CN1890370A - RNAi potency prediction method - Google Patents
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Abstract
Methods for making an algorithm for the prediction of the RNAi potency of a RNAi reagent are provided. Also provided are methods for predicting the RNAi potency of a RNAi reagent and methods for inhibiting the expression of a given target gene using such an algorithm.
Description
Background of invention
The present invention relates generally to that RNA disturbs (RNAi) field and relates in particular to the RNAi compositions and methods that end user's artificial neural networks training algorithm obtains high RNAi potential.
Invention field
Since having proved synthetic siRNA (siRNA), Elbashir etc. disturb the ability of special mRNA downward modulation in (RNAi) machine-processed mediate mammalian cell [for example to see Elbashir etc., Nature, the 411st volume, 494-498 page or leaf (2001) by RNA; With Caplen etc., PNAS, the 98th volume, the 17th phase, 9742-9747 page or leaf (2001)], this technology day by day as a kind of research tool to study gene function by the special downward modulation of research specific gene institute inductive phenotype.Particularly, owing to the gene silencing that carries out in the mode of siRNA or RNA interference type reagent is tested the partial nucleotide sequence that only needs to know target gene, can estimate and screen and for example to treat the relevant phenotype of target spot discovery in case the genome of known particular organisms just can design at the RNAi experiment of each gene.The method of this genome range has been specified the standard that is used for the design of RNAi reagent.Really, avoid the specificity parameter of the non-target silence particularly important that becomes.Equally, the feature of single siRNA potential no longer can be expected.Therefore, need effective prediction algorithm so that be applied to the reticent experiment of rna gene to carry out the phenotypic screen of genome range.
Show that similar to antisense, the accessibility of target plays an important role in siRNA potential.See Kretschmer-Kazemi etc., Nucleic Acids Res., the 31st volume, the 15th phase, 4417-4424 page or leaf (2003).Recently, another research [is seen Anastasia Khvorova, Cell, the 115th volume, 209-216 page or leaf (2003)] show, some sequence demands are arranged in exciting siRNA and miRNA potential, for example antisense strand 5 ' and in the 9-14 of antisense strand zone dsRNA have significantly low internal stability.These discoveries are to obtain by the mutual relationship between siRNA potential and the duplex internal heat kinetic stability is carried out statistical analysis.This research is based on 375 siRNA potential of selecting siRNA at random at 3 different targets.
Consider that RNAi mechanism is not characterized fully and many additional parameter can influence this fact of siRNA potential, it is useful obtaining bigger functional image data set so that understand sequence-active mutual relationship better.
Summary of the invention
In one aspect, the present invention relates to produce the method for the algorithm of the RNAi potential that is used to predict RNAi reagent, it comprises:
A) potential of a plurality of RNAi reagent downward modulation reporter gene protein readings (readout) is determined in experiment; With
B) use described potential data set training of human artificial neural networks.
In yet another aspect, the present invention relates to by the resulting algorithm of method of the present invention.
In yet another aspect, the present invention relates to be used to predict the method for the RNAi potential of RNAi reagent, it comprises:
A) provide a plurality of RNAi reagent sequences that comprise with given target gene complementary zone;
B) artificial neural network that will be trained according to the present invention is applied to described RNAi reagent sequence; With
C) select through predicting effective RNAi reagent sequence.
On the other hand, the present invention relates to suppress the method for given expression of target gene, it comprises:
A) provide a plurality of RNAi reagent sequences that comprise with given target gene complementary zone;
B) artificial neural network that will be trained according to the present invention is applied to described RNAi reagent sequence;
C) select through predicting effective RNAi reagent sequence;
D) synthesize at c) in selected RNAi reagent;
E) will express the cellular exposure of target gene in d) RNAi reagent; With
F) the active or measurement of measure R NAi reagent is by downward modulation target gene institute other phenotype of inductive.
The accompanying drawing summary
Fig. 1: normalization data collection example.79 siRNA and reporter gene fusion mRNA cotransfection (H1299,50nM was at 50 hours readings) with target YFP mRNA 3 '-UTR insertion sequence.The grey post is the siRNA of target 3 '-UTR, positive contrast of black post and negative control.Negative control set for arbitrarily to have 10% potential and positive control set for have 90% potential.The potential of each siRNA carries out normalization method according to these contrasts.
Fig. 2: the filtration of graphic extension garbled data.
Fig. 3: the comparison between the predictor of training set and the screening observed value.
Fig. 4: the comparison between the predictor of inspection set and the screening observed value.
Fig. 5: prediction-measurement dependency (about inspection set) is to the dependency of training set size.
Detailed Description Of The Invention
Whole patent applications, patent and the list of references quoted in the literary composition are quoted as a reference in this integral body.
Such as in the literary composition use, term " RNAi reagent " and " oligoribonucleotide " are used interchangeably And the meaning refers to the oligomerization of ribonucleic acid (RNA) or DNA (DNA) or its analogies Body or polymer. RNAi reagent can also comprise the ribonucleotide residue of modification. Suitable modification Known in the art. See for example Uhlmann, Current Opin.Drug Discovery Dev., The 3rd volume, the 2nd phase, 203-213 page or leaf (2000); And Uhlmann and Peyman, Chem. Rev., Washington, DC, the 90th volume, the 4th phase, 543-584 page or leaf (1990). Term RNA i Reagent comprises strand and double chain acid molecule. Double chain acid molecule can by two uncrosslinking chains form or Be made up of a such chain, a wherein said chain comprises two districts that can form duplex structure Two interregional spacer regions of territory and formation hairpin loop. In the RNAi situation, RNAi reagent is excellent Elect duplex structure as and comprise sequence with the target gene complementation, in any case the present invention is not restricted to two Chain structure and comprise the single stranded RNA i reagent that to induce RNAi. See Schwarz etc., Mol. Cell, the 10th volume, the 3rd phase, 537-548 page or leaf (2002).
Aspect first, the invention provides the method for the algorithm that produces the RNAi potential that is used for prediction RNAi reagent, it comprises:
A) potential of a plurality of RNAi reagent downward modulation reporter protein readings is determined in experiment; With
B) use described potential data set training of human artificial neural networks.
In one embodiment, generation comprises step for the method for the algorithm of the RNAi potential of prediction RNAi reagent:
A) experiment is determined to comprise with a plurality of RNAi reagent of the sequence of at least one target gene complementation RNAi potential;
B) the RNAi potential that experiment is determined in using a) produces the data set of described RNAi reagent potential, Although wherein described data set from different targets (reporter merges-mRNA) obtains, They have and can carry out normalized standard egg by the special positive control of reporter and negative control The white matter reading; With
C) use described reading training of human artificial neural networks.
In the context of the present invention, the term algorithm meaning refers to a prescription journey and one group of rule, they Can automatically be applied to data and can realize as computer-executable code.
" RNAi potential " or " potential " are that term and the meaning of this area refers to specific siRNA In case in raji cell assay Raji transfection its can reduce the relative ability of specified protein or mRNA. Generally The potential of siRNA and common table are determined by measuring said target mrna or protein expression level in ground Be shown the percentage of negative control. Therefore, the high potential meaning refers to that RNAi reagent can press down effectively The expression of system (namely reduce) target gene, and the expression that the low potential meaning refers to target gene is not suppressed or Only be subjected to very little inhibition. Compare with negative control, effectively the expression of RNAi reagent inhibition target gene is big In 50%, be preferably greater than 60%, greater than 70%, greater than 80%, most preferably greater than 90%.
RNAi reagent of the present invention is the RNAi reagent that is suitable for the RNAi experiment. Suitable RNAi All kinds RNAi reagent be known in the art. See Dykxhoorn etc., Nature Rev., the 4 volumes, 457-467 page or leaf (2003). This kind RNAi reagent comprises the sequence with the target gene complementation. In the context of the invention, refer to sequence and transcribe from the target gene dna sequence dna with the complementary meaning of target gene The RNA that obtains (comprising premessenger RNA, mRNA, cDNA) complementation. Term " target gene " meaning Think of is to comprise being expressed any of (namely be transcribed into and be RNA) in cell, tissue or organism Dna sequence dna. The sequence of expressing is not to translate into protein, and before it for example also comprises MRNA, adjusting RNA, rRNA etc. With the general length of sequence of target gene complementation be about 19-23 Individual nucleotides, but can also be longer. Preferably, complementary series length is less than 50 nucleotides, more Add and be preferably 15-35 nucleotides or 18-25 nucleotides. Complementary series is preferably with target gene Corresponding sequence 100% is identical, does not namely have mispairing between the corresponding sequence of complementary series and target gene. In some embodiments, if mispairing does not eliminate the RNAi activity of RNAi reagent, then complementary Sequence can comprise 1,2,3,4,5 or a plurality of mispairing. The RNAi that is used for RNAi Reagent be preferably double-stranded and can by two independently chain form, but can also be by forming hair clip A chain of ring forms. The double-stranded RNA zone of RNAi reagent can comprise mispairing and with miRNA Model machine system works. The RNAi types of agents example of mediate rna i is for example siRNA or miRNA (microRNA) or bobby pin RNA (shRNA).
In a step, determine the RNA of a plurality of RNAi reagent according to methods experiment of the present invention Potential. Generally speaking, it is favourable that a large amount of RNAi reagent are provided, because the RNAi that is providing There is positive correlation between amount of reagent and the algorithm quality. Yet, because RNAi reagent is synthetic and RNAi The actual cause such as expensive and time-consuming are determined in the experiment of potential, will determine for the experiment of RNA potential The quantity of RNAi reagent keeps as far as possible low makes us expecting. Yet the quantity of RNAi reagent will Can not be lower than a minimum of a value, namely being lower than this value-based algorithm is what can not correctly train. In preferred enforcement side In the case, provide at least 10 RNAi reagent, at least 50 RNAi reagent or at least 100 RNAi Reagent provides at least 200 RNAi reagent, at least 500 in the embodiment that is more preferably RNAi reagent, at least 1000 RNAi reagent or at least 2000 RNAi reagent. At another In the preferred embodiment, provide to be less than 10000 RNAi reagent, preferably be less than 5000 RNAi Reagent or be more preferably and be less than 3000 RNAi reagent. In a further preferred embodiment, RNAi Reagent is selected at random. RNAi reagent can be overlapping or nonoverlapping, in preferred enforcement In the scheme, RNAi reagent is nonoverlapping. RNAi reagent comprises the zone with the target gene complementation. Several Individual RNAi reagent can comprise the zone with same target gene complementation, they can be overlapping or Nonoverlapping. In a particular, all RNAi reagent comprises with same target gene mutual The zone of mending, they can be overlapping or nonoverlapping. In another embodiment, RNAi Reagent comprises and an above target gene, preferably at least 2 target genes, 5 target genes or extremely at least The zone of few 10 target gene complementations.
In another preferred embodiment of the present invention, the RNAi reagent sequence that step provides in a) Be used for Preliminary screening RNAi specificity. " RNAi specificity " or " specificity " are this areas Term and refer in the context of the present invention the selective of RNAi reagent namely refers to the choosing of RNAi reagent Selecting property inhibition or reduce particular target gene expression and do not suppress or reduce in cell, tissue or the organism and show The ability of other gene expression that reaches. In theory, specific RNA i reagent only suppresses the table of target gene Reach and do not affect the expression of whole other genes of expressing in cell, tissue or the organism. For this reason, Specific RNA i reagent advantageously comprises the sequence with the target gene complete complementary, does not namely have with target sequence The complementary series of mispairing, but do not comprise with whole other genes of in cell or organism, expressing complete Complete complementary sequence, namely described complementary series with except target gene cell, tissue or organism Middle full sequence of expressing has at least 1 mispairing, and preferably at least 2 mispairing are more preferably at least 3 mispairing. For example, use to be used for sequence suitable software relatively, by with purpose RNAi reagent The whole known expressed sequence of available specific cells, tissue or organism advances in sequence and the database The row computer relatively can carry out the specific Preliminary screening of RNAi reagent.
Appropriate experimental determines that many methods of the RNAi potential of RNAi reagent are known in the art. Usually, will comprise the complementary double-stranded RNA i reagent transfection of distinguishing of given target gene and enter the expression target gene Cell. For transfection, can use diverse ways, for example electroporation, use cation lipid Or cationic polymer is used for transfection as adminicle. Then at the suitable condition that allows expression of target gene Lower incubated cell. Use subsequently proper technology to measure the expression of target gene, for example RT-PCR or measurement The amount of reporter protein. In preferred embodiments, the coding reporter fusion mRNA with The siRNA cotransfection. Preferably, target nucleotide sequences is inserted into 3 of reporter coded sequence ,-UTR In. Like this, target will not translated and its downward modulation will not have biological impact. In preferred reality Execute in the scheme, reporter protein is yellow fluorescence protein matter (YFP). In another embodiment In, used the contrast of negative control and reporter specificity, i.e. target reporter protein coding District and do not rely on thus 3 '-UTR insetion sequence and with the reticent reporter protein of similar potential SiRNA, and allow to use reporter protein expression level that each siRNA obtains and The expression of negative control and the special positive control of reporter compares. Like this, will be for entirely The measured expression of the siRNA of section compares and concentrates is a single homogeneity data set. Substantially On, the cell of any kind can be used for transfection, however in preferred embodiments, cell is Eukaryotic, preferred zooblast is more preferably mammalian cell and human cell most preferably.
In case will compare (being also referred to as normalization method) for the viewed restraining effect of each siRNA and positive and negative control, to produce with experiment institute for each RNAi reagent and to determine the experiment latent energy value that RNAi potential is relevant, and cause producing and to collect into the concentrated reading of experimental data.In preferred embodiments, obtain experimental value by measuring reporter gene protein.Preferably, all data of experiment reading is from the single type experimental situation under the homogeneity condition.Data are preferably measured based on protein level rather than the measurement of mRNA level, in case promptly be exposed to RNAi reagent then measure expressed proteinic amount of target gene rather than the amount of mRNA.Described among the embodiment hereinafter and be used for testing the general experimental program of determining RNAi potential according to the present invention.
In the preferred embodiment of the invention, the reporter gene assay method is used for the active experiment of RNAi and determines.Use according to reporter gene assay method of the present invention allows screening to have a large amount of siRNA at extensive target of standard test reading.This kind assay method is described in H ü sken etc., Nucleic AcidsRes., the 31st volume, the 17th phase, e102 page or leaf (2003).In brief, provide and comprise the total length reporter gene mRNA that has the purpose target region that is inserted into 3 ' non-translational region and merge the transcript construct.For example, in construct, use luciferase and fluorescence reporter gene.The RNAi reagent of pending RNAi potential test comprises and the purpose insertion sequence complementary sequence that is inserted into 3 ' non-translational region.With suitable transfection method the transfection of RNAi reagent is entered in the cell of instantaneous or constitutive expression reporter gene construct then, and under the suitable condition that allows reporter gene to express culturing cell.Measure the expressed proteinic level of reporter gene subsequently.This kind assay method allows to have in the protein level measurement a large amount of siRNA reagent at extensive target of standard test reading.Like this, the data set that is produced is that potential data homogeneity and whole are comparable each other.
Above-mentioned data set is used for the training of human artificial neural networks.Artificial neural network is known in the art, sees for example Zell, Simulation neuronaler Netzwerke, Addison Wesley (1994); And Rumelhart and McLelland, Parallel Distributed Processing, the 1st volume, MITPress, Cambridge, MA (1986), and artificial neural network can obtain from for example http://www-ra.informatik.uni-tuebingen.de/SNNS.Demographic information will be by artificial neural network from siRNA sequence antisense strand extraction and relevant with the screening measurement.Finally, the network of being trained can be applied to any list entries with provide to " will " screen the evaluation of measurement.For example, illustrative purposes for example can be used 3 layers of feedforward network with anti-pass training 7-8 time.Input layer is made up of the orderly node in 4 roads, sees Fig. 1.Examine 4 nodes that the base type has a road and usually has the Different Alkali base type in the same list entries position of mentioning for every kind.The position number is the length of list entries.During training and/or using, preset time arbitrarily point on given position arbitrarily exactly a node be activatory.Activity along orderly road shows as list entries then.The signal of activation node propagates into the second layer from input layer, is also referred to as the hidden unit layer.In this was propagated, the signal of input layer (perhaps 0 or 1) carried out different weights, adds up to the signal that forms hidden unit.Similarly, the signal of hidden unit develops into the single output node of the 3rd layer and last one deck.Weighting is a storage element of representing knowledge of statistics.Originally, weights are set at random, and cause producing the output signal of the siRNA antisense sequences that departs from true screening signal.Difference between current network output signal and the experimental result is used to change whole weights to reduce difference.Backpropagation by the network in-position so that in the second layer, have ' truly ' target signal of hidden unit.
The present invention provides the computer system that comprises computer hardware and algorithm of the present invention on the other hand.Another aspect of the present invention provides the computer-readable medium that comprises algorithm of the present invention.
The present invention provides on the other hand and has been used to obtain to have the RNAi compositions and methods of enhanced at the RNAi potential possibility (being the possibility that enhanced suppresses to select in advance expression of target gene) of given target gene.Therefore, for specified given amount R NAi reagent, if RNAi reagent designs at random, it will be effective then using the high per-cent of the RNAi reagent of this method design.On the contrary, have to design and screen less RNAi reagent so that find RNAi reagent to be used for specificity inhibition target gene expression to the high RNAi potential of determined number.In one embodiment, the method according to this invention may further comprise the steps:
A) provide a plurality of RNAi reagent sequences that comprise with given target gene complementary zone;
B) use algorithm application that neural network will be trained according to the present invention in described RNAi reagent sequence; With
C) select through predicting effective RNAi reagent sequence.
In the first step, selected a plurality of candidate rna i reagent sequences that comprise with target gene complementary sequence, target gene is promptly by the repressed gene of RNAi.The specificity that can preliminary screening RNAi reagent or the existence or the shortage of some sequence motifs.They can be eclipsed or non-overlapped.In one embodiment, candidate rna i reagent is selected at random.In second step, the potential of the RNAi reagent that the neural network prediction of use algorithm training according to the present invention is provided in the first step.In next step, select through predicting effective RNAi reagent sequence.For example, can select 3 or 5 or 10 RNAi reagent the most effective.Alternatively, select the whole RNAi reagent sequences of predictor greater than certain threshold value.In preferred embodiments, threshold value is at least 0.7, at least 0.75, at least 0.8 or at least 0.85.RNAi potential that at present can the selected RNAi reagent of experimental mensuration.Therefore, in next step, synthesized the RNAi reagent that is suitable for RNAi, it comprises the activated sequence of prediction.In preferred embodiments, RNAi reagent is chemosynthesis.Those skilled in the art are familiar with being used for this class oligonucleotide synthetic chemical process, for example by well-known solid phase synthesis technique.Yet RNAi reagent can also use biochemical method synthetic, for example in-vitro transcription or based on the system of carrier.Now, can with the suitable cellular exposure of expressing target gene in synthetic RNAi reagent (perhaps under situation based on the system of carrier, with cellular exposure in the carrier that comprises aim sequence), hatch under optimum conditions and can use proper method to measure the target gene expression level.In contrast, the cell that can the not be exposed to aim sequence gene expression dose that hits.
Following examples are used to the preferred embodiments of the invention are described and are not intended to limit the present invention.
Embodiment
In illustrating method of the present invention, in raji cell assay Raji, screened the potential of 3,000 above siRNA of 34 different mRNA of target.Characteristics of this research are to have produced the analysis that the homogeneity data set is used for potential-sequence relation subsequently.This is by using fusion mRNA reporter gene assay method to become possibility.See (2003) such as H ü sken, on seeing.In this assay method, the plasmid transfection of the proteinic reporter gene fusion mRNA of the reporter gene of will encoding (wherein target sequence has been inserted among 3 '-UTR of reporter gene mRNA) then carries out the transfection of RNAi reagent.Like this:
A) result of target sequence downward modulation does not have the biology consequence;
B) in all measuring, the potential reading is a protein level, and because same reporter gene protein is used for full-fledged research, so the potential data can not produce deviation because of reading is different; With
C) in all measuring, use common positive control and negative control to allow whole potential data are carried out normalization method.
Artificial neural network has been used to study the sequence potential relation of this homogeneity potential data set.As a result, the artificial neural network of being trained can only be predicted the potential of any siRNA based on its nucleotide sequence.Because there is the sequence demand in the potential of the gene silencing reagent that works by the RNA interference channel, so this method will be applied to other RNAi reagent such as shRNA or miRNA.
RNAi reagent
The RNAi reagent that is used for this research is 21-mer double-stranded RNA i reagent, and it has the RNA zone of 19 base pairings and has di-deoxynucleoside acid at 3 ' end of every chain overhangs.Overhanging of sense strand is two thymidines all, and overhanging of antisense strand is to be designed to and the acid of target complementary di-deoxynucleoside.
Screening method: eYFP mRNA-merges the reporter gene assay method
The structure of reporter gene cloning by expression
Made up based on the carrier pNAS-092 that strengthens blue-greenish colour and the two reporter genes of yellow fluorescence protein matter (eCFP, eYFP) and (be described in (2003) such as H ü sken, on seeing), after the terminator codon of eYFP, comprise multiple clone site so that insert suitable purpose cDNA or EST.The eCFP reporter gene is used for that normalization method under EF-1 α (EF-1 α) promoters driven is measured and the eYFP reporter gene is used to monitor siRNA activity under the CMV promoters driven.The source of carrier is the plasmid pBudCE4 (Invitrogen) that comprises hCMV and EF-1 α promotor.Produce pNAS-092 by inserting from the eCFP gene of peCFP-N1 (Clontech) and by shifting with synthetic DNA fragment from the eYFP gene of peFP-N1 (Clontech) with cloning site (EcoRV, NotI, HindIII, KpnI, XbaI).Confirm employed synthetic DNA in pNAS-092 by order-checking.For alternative clone's strategy, convert pNAS-092 to GatewayTM destination carrier pNAS-097 by after the eYFP terminator codon, inserting attR1 and attR2 cloning site according to the method (Invitrogen) of manufacturer.Process connection (pNAS-092) or reorganization (pNAS-097) are inserted cloning site with c-DNA and have been made up the whole plasmids that are used for final reporter gene assay method.
Clone and cell cultures
People's non-small cell lung cancer cell be H-1299 (CRL-5803) available from ATCC (Rockville, MD).The H-1299 cell is at 37 ℃, 5% humidity CO
2Be maintained under the air in RPMI 1640 substratum (Life Technologies) that contain 10% foetal calf serum and 1%L-glutamine.Preceding 48 hours of transfection is dispersed to 80% inferior converging state with cell.Transfection the day before yesterday, disperse (50 μ L) in black 96 hole assay plate (Costar, clear bottom) with trypsin digestion cell, washing and equivalent.
Use is carried two reporter gene constructs of reference gene (normalization method in the cell) and is measured fluorescence protein.
Plasmid transfection
Lipofectamine-PLUS reagent is hatched (22ng/ μ L plasmid, 4.4mL/mL Lipofectamine-PLUS) with the plasmid that is diluted in OptiMEM-I, then with OptiMEM-I with 11 times of this mixture diluted.(20mM pH7.2) dilutes Lipofectamine 1.3 times and with further dilution 28.6 times (26.6 μ L/mL Lipofectamine) and placing 15 minutes of OptiMEM-I in advance with HEPES.Two kinds of mixtures 1: 1 are mixed and hatched 15 minutes, further with 10 times of OptiMEM-I dilutions.Suction is removed substratum and 100 μ L is added cell (0.2 μ L/mLLipofectamine-PLUS, 13.3 μ L/mL Lipofectamine, 1ng/ μ L plasmid).After 2 hours, 50 μ L siRNA transfection mixtures are added cell, then it was further hatched 2 hours.
The siRNA transfection
To mix with the Oligofectamin (60 μ L/mL) of OptiMEM-I dilution and incubated at room 30 minutes.With hybridization buffer (30mM HEPES, 100mM Potassium ethanoate, 2mM magnesium acetate: pH7.63 during room temperature) siRNA is diluted to 600 μ M from the hybridization stock solution.Annealed 2 minutes for 90 ℃, placed 1 hour for 37 ℃ subsequently.The Oligofectamin of dilution and siRNA with 2: 1 volume mixture, and were hatched 15 minutes.The siRNA-Oligofectamin mixture is further used OptiMEM-I to dilute with 1: 1 and is transferred to (50 μ L) on the cell (final concentration 0.7ng/ μ L plasmid, 10 μ L/mLOligofectamin, 50nM siRNA).Remove substratum and do not contain standard RPMI substratum phenol red but that contain 10% foetal calf serum and 1%L-glutamine and replace with 100 μ L, and 37 ℃ of cultivations 3 days.Interval measurement fluorescence with 24 hours.Use exciter filter and the emission spectral filter of 480/30nm and exciter filter and the emission spectral filter measurement eCFP of 535/30nm and the fluorescence of eYFP of 500/25nm of 436/20nm respectively.The normal eYFP activity of each cell count of quotient representation of eYFP/eCFP fluorescence counting.For this data gathering of using eYFP reporter gene assay method, whole siRNA processing of positive criteria (the special siRNA NAS-12842/58 of YFP-) and negative standard (luciferase siRNA NAS-8548/9) are repeated in triplicate.Calculate standard siRNA NAS-12842/58 and handled standard error of the mean, found its average out to 9.1%.
Target is selected
At first, selected to have the reporter plasmid of 34 different insertion sequences.The size of insertion sequence is between 344 Nucleotide and 3784 Nucleotide.
The siRNA sequences Design
Every block of plate has 79 siRNA, always has 3160.Sequences Design is moved for chance move on insertion sequence, allows overlapping (0-20 the base) of different sizes.When the insertion sequence size is 27kb, even the regular selected location of 3160 siRNA will cause obviously overlapping (13 bases) in the siRNA sequence.Do not consider to have the sequence that long polynucleotide extend (5 or more continuous nucleotides).Under long insertion sequence situation, design two covers, 79 siRNA of every cover.
Detected the Nucleotide of 3160 siRNA sequences in whole set.The whole possible motif of finding the as many as tetranucleotide all is present in the siRNA arrangement set.
The screening form
Each siRNA plate comprises 79 siRNA, a negative control (anti-luciferase siRNANAS-8549), 2 special siRNA of reporter gene (anti-YFP siRNA NAS-12842 and NAS-12847).With the contrast siRNA of single batch of pipette, extract, and triplicate.Stay the negative control that 8 emptying apertures will be used for " plasmid is only arranged ".
The filtration of siRNA activity data and normalization method (see figure 2)
Each plate is measured in duplicate, and measures the YFP level at 2 time points by fluorimetry.For each plate, detected the linear dependence between twice repetition, and the restraining effect level of positive and negative control.When the dependency between twice repetition surpasses 0.7 and extremely then accept the data (see figure 3) at least 60% the time when compare positive control downward modulation YFP with negative control.Abandoned the data set measured from 5 times according to this filtration.Remaining data collection (2717 sequences) is divided into training set and inspection set, is higher than 30% siRNA and further filtration by individually detecting each siRNA and getting rid of to make a variation in twice repetition.Like this, about 15% data have further been removed.Remaining twice repeat number strong point averaged to produce the data set that noisiness reduces.The final data collection comprises 2109 sequences in the training set and 234 sequences in the inspection set.By affine system A
T (i)=A (S (i))=(T_ height-T_ is low)/(S_ height-S_ is low) * (S (i)-S_ is low)+T_ is low to put S (i) (i index strong point) normalization method with all data, wherein the initialize signal of negative control be S_ low and will carry out normalization method obtain T_ low (we set T_ low=0.1=10%).Same definition positive control signal S_ height and the T_ height (being set at 0.9=90%) that converts it into.
Artificial neural network training (see figure 3)
The siRNA sequence data is sent into input layer and is adjusted weights between the node of network with screening reporter gene signal.Each siRNA sequence and its screening are measured and are sent into altogether 10 times.After sending into all data point once, learning rate with 0.1 and 0.1 factor of momentum upgrade the weights of network synchronously.See Zell, Simulation Neuronaler Netzwerke, Addison Wesley (1994).Based on 5 different initial weights, resulting 5 housebroken network weights are different but whole 5 networks show that consistently the output of predicting only slightly changes.Obtained last output result by signal averaging with each output node of whole 5 networks.For simplicity, we will on average export the output that is called this network and check any single network characteristic with replacement.
The evaluation (see figure 4) of predictor performance
With the network of being trained experiment is suppressed activity and predict, prediction is active when being applied to inspection set suppresses active 0.63 the dependency that has with experiment, then shows the higher dependency 0.665 of appropriateness when being applied to training set.Fig. 3 describes both consistence.The dependency between predictor and experimental value, the performance that the threshold value by setting active siRNA of experiment and the active siRNA of prediction can evaluation algorithms.The threshold setting of testing active siRNA is 75% (0.75) of a normalization method potential.The threshold setting of predicting active siRNA is the value greater than 0.8.These threshold values form 4 quadrants, comprise true negative (prediction non-activity and non-activity), false negative (prediction non-activity but activity is arranged), false positive (prediction has activity but non-activity) and true positives (prediction has activity and activity is arranged) sequence.Can determine the predictor performance by its susceptibility and selectivity.
Predictor susceptibility=true positives/(true positives+false negative)=0.26
Predictor selectivity=true positives/(true positives+false positive)=0.71
These numerical value show that institute of institute forecasting sequence is that active probability was 71% (as defined above).This numerical value can with concentrate viewed hit rate to compare in one-hundred-percent inspection, be activated and concentrate 35% sequence in one-hundred-percent inspection.Equally, predictor will identify 26% bioactive sequence.
The training set size is to predictor Effect on Performance (see figure 5)
Training set data is unnecessary all to be used for training, and this allows the reduction of research along with the minimizing BIOpred estimated performance of collection size.The size of inspection set is a constant.See Fig. 5, dependency continues along with the minimizing of training set slowly to reduce.For few training dataset to 265 data points, dependency still is about 0.53.
Claims (18)
1. a generation is used to predict the method for algorithm of the RNAi potential of RNAi reagent, and it comprises:
A) experiment determines to comprise the RNAi potential with a plurality of RNAi reagent of at least one target gene complementary sequence; With
B) with described data set training of human artificial neural networks.
2. method according to claim 1, wherein RNAi potential is determined by measuring the coded proteinic quantity of target gene.
3. according to requiring any described method in the aforementioned right, wherein RNAi potential is determined by the reporter gene assay method.
4. according to requiring any described method in the aforementioned right, wherein RNAi reagent is by the 3 ' UTR of target to the proteinic fusion mRNA of reporter gene of encoding.
5. according to requiring any described method in the aforementioned right, wherein will carry out normalization method from the data of different fusion mRNA.
6. according to requiring any described method in the aforementioned right, wherein determine the potential of at least 100 RNAi reagent, preferred at least 1000 RNAi reagent.
7. according to requiring any described method in the aforementioned right, the sequence of wherein said RNAi reagent is selected at random.
8. according to requiring any described method in the aforementioned right, the sequence of wherein said RNAi reagent has fully complementary with said target mrna so that to combine the length of said target mrna be 15 and 30 zones between the Nucleotide.
9. according to requiring any described method in the aforementioned right, wherein the respective regions of the complementary region of RNAi reagent and target gene has one or several mispairing.
10. according to requiring any described method in the aforementioned right, wherein RNAi reagent is siRNA.
11. according to requiring any described method in the aforementioned right, wherein RNAi reagent is shRNA.
12. according to requiring any described method in the aforementioned right, wherein RNAi reagent is miRNA.
13. according to requiring any resulting algorithm of described method in the aforementioned right.
14. a computer-readable storage media, it comprises algorithm according to claim 13.
15. a computer system, it comprises algorithm according to claim 13 and computer hardware.
16. a method of predicting the RNAi potential of RNAi reagent, it comprises:
A) provide a plurality of RNAi reagent that comprise with given target gene complementary zone;
B) use neural network that described RNAi reagent is moved housebroken algorithm according to claim 13; With
C) select through predicting effective RNAi reagent sequence.
17. a method that suppresses given expression of target gene, it comprises:
A) provide a plurality of RNAi reagent that comprise with given target gene complementary zone;
B) use neural network that described RNAi reagent is moved housebroken algorithm according to claim 13;
C) select through predicting effective RNAi reagent sequence;
D) synthesize at c) in selected RNAi reagent; With
E) will express the cellular exposure of target gene in d) RNAi reagent in.
18. according to claim 16 or 17 described methods, wherein at c) in selected RNAi reagent be higher than given threshold value.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101625732B (en) * | 2009-08-03 | 2011-11-30 | 杭州电子科技大学 | Forecasting method of water level of potamic tidewater |
| CN109360604A (en) * | 2018-11-21 | 2019-02-19 | 南昌大学 | A kind of oophoroma molecule parting forecasting system |
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| DE102006016365B4 (en) * | 2006-04-05 | 2015-08-13 | Qiagen Gmbh | RNA interference tags |
| JP2010513246A (en) * | 2006-12-15 | 2010-04-30 | ノバルティス アーゲー | Inhibition of GPR4 |
| JP5258874B2 (en) | 2007-04-10 | 2013-08-07 | キアゲン ゲゼルシャフト ミット ベシュレンクテル ハフツング | RNA interference tag |
| EP2260102A1 (en) | 2008-03-25 | 2010-12-15 | Novartis Forschungsstiftung, Zweigniederlassung Friedrich Miescher Institute For Biomedical Research | Treating cancer by down-regulating frizzled-4 and/or frizzled-1 |
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| WO2012168259A1 (en) | 2011-06-06 | 2012-12-13 | Novartis Forschungsstiftung, Zweigniederlassung | Protein tyrosine phosphatase, non-receptor type 11 (ptpn11) and triple-negative breast cancer |
| US20140314787A1 (en) | 2011-11-08 | 2014-10-23 | Novartis Forschungsstiftung, Zweigniederlassung, Friedrich Miescher Institute | Treatment for neurodegenerative diseases |
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| WO2014006115A1 (en) | 2012-07-06 | 2014-01-09 | Novartis Ag | Combination of a phosphoinositide 3-kinase inhibitor and an inhibitor of the il-8/cxcr interaction |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101625732B (en) * | 2009-08-03 | 2011-11-30 | 杭州电子科技大学 | Forecasting method of water level of potamic tidewater |
| CN109360604A (en) * | 2018-11-21 | 2019-02-19 | 南昌大学 | A kind of oophoroma molecule parting forecasting system |
| CN109360604B (en) * | 2018-11-21 | 2021-09-24 | 南昌大学 | Ovarian cancer molecular typing prediction system |
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| JP2007523632A (en) | 2007-08-23 |
| KR20060126499A (en) | 2006-12-07 |
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