895	0.274		11710.0

898	CATCTTCCTGGCAAACTCAT	1042	63.55	0.90	0.466	0.670	0.544		7635.3

899	ATCTTCCTGGCAAACTCATT	1043	62.71	1.00	0.343	0.757	0.501		8378.2

900	TCTTCCTGGCAAACTCATTT	1044	63.06	0.90	0.395	0.670	0.500		6321.4

901	CTTCCTGGCAAACTCATTTC	1045	63.06	0.70	0.395	0.496	0.434		7659.0

902	TTCCTGGCAAACTCATTTCT	1046	63.06	0.70	0.395	0.496	0.434		11621.0

903	TCCTGGCAAACTCATTTCTT	1047	63.06	0.70	0.395	0.496	0.434		3389.0

904	CCTGGCAAACTCATTTCTTC	1048	63.06	0.70	0.395	0.496	0.434		3870.6

905	CTGGCAAACTCATTTCTTCT	1049	61.24	0.70	0.127	0.496	0.268		1992.7

906	TGGCAAACTCATTTCTTCTA	1050	58.74	0.70	−0.239	0.496	0.040		698.3

907	GGCAAACTCATTTCTTCTAA	1051	56.86	0.70	−0.514	0.496	−0.130		718.3

908	GCAAACTCATTTCTTCTAAT	1052	54.36	0.70	−0.882	0.496	−0.358		372.3

909	CAAACTCATTTCTTCTAATA	1053	49.93	0.60	−1.530	0.410	−0.793		180.6

910	AAACTCATTTCTTCTAATAC	1054	49.11	0.60	−1.651	0.410	−0.868		430.0

911	AACTCATTTCTTCTAATACT	1055	52.79	0.60	−1.111	0.410	−0.533		904.3

912	ACTCATTTCTTCTAATACTG	1056	54.63	0.60	−0.842	0.410	−0.366		1663.5

913	CTCATTTCTTCTAATACTGT	1057	57.14	0.60	−0.474	0.410	−0.138		2694.2

914	TCATTTCTTCTAATACTGTA	1058	54.51	0.60	−0.859	0.410	−0.377		3222.9

915	CATTTCTTCTAATACTGTAT	1059	53.21	0.60	−1.049	0.410	−0.495		3142.8

916	ATTTCTTCTAATACTGTATC	1060	53.13	0.80	−1.061	0.583	−0.436		5867.0

917	TTTCTTCTAATACTGTATCA	1061	54.51	1.20	−0.859	0.931	−0.179		6641.4

918	TCTTCTAATACTGTATCAT	1062	54.17	1.30	−0.908	1.018	−0.176		7151.9

919	TCTTCTAATACTGTATCATC	1063	55.17	1.30	−0.762	1.018	−0.086		8134.9

920	CTTCTAATACTGTATCATCT	1064	55.86	1.30	−0.661	1.018	−0.023		8551.4

921	TTCTAATACTGTATCATCTG	1065	53.80	1.30	−0.964	1.018	−0.211		5741.7

922	TCTAATACTGTATCATCTGC	1066	57.65	1.30	−0.398	1.018	0.140		8575.9

923	CTAATACTGTATCATCTGCT	1067	58.28	1.30	−0.307	1.018	0.197		8980.3

924	TAATACTGTATCATCTGCTC	1068	57.65	1.30	−0.398	1.018	0.140		10762.0

925	AATACTGTATCATCTGCTCC	1069	62.19	1.30	0.268	1.018	0.553		17037.0

926	ATACTGTATCATCTGCTCCT	1070	66.43	1.30	0.889	1.018	0.938		20970.0

927	TACTGTATCATCTGCTCCTG	1071	66.32	1.30	0.874	1.018	0.929		23084.0

928	ACTGTATCATCTGCTCCTGT	1072	70.36	0.60	1.466	0.410	1.065	0.875	24474.0

929	CTGTATCATCTGCTCCTGTA	1073	69.13	0.60	1.286	0.410	0.953	0.910	22217.0

930	TGTATCATCTGCTCCTGTAT	1074	67.04	0.60	0.979	0.410	0.763	0.890	19829.0

931	GTATCATCTGCTCCTGTATC	1075	68.85	0.60	1.244	0.410	0.927	0.842	23548.0

932	TATCATCTGCTCCTGTATCT	1076	67.44	0.60	1.037	0.410	0.799		21759.0

933	ATCATCTGCTCCTGTATCTA	1077	67.44	0.60	1.037	0.410	0.799		22711.0

934	TCATCTGCTCCTGTATCTAA	1078	65.13	0.60	0.699	0.410	0.589		18134.0

935	CATCTGCTCCTGTATCTAAT	1079	63.60	1.00	0.475	0.757	0.582		17772.0

936	ATCTGCTCCTGTATCTAATA	1080	61.77	1.60	0.207	1.279	0.614		17134.0

937	TCTGCTCCTGTATCTAATAG	1081	62.01	1.60	0.241	1.279	0.635		10969.0

938	CTGCTCCTGTATCTAATAGA	1082	61.90	0.50	0.225	0.323	0.262		9556.3

939	TGCTCCTGTATCTAATAGAG	1083	60.12	0.30	−0.036	0.149	0.034		3739.9

940	GCTCCTGTATCTAATAGAGC	1084	64.50	−1.00	0.607	−0.982	0.003		4088.3

941	CTCCTGTATCTAATAGAGCT	1085	62.21	0.30	0.271	0.149	0.224		2263.0

942	TCCTGTATCTAATAGAGCTT	1086	60.56	0.30	0.028	0.149	0.074		1018.0

943	CCTGTATCTAATAGAGCTTC	1087	60.56	0.30	0.028	0.149	0.074		1319.1

944	CTGTATCTAATAGAGCTTCC	1088	60.56	0.30	0.028	0.149	0.074		2347.8

945	TGTATCTAATAGAGCTTCCT	1089	60.56	0.30	0.028	0.149	0.074		1871.6

946	GTATCTAATAGAGCTTCCTT	1090	61.00	0.30	0.092	0.149	0.114		3469.1

947	TATCTAATAGAGCTTCCTTT	1091	58.20	0.30	−0.318	0.149	−0.141		1114.6

948	ATCTAATAGAGCTTCCTTTA	1092	58.20	0.30	−0.318	0.149	−0.141		1358.4

949	TCTAATAGAGCTTCCTTTAG	1093	58.39	0.30	−0.289	0.149	−0.123		665.4

950	CTAATAGAGCTTCCTTTAGT	1094	60.12	0.00	−0.036	−0.112	−0.065		807.4

951	TAATAGAGCTTCCTTTAGTT	1095	58.46	0.30	−0.280	0.149	−0.117		608.7

952	AATAGAGCTTCCTTTAGTTG	1096	58.97	0.30	−0.205	0.149	−0.070		623.8

953	ATAGAGCTTCCTTTAGTTGC	1097	65.53	0.30	0.758	0.149	0.526		674.5

954	TAGAGCTTCCTTTAGTTGCC	1098	69.50	0.30	1.340	0.149	0.887	0.841	814.3

955	AGAGCTTCCTTTAGTTGCCC	1099	73.89	0.30	1.983	0.149	1.286	1.157	1183.8

956	GAGCTTCCTTTAGTTGCCCC	1100	77.20	0.30	2.470	0.149	1.588	1.454	2219.4

957	AGCTTCCTTTAGTTGCCCCC	1101	79.38	0.30	2.789	0.149	1.785	1.650	4642.2

958	GCTTCCTTTAGTTGCCCCCC	1102	82.41	0.40	3.234	0.236	2.095	1.765	8804.8

959	CTTCCTTTAGTTGCCCCCCT	1103	80.06	0.80	2.889	0.583	2.013	1.823	11331.0

960	TTCCTTTAGTTGCCCCCCTA	1104	77.67	1.10	2.539	0.844	1.895	1.818	12976.0

961	TCCTTTAGTTGCCCCCCTAT	1105	77.27	0.60	2.480	0.410	1.693	1.765	12369.0

962	CCTTTAGTTGCCCCCCTATC	1106	77.27	0.60	2.480	0.410	1.693	1.669	15090.0

963	CTTTAGTTGCCCCCCTATCT	1107	75.74	0.60	2.255	0.410	1.554	1.581	16130.0

964	TTTAGTTGCCCCCCTATCTT	1108	74.23	0.60	2.033	0.410	1.416	1.545	15304.0

965	TTAGTTGCCCCCCTATCTTT	1109	74.23	0.60	2.033	0.410	1.416	1.539	14829.0

966	TAGTTGCCCCCCTATCTTTA	1110	73.31	0.80	1.899	0.583	1.399	1.490	15309.0

967	AGTTGCCCCCCTATCTTTAT	1111	73.83	1.40	1.976	1.105	1.645	1.498	15205.0

968	GTTGCCCCCCTATCTTTATT	1112	73.91	1.40	1.986	1.105	1.652	1.524	14192.0

969	TTGCCCCCCTATCTTTATTG	1113	70.59	1.40	1.500	1.105	1.350	1.515	8699.5

970	TGCCCCCCTATCTTTATTGT	1114	73.39	1.40	1.911	1.105	1.605	1.461	7786.6

971	GCCCCCCTATCTTTATTGTG	1115	73.39	1.40	1.911	1.105	1.605	1.328	6709.1

972	CCCCCCTATCTTTATTGTGA	1116	70.61	1.40	1.502	1.105	1.351	1.165	6198.4

973	CCCCCTATCTTTATTGTGAC	1117	67.66	1.20	1.070	0.931	1.017	0.999	4910.2

974	CCCCTATCTTTATTGTGACG	1118	64.37	1.20	0.587	0.931	0.718		850.0

975	CCCTATCTTTATTGTGACGA	1119	62.05	1.20	0.248	0.931	0.507		404.9

976	CCTATCTTTATTGTGACGAG	1120	58.56	1.20	−0.265	0.931	0.190		166.6

977	CTATCTTTATTGTGACGAGG	1121	57.28	1.20	−0.452	0.931	0.073		126.9

978	TATCTTTATTGTGACGAGGG	1122	57.91	1.20	−0.361	0.931	0.130		92.6

979	ATCTTTATTGTGACGAGGGG	1123	61.03	1.20	0.097	0.931	0.414		97.9

980	TCTTTATTGTGACGAGGGGT	1124	64.18	0.90	0.559	0.670	0.601		122.3

981	CTTTATTGTGACGAGGGGTC	1125	64.18	−0.80	0.559	−0.808	0.039		267.0

982	TTTATTGTGACGAGGGGTCG	1126	62.63	−1.20	0.332	−1.156	−0.233		396.0

983	TTATTGTGACGAGGGGTCGT	1127	65.37	−2.30	0.734	−2.112	−0.348		446.0

984	TATTGTGACGAGGGGTCGTT	1128	65.37	−2.80	0.734	−2.547	−0.513		661.9

985	ATTGTGACGAGGGGTCGTTG	1129	65.82	−2.80	0.800	−2.547	−0.472		864.5

986	TTGTGACGAGGGGTCGTTGC	1130	70.01	−2.80	1.414	−2.547	−0.091		1465.7

957	TGTGACGAGGGGTCGTTGCC	1131	73.21	−2.80	1.884	−2.547	0.200		2836.9

988	GTGACGAGGGGTCGTTGCCA	1132	74.44	−2.80	2.065	−2.547	0.312		3589.7

989	TGACGAGGGGTCGTTGCCAA	1133	69.05	−2.80	1.274	−2.547	−0.178		2100.4

990	GACGAGGGGTCGTTGCCAAA	1134	67.10	−2.80	0.988	−2.547	−0.355		1948.7

991	ACGAGGGGTCGTTGCCAAAG	1135	66.13	−2.60	0.845	−2.373	−0.378		1384.3

992	CGAGGGGTCGTTGCCAAAGA	1136	66.81	−1.40	0.945	−1.330	0.081		1192.0

993	GAGGGGTCGTTGCCAAAGAG	1137	66.84	0.20	0.950	0.062	0.612		1221.0

994	AGGGGTCGTTGCCAAAGAGT	1138	68.70	0.20	1.223	0.062	0.782		953.2

995	GGGGTCGTTGCCAAAGAGTG	1139	68.32	0.20	1.167	0.062	0.747		988.6

996	GGGTCGTTGCCAAAGAGTGA	1140	67.11	0.20	0.989	0.062	0.636		937.8

997	GGTCGTTGCCAAAGAGTGAT	1141	64.59	0.50	0.620	0.323	0.507		852.1

998	GTCGTTGCCAAAGAGTGATC	1142	63.51	0.00	0.461	−0.112	0.243		1189.4

999	TCGTTGCCAAAGAGTGATCT	1143	62.35	−1.00	0.291	−0.982	−0.192		1501.7

1000	CGTTGCCAAAGAGTGATCTG	1144	60.92	−1.20	0.081	−1.156	−0.389		1360.9

1001	GTTGCCAAAGAGTGATCTGA	1145	61.71	−1.20	0.198	−1.156	−0.317		1112.9

1002	TTGCCAAAGAGTGATCTGAG	1146	58.90	−1.20	−0.215	−1.156	−0.572		468.3

1003	TGCCAAAGAGTGATCTGAGG	1147	61.08	−1.20	0.104	−1.156	−0.375		400.1

1004	GCCAAAGAGTGATCTGAGGG	1148	63.68	−1.50	0.485	−1.417	−0.237		401.6

1005	CCAAAGAGTGATCTGAGGGA	1149	60.94	−1.20	0.084	−1.156	−0.387		199.9

1006	CAAAGAGTGATCTGAGGGAA	1150	55.32	−1.20	−0.741	−1.156	−0.899		202.1

1007	AAAGAGTGATCTGAGGGAAG	1151	54.21	−1.20	−0.903	−1.156	−0.999		258.7

1008	AAGAGTGATCTGAGGGAAGT	1152	59.12	−1.20	−0.183	−1.156	−0.552		274.7

1009	AGAGTGATCTGAGGGAAGTT	1153	61.60	−1.00	0.181	−0.982	−0.261		297.2

1010	GAGTGATCTGAGGGAAGTTA	1154	60.78	−0.30	0.061	−0.373	−0.104		250.6

1011	AGTGATCTGAGGGAAGTTAA	1155	57.35	0.60	−0.443	0.410	−0.119		231.3

1012	GTGATCTGAGGGAAGTTAAA	1156	55.25	0.60	−0.751	0.410	−0.310		214.5

1013	TGATCTGAGGGAAGTTAAAG	1157	52.55	0.60	−1.147	0.410	−0.556		102.3

1014	GATCTGAGGGAAGTTAAAGG	1158	55.09	0.60	−0.774	0.410	−0.324		102.3

1015	ATCTGAGGGAAGTTAAAGGA	1159	55.09	0.60	−0.774	0.410	−0.324		49.4

1016	TCTGAGGGAAGTTAAAGGAT	1160	55.09	0.60	−0.774	0.410	−0.324		104.3

1017	CTGAGGGAAGTTAAAGGATA	1161	53.32	1.00	−1.034	0.757	−0.353		46.3

1018	TGAGGGAAGTTAAAGGATAC	1162	51.95	1.30	−1.235	1.018	−0.378		50.9

1019	GAGGGAAGTTAAAGGATACA	1163	53.26	0.90	−1.043	0.670	−0.392		58.2

1020	AGGGAAGTTAAAGGATACAG	1164	52.14	0.90	−1.207	0.670	−0.494		50.5

1021	GGGAAGTTAAAGGATACAGT	1165	54.81	0.90	−0.815	0.670	−0.251		53.1

Example 3

Synopsis: The method of the present invention is particularly useful as a guide to the iterative refinement of probes. One of the specific predictions made for rabbit β-globin in Example 1 is used to provide an example of such a refinement.
Materials and Methods: The contig spanning positions 5-11 of a portion of the rabbit β-globin gene (Example 1, Table 3) was analyzed, using the experimentally measured data to simulate the results of successive experimental measurements. The iterative refinement was performed using a rule-based algorithm, outlined below. This algorithm is used by way of example only; other algorithms for efficiently finding local maxima are well known to the art and could be employed to perform this task.
Given experimental data for probes from the 1^stquartile, median and 3^rdquartile of a contig, as well as a user-set signal threshold for further consideration of a probe,

1) If all 3 measurements are below the user-specified signal threshold, discard the prediction.
2) If at least one of the measurements is above the user-specified threshold, determine which point yields the maximum signal.
- a) If the maximum point is the 1^stquartile probe, then make three new measurements for probes with the same spacing as that used in the preceding iteration, but displaced so that the third probe is identical to the original 1^stquartile probe. In other words, repeat the search with the same pattern and spacing, but displace the pattern in the direction of increasing signal found in the first experiment.
- b) If the maximum point is the 3^rdquartile probe, then make three new measurements for probes with the same spacing as that used in the preceding iteration, but displaced so that the first probe is identical to the original 3^rdquartile probe. In other words, repeat the search with the same pattern and spacing, but displace the pattern in the direction of increasing signal found in the first experiment.
- c) If the maximum point is the median probe, then repeat the experiment, keeping the median point the same, but shrinking the spacing between probes by a factor of 2.
3) Continue iteration until a maximum is found, or the user judges the signal level observed to be acceptable. Use the experimental value measured for the probe duplicated in successive iterations to tie together the successive data sets, via a simple normalization procedure, described below. Where appropriate, consider all of the data (i.e. all of the iterations) when deciding how to proceed, or whether the peak hybridization intensity has been found.

Results: Iterative refinement of the contig spanning positions 5-11 in Table 3 proceeds as follows:
Iteration 1: Probes are synthesized at positions 6, 8 and 10, yielding the experimental hybridization intensities 180, 220 and 310, respectively.
Iteration 2: Following rule 2b), probes are synthesized at positions 10, 12 and 14. Note that the redundant measurement at position 10 serves as a bridge between experiments, and allows comparison of the two sets by normalizing the intensities by multiplying the second iteration measurements by the ratio of the intensity observed for the probe at position 10 in the first iteration to the value observed in the second iteration. In the simplest case, the ratio is 1; in any case, the second iteration yields the normalized values 310, 390, 240 for probe positions 10, 12 and 14, respectively.
Iteration 3: By rule 2c), measurements are performed for probes at positions 11, 12 and 13; after normalization, these yield the normalized hybridization intensities 320, 390 and 410, respectively. Combination of these results with the results from iteration 2, probe position 14, yields the conclusion that the best probe for this intensity peak is the probe that starts at sequence position 13.
The overall result is that iterative improvement converges in three iterations, and requires the synthesis of seven test probes, one of which is the local optimal probe. In addition, the first and second iterations yield probes that exhibit 75% and 95% of the local maximum hybridization intensities, respectively. In many applications, either of these probes would be considered acceptable.
The above examples 1 and 2 demonstrate that two different implementations of the method of the present invention are capable of efficiently predicting regions of high hybridization efficiency in a variety of polynucleotide targets. Many of the predictions yield acceptable probe sequences on the first design iteration, and all would yield optimized probe sets after 24 rounds of iterative refinement, as demonstrated in Example 3. The performance demonstrated in these examples greatly exceeds the performance of current methods. Finally, the examples demonstrate that the predictions can be performed by a software application that has been implemented and installed on a Pentium®-based computer workstation.
All publications and patent applications cited in this specification are herein incorporated by reference as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference.
Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

Claims

1. A method for producing an array comprising:

receiving one or more target polynucleotide sequences;

determining an evaluated probe set for each of said one or more target polynucleotide sequences;

outputting data relating to the oligonucleotide sequences of said evaulated probe set for each of said one or more target polynucleotide sequences; and

fabricating an array from said outputted data.

2. A method for producing an array comprising:

eceiving one or more target polynucleotide sequences;

outputting data relating to the oligonucleotide sequences of said evaluated probe set for each of said one or more target polynucleotide sequences to a user for examination and modification, as desired to produce a final output data set; and

fabricating an array from said final output data set.

3. The method according to claim 2, wherein said array is useful in diagnostic applications.

4. The method according to claim 2, wherein said method comprises coummunicating said outputted data with another computer.

5. The method according to claim 2, wherein each of said evaulated probe sets is made up of probes evaulated for at least one parameter predictive of hybridization.

6. The method according to claim 2, wherein said array comprises nucleic acids synthesized on a support.

7. A system for fabricating an array, said system comprising:

an input device for receiving one or more target polynucleotide sequences;

a means for determining an evaluated probe set for each of said one or more target polynucleotide sequences;

a communication means for outputting data relating to the oligonucleotide sequences of said evaluated probe set for each of said one or more target polynucleotide sequences to a user for examination and modification, as desired to produce a final output data set; and

means for fabricating an array from said final output data set.

8. The system according to claim 7, wherein said communication means communicates with another computer.

9. The system according to claim 7, wherein said means for determining evaluated probe sets evaluates probes for at least one parameter predictive of hybridization.

10. The system according to claim 7, wherein said array comprises nucleic acids synthesized on a support.

11. A method for providing custom probe arrays, comprising the acts of:

receiving a user selection of one or more probe set identifiers that each identify a plurality of potential probes;

determining verified probe sets of verified probes corresponding to the probe set identifiers;

generating a custom probe array design based, at least in part, upon the verified probe sets; and

providing to the user one or more probe arrays based on the probe array design.

12. A method for providing custom probe arrays, comprising the acts of:

receiving a user selection of one or more probe set identifiers that identify one or more potential probes;

generating a custom probe array design based, at least in part, upon the verified probe sets;

enabling for display to the user a representation of one or more aspects of the custom probe array design via one or more graphical user interfaces enabled to receive a user selection specifying acceptance, modification, or rejection of the custom probe array design; and

providing to the user one or more probe arrays based on the probe array design and responsive to the user specification of acceptance or modification.

13. The method of claim 12, wherein:one or more of the probe arrays is constructed and arranged to diagnose a disease or medical condition.

14. The method of claim 12, wherein:the user selection is received over the Internet.

15. The method of claim 12, wherein:the probe set identifiers comprise sequence information.

16. The method of claim 12, wherein:the verified probe sets are determined based, at least in part, on any one or any combination of frequency, length, or position of probe sequence repeats; probe sequence length, thermodynamic properties, energetic parameters, or uniqueness; or one or more characteristics of target molecules specified by the user for use with the probe array.

17. The method of claim 12, wherein:the graphical user interface is provided over a network.

18. The method of claim 5, wherein:the probe arrays include synthesized or spotted probe arrays.

19. A system for providing custom probe arrays, comprising:

an input manager constructed and arranged to receive a user selection of one or more probe set identifiers that identify one or more potential probes;

a gene or EST verifier constructed and arranged to determine one or more verified probe sets of verified probes corresponding to the probe set identifiers;

a probe array generator constructed and arranged to generate a custom probe array design based, at least in part, upon the verified probe sets; and

a user data processor constructed and arranged to enable for display a representation of one or more aspects of the custom probe array design via one or more graphical user interfaces that are further enabled to receive a user selection specifying acceptance, modification, or rejection of the custom probe array design, and further is constructed and arranged to provide to the user one or more probe arrays based on a user selection specifying acceptance or modification of the probe array design.

20. The system of claim 19, wherein:the user selection is received over the Internet.

21. The system of claim 19, wherein:the probe set identifiers comprise sequence information.

22. The system of claim 19, wherein:the verified probe sets are determined based, at least in part, on any one or any combination of frequency, length, or position of probe sequence repeats; probe sequence length, thermodynamic properties, energetic parameters, or uniqueness; or one or more characteristics of target molecules specified by the user for use with the probe array.

23. The system of claim 19, wherein:the graphical user interface is provided over a network.

24. The system of claim 19, wherein:the probe arrays include synthesized or spotted probe arrays.