KR102452413B1 - Method for detecting chromosomal abnormality using distance information between nucleic acid fragments - Google Patents

Abstract

The present invention relates to a method for detecting a chromosome abnormality using distance information between nucleic acid fragments, and more specifically, by extracting nucleic acids from a biological sample to obtain sequence information, and then calculating the distance between reference values of nucleic acid fragments. It relates to a method for detecting abnormalities in chromosomes. The chromosomal abnormality determination method according to the present invention is a method of analyzing using the concept of a distance between aligned nucleic acid fragments, unlike the conventional method using a step of determining the amount of chromosomes based on the number of reads. Although the accuracy of the method decreases when the number of reads is reduced, the method of the present invention can not only increase the accuracy of detection even when the number of reads is reduced, but also detect by analyzing the distance between the nucleic acid fragments of a certain section rather than all chromosome sections. It is useful because of its high accuracy.

Description

Method for detecting chromosomal abnormality using distance information between nucleic acid fragments

The present invention relates to a method for detecting a chromosome abnormality using distance information between nucleic acid fragments, and more specifically, by extracting nucleic acids from a biological sample to obtain sequence information, and then calculating the distance between reference values of nucleic acid fragments. It relates to a method for detecting abnormalities in chromosomes.

Chromosomal abnormalities are associated with genetic defects and tumor diseases. The chromosomal abnormality may mean a deletion or duplication of a chromosome, a deletion or duplication of a portion of a chromosome, or a break, translocation, or inversion in a chromosome. Chromosomal abnormalities are one of the disorders of genetic balance, leading to fetal death or serious defects in physical and mental condition and tumor diseases. For example, Down's syndrome is a common form of chromosome number abnormality caused by the presence of three chromosome 21 (trisomy 21). Edwards syndrome (trisomy 18), Patau syndrome (trisomy 13), Turner syndrome (XO), and Klinefelter syndrome (XXY) are also chromosome abnormalities do. Chromosomal abnormalities are also found in tumor patients. For example, duplication of regions 4q, 11q, and 22q and deletion of region 13q were confirmed in liver cancer patients (Liver Adenomas and adenocarcinomas), and duplication of regions 2p, 2q, 6p, 11q and 6q, 8p, 9p, and chromosome 21 in pancreatic cancer patients. Areas were confirmed. These regions are related to tumor-related oncogene and tumor suppressor gene regions.

Chromosomal abnormalities can be detected using Karyotype and Fluorescent In Situ Hybridization (FISH). This detection method is disadvantageous in terms of time, effort and accuracy. In addition, DNA microarrays can be used to detect chromosomal abnormalities. In particular, in the case of a genomic DNA microarray system, it is easy to prepare a probe and detect chromosomal abnormalities in the intron region of the chromosome as well as the extended region of the chromosome, but the location and function of the DNA fragment in the chromosome can be confirmed in large numbers. difficult to manufacture

Recently, next-generation sequencing technology has been used to analyze chromosome number abnormalities (Park, H., Kim et al., Nat Genet 2010, 42, 400-405.; Kidd, J. M. et al., Nature 2008, 453, 56-64. ). However, this technique requires high coverage readings for the analysis of chromosome number abnormalities, and CNV measurements also require independent validation. Therefore, the cost is very high and the results are difficult to understand, so it was not suitable as a general gene search analysis at that time.

Real-time qPCR is currently used as a state-of-the-art technique for quantitative genetic analysis, which has a wide kinetic range (Weaver, S. et al, Methods 2010, 50, 271-276) and a linearity between threshold cycle and initial target amount. This is because a positive correlation is observed reproducibly (Deepak, S. et al., Curr Genomics 2007,8, 234-251). However, the sensitivity of the qPCR assay is not high enough to discriminate copy number differences.

On the other hand, existing prenatal tests for fetal chromosomal abnormalities include ultrasound test, blood marker test, amniotic fluid test, chorionic blood test, and transdermal umbilical cord blood test (Mujezinovic F, et al. Obstet Gynecol. 2007, 110(3):687). -94.). Among them, ultrasound and blood marker tests are classified as screening tests, and amniotic chromosome tests are classified as confirmatory tests. Ultrasound and blood marker tests, which are non-invasive methods, are safe methods because they do not collect samples directly from the fetus, but the sensitivity of the tests is less than 80% (ACOG Committee on Practice Bulletins. 2007). Invasive methods such as amniotic fluid test, chorionic blood test, and transdermal umbilical cord blood test can confirm fetal chromosomal abnormalities, but have a disadvantage in that there is a possibility of loss of the fetus due to invasive medical practices.

In 1997, Lo et al. succeeded in sequencing the Y chromosome of fetal genetic material from maternal plasma and serum, and used the fetal genetic material in the mother for prenatal testing (Lo YM, et al. Lancet. 1997, 350(9076)). :485-7). Fetal genetic material in maternal blood is a part of trophoblast cells that have undergone apoptosis during placental remodeling and entered into maternal blood through a substance exchange mechanism. do.

cff DNA is found in most maternal blood as early as day 18 and day 37 of embryo transfer. Since cff DNA is a short strand of 300 bp or less and is present in a small amount in maternal blood, large-scale parallel sequencing technology using next-generation sequencing (NGS) is used to apply it to the detection of fetal chromosomal abnormalities. The non-invasive detection of fetal chromosomal abnormalities using large-scale parallel nucleotide analysis technology shows detection sensitivity of 90-99% or more depending on chromosomes, but false positive and false negative results are 1-10%, so it is time to correct this. (Gil MM, et al. Ultrasound Obstet Gynecol. 2015, 45(3):249-66).

Accordingly, as a result of the present inventors' diligent efforts to solve the above problems and develop a method for detecting chromosomal abnormalities with high sensitivity and accuracy, grouping nucleic acid fragments aligned in a chromosomal region, and then determining the distance between the nucleic acid fragment reference values When compared with the normal group by calculation, it was confirmed that chromosomal abnormalities can be detected with high sensitivity and accuracy, and the present invention has been completed.

An object of the present invention is to provide a method for determining a chromosome abnormality using distance information between nucleic acid fragments.

Another object of the present invention is to provide an apparatus for determining a chromosome abnormality using distance information between nucleic acid fragments.

Another object of the present invention is to provide a computer-readable storage medium comprising instructions configured to be executed by a processor for determining an abnormality of a chromosome by the above method.

In order to achieve the above object, the present invention provides a method for detecting a chromosomal abnormality by calculating a distance between reference values of nucleic acid fragments extracted from a biological sample.

The present invention also provides a decoding unit for extracting nucleic acids from a biological sample and deciphering sequence information; an alignment unit that aligns the translated sequence to a standard chromosomal sequence database; And by measuring the distance between the reference values of the aligned nucleic acid fragments with respect to the selected nucleic acid fragments (fragments), to calculate the FD value (Fragments Distance), based on the calculated FD value FDI value for the entire chromosome region or for each specific genetic region By calculating (Fragments Distance Index), when the FDI value is less than or exceeding the reference value range, there is provided a chromosomal abnormality detecting apparatus including a chromosomal abnormality determining unit that determines that there is a chromosomal abnormality.

The present invention also provides a computer readable storage medium comprising instructions configured to be executed by a processor for detecting a chromosomal abnormality,

(A) extracting a nucleic acid from a biological sample to obtain a nucleic acid fragment to obtain sequence information;

(B) aligning the nucleic acid fragment to a standard chromosome sequence database (reference genome database) based on the obtained sequence information (reads);

(C) measuring the distance between the reference values of the selected nucleic acid fragments (fragments), calculating the FD value (Fragments Distance); and

(D) Calculate the FDI value (Fragments Distance Index) for the entire chromosome region or for each specific genetic region based on the FD value calculated in step (C). If the FDI value is less than or greater than the reference value or range, chromosomal abnormal There is provided a computer-readable storage medium comprising instructions configured to be executed by a processor that detects a chromosomal abnormality through the step of determining that there is a chromosomal abnormality.

The present invention also comprises the steps of (A) extracting nucleic acids from a biological sample to obtain sequence information; (B) aligning the obtained sequence information (reads) to a standard chromosome sequence database (reference genome database); (C) calculating the RD value (Read Distance) by measuring the distance between the aligned reads with respect to the aligned sequence information (reads); and (D) calculating the RDI value (Read Distance Index) for the entire chromosome region or for each specific genetic region based on the RD value calculated in step (C), and when the RDI value is less than or greater than the reference value or range, the chromosome It provides a chromosomal abnormality detection method comprising the step of determining that there is an abnormality.

The present invention also provides a decoding unit for extracting nucleic acids from a biological sample and deciphering sequence information; an alignment unit that aligns the translated sequence to a standard chromosomal sequence database; and by measuring the distance between reads aligned with respect to the selected sequence information (reads), calculating the RD value (Read Distance), and based on the calculated RD value, the RDI value (Read Distance) for the entire chromosome region or for each specific genetic region Index) to provide a chromosomal abnormality detection apparatus including a chromosomal abnormality determining unit that determines that there is a chromosomal abnormality by calculating the RDI value is less than or exceeding the reference value range.

The present invention also provides a computer-readable storage medium comprising instructions configured to be executed by a processor for detecting a chromosomal abnormality, comprising the steps of: (A) extracting nucleic acids from a biological sample to obtain sequence information; (B) aligning the obtained sequence information (reads) to a standard chromosome sequence database (reference genome database); (C) measuring the distance between the reads aligned with respect to the selected sequence information (reads), calculating the RD value (Read Distance); and (D) calculating the RDI value (Read Distance Index) for the entire chromosome region or for each specific genetic region based on the RD value calculated in step (C), and when the RDI value is less than or greater than the reference value or range, the chromosome There is provided a computer-readable storage medium comprising instructions configured to be executed by a processor that detects a chromosomal abnormality through the step of determining that there is an abnormality.

The chromosomal abnormality determination method according to the present invention is different from the conventional method of using a step of determining the amount of chromosomes based on the number of reads, after grouping the aligned nucleic acid fragments, and then between the nucleic acid fragment reference value As a method using the concept of distance, the existing method loses accuracy when the number of reads is reduced, but the method of the present invention can increase the detection accuracy even when the number of reads is reduced, as well as nucleic acid fragments of a certain section rather than all chromosome sections. Even if the distance between them is analyzed, the detection accuracy is high, which is useful.

1 is an overall flowchart for determining a chromosomal abnormality based on an FD value according to an embodiment of the present invention.
2 is a conceptual diagram illustrating a method of calculating an FD value of the present invention in reads produced by a single-end sequencing method.
3 is a conceptual diagram illustrating a method of calculating an FD value of the present invention in reads produced by a paired-end sequencing method.
4 is a conceptual diagram of a method of correcting an FD value by using location information other than a lead in the present invention.
5 is a graph measuring the difference in FD values calculated when non-read position information is not used and when it is used based on read data produced by the paired-end sequencing method in an embodiment of the present invention.
6 is an overall flowchart for determining a chromosomal abnormality based on an RD value according to an embodiment of the present invention.
7 is a schematic diagram of the concept of the Read Distance calculated in the method based on the RD value according to an embodiment of the present invention. In the case of Reads used for the Reads Distance calculation, it can be used regardless of the aligned direction (FIG. 7. A), and can be used in consideration of the aligned direction. (Positive direction: Fig. 7. B, Negative direction: Fig. 7. C)
8 is a diagram illustrating the distribution of the read count and RepRD of the X chromosome in the RD value-based method according to an embodiment of the present invention, and it was confirmed that the relationship between the two values is not a linear but a non-linear relationship.
9 is a schematic diagram of the distribution of read counts and RepRDs for each chromosome in the RD value-based method according to an embodiment of the present invention, (A) is a normal chromosome, (B) is a trisomy ) chromosome 21, (C) shows a trisomy 18, and (D) shows a trisomy 13 chromosome.
10 is an RDI value calculated by the RD value-based method according to an embodiment of the present invention, fetal fraction (FIG. 10A), gestational weeks (FIG. 10B), and G-score value (Korean Patent No. 10- 1686146, it is the result of confirming the relationship of FIG. 10C), respectively.
11 is a result of ROC analysis of a sample identified as a normal group and each chromosome aneuploidy in the method based on the RD value according to an embodiment of the present invention.
12 is a result of confirming the accuracy according to the number of reads in the method based on the RD value according to an embodiment of the present invention. The X axis is the number of reads, and the Y axis is the AUC.
13 is a result of confirming the relationship between the RD value-based method and the number of reads and chromosomal abnormalities according to an embodiment of the present invention.
14 is a result of comparing the RD value-based method and microarray analysis results according to an embodiment of the present invention.
15 is a result of confirming the distribution of RDI values in which the RepRD of a normal person and a sample of chromosome 21 aneuploidy is set as the reciprocal of the median in the RD value-based method according to an embodiment of the present invention.
16 is a result of confirming the distribution of RDI values in which RepRD of a normal person and a sample of chromosome 21 aneuploidy is set as an average value in the RD value-based method according to an embodiment of the present invention.
17 is a result of confirming the distribution of RDI values in which RepRD of a normal person and a sample of chromosome 21 aneuploidy is set as the reciprocal of the average value in the method based on the RD value according to an embodiment of the present invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In general, the nomenclature used herein and the experimental methods described below are well known and commonly used in the art.

In the present invention, sequence information (read) data obtained from a sample is aligned with a reference genome, then the aligned nucleic acid fragments are grouped, and then the distance between the nucleic acid fragment reference values is calculated to analyze the normal population and test subjects. In the case of detecting a chromosomal abnormality by comparing the representative values in the desired chromosome, it was tried to confirm that the chromosomal abnormality can be detected with high sensitivity and accuracy.

The chromosomal abnormality detection method according to the present invention can be used not only for chromosomal abnormalities such as aneuploidy, but also for tumor detection, ie, tumor diagnosis or prediction of prognosis.

That is, in one embodiment of the present invention, after sequencing the DNA extracted from blood, aligning it to a reference chromosome, grouping the nucleic acid fragments into a whole group, a forward group, and a reverse group, and then grouping the nucleic acid fragment reference value for each group After calculating the fragment distance (FD), deriving a representative value (RepFD) of the distance between the reference values of nucleic acid fragments for each genetic region, the RepFD ratio is calculated using a normalization factor, and the RepFD ratio and By comparison, FDI (Fragment Distance Index) values for each group were derived, and when the FDI values for all groups were less than or more than the reference value, a method was developed for determining that there is a chromosomal abnormality of the test subject (FIG. 1).

Accordingly, in one aspect, the present invention relates to a method for detecting a chromosomal abnormality by calculating a distance between reference values of nucleic acid fragments extracted from a biological sample.

In the present invention, the nucleic acid fragment can be used without limitation as long as it is a fragment of a nucleic acid extracted from a biological sample, but preferably a fragment of a cell-free nucleic acid or an intracellular nucleic acid, but is not limited thereto.

In the present invention, the nucleic acid fragment may be characterized in that it is obtained by direct sequencing, sequencing through next-generation sequencing, or sequencing through non-specific whole genome amplification.

In the present invention, for the method of directly sequencing the nucleic acid fragment, all known techniques may be used.

In the present invention, the method of sequencing through non-specific full-length genome amplification refers to any method of amplifying a nucleic acid using a random primer and then performing sequencing.

In the present invention, the method of calculating the distance between the reference values of nucleic acid fragments using sequencing through next-generation sequencing and determining whether there is a chromosomal abnormality therefrom,

(A) extracting a nucleic acid from a biological sample to obtain a nucleic acid fragment to obtain sequence information;

(B) confirming the position of the nucleic acid fragment in a standard chromosome sequence database (reference genome database) based on the obtained sequence information (reads);

(C) grouping the sequence information (reads) into full sequence, forward sequence and reverse sequence;

(D) using the grouped sequence information, defining a reference value of each nucleic acid fragment, measuring a distance between the reference values, and calculating an FD value (Fragments Distance) for each group; and

(E) Calculate each FDI value (Fragments Distance Index) for the entire chromosome region or for each specific region based on the FD value for each group calculated in step (D), so that each FDI value is less than or exceeding the reference value range , determining that there is a chromosomal abnormality;

It may be characterized in that it is performed by a method comprising, but is not limited thereto.

In the present invention, the term “chromosomal abnormality” refers to various mutations occurring in chromosomes, and can be largely divided into number abnormalities, structural abnormalities, microdeletions, chromosomal instability, and the like.

An abnormality in the number of chromosomes is a case in which an abnormality occurs in the number of chromosomes, for example, Down Syndrome (the total number of chromosomes is 47 with one more 21st chromosome), Turner Syndrome (with a single X) There are 45 chromosomes) and Klinefelter Syndrome (having chromosome numbers such as XXYY, XXXY, XXXXY) in which the total number of chromosomes occurs in 23 pairs and 46 cases can include both. .

Chromosomal structural abnormalities refer to all cases in which there is no change in the number of chromosomes, such as deletions, duplications, inversions, translocations, fusions, and microsatellite instability (MSI-H), but changes in the structure of chromosomes occur. For example, there may be partial deletion of chromosome 5 (cat crying syndrome), partial deletion of chromosome 7 (Philliams syndrome), partial duplication of chromosome 12 (Wolf-Hirschhorn syndrome), and the like. Chromosomal structural abnormalities found in tumor patients include translocation between chromosomes 9 and 22 (chronic myelogenous leukemia), duplication of regions 4q, 11q, and 22q and deletion of region 13q (liver cancer), 2p, 2q, 6p, and 11q regions. Duplications, deletions of the 6q, 8p, 9p, and chromosome 21 regions (pancreatic cancer), TMPRSS2-TRG gene fusion (prostate cancer), and chromosome-wide microsatellite instability (colon cancer) were confirmed. These regions are related to regions of oncogenes and tumor suppressor genes associated with tumors, but are not limited to those described above.

In the present invention,

The step (A) is

(A-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, amniotic fluid including oral cells, placental cells or fetal cells, tissue cells, and mixtures thereof;

(A-ii) removing proteins, fats, and other residues from the collected nucleic acids using a salting-out method, a column chromatography method, or a beads method; obtaining purified nucleic acids;

(A-iii) single-end sequencing or pair-end sequencing for purified nucleic acids or nucleic acids randomly fragmented by enzymatic digestion, pulverization, or hydroshear method end sequencing) preparing a library;

(A-iv) reacting the prepared library with a next-generation sequencer; and

(A-v) it may be characterized in that it comprises the step of acquiring sequence information (reads) of the nucleic acid in the next-generation gene sequencing machine.

In the present invention, the next-generation sequencer may be used by any sequencing method known in the art. Sequencing of nucleic acids isolated by selection methods is typically performed using next-generation sequencing (NGS). Next-generation sequencing includes any sequencing method that determines the nucleotide sequence of either an individual nucleic acid molecule or a clonal extended proxy for an individual nucleic acid molecule in a highly similar manner (e.g., 10 ⁵ or more molecules simultaneously sequenced). In one embodiment, the relative abundance of a nucleic acid species in a library can be estimated by counting the relative number of occurrences of its cognate sequence in data generated by sequencing experiments. Methods for next-generation sequencing are known in the art and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11:31-46, which is incorporated herein by reference.

In one embodiment, next-generation sequencing is performed to determine the nucleotide sequence of individual nucleic acid molecules (e.g., HeliScope Gene Sequencing system from Helicos BioSciences and Pacific Biosciences). PacBio RS system). In other embodiments, sequencing, e.g., mass-parallel short-read sequencing that yields more bases of sequence per sequencing unit (e.g., San Diego, CA) than other sequencing methods yielding fewer but longer reads. The Illumina Inc. Solexa sequencer method determines the nucleotide sequence of a cloned extended proxy for an individual nucleic acid molecule (e.g., Illumina, San Diego, CA). Illumina Inc. Solexa sequencer; 454 Life Sciences (Branford, Conn.) and Ion Torrent). Other methods or machines for next-generation sequencing include, but are not limited to, 454 Life Sciences (Branford, Conn.), Applied Biosystems (Foster City, CA; SOLiD Sequencer), Helicos. Bioscience Corporation (Cambridge, Massachusetts) and Emulsion and Micro Flow Sequencing Techniques Nano Droplets (eg, GnuBio Drops).

Platforms for next-generation sequencing include, but are not limited to, Roche/454's Genome Sequencer (GS) FLX System, Illumina/Solexa Genome Analyzer (GA). , Life/APG's Support Oligonucleotide Ligation Detection (SOLiD) system, Polonator's G.007 system, Helicos BioSciences' HeliScope Gene Sequencing system (Helicos BioSciences' HeliScope Gene Sequencing system) and Pacific Biosciences' PacBio RS system, MGI's DNBseq.

NGS Technologies may include, for example, one or more of template preparation, sequencing and imaging and data analysis steps.

Mold manufacturing steps. Methods for making templates include steps such as randomly disrupting nucleic acids (e.g., genomic DNA or cDNA) into small sizes and making sequencing templates (e.g., fragment templates or mate-pair templates). can do. Spatially separated templates can be attached or immobilized on a solid surface or support, which allows large-scale sequencing reactions to be performed simultaneously. Types of templates that can be used for NGS reactions include, for example, cloned amplified templates derived from single DNA molecules and single DNA molecule templates.

Methods for preparing the clone-amplified template include, for example, emulsion PCR (emPCR) and solid-phase amplification.

EmPCR can be used to prepare templates for NGS. Typically, a library of nucleic acid fragments is made, and adapters containing universal priming sites are ligated to the ends of the fragments. The fragments are then denatured into single strands and captured by beads. Each bead captures a single nucleic acid molecule. After amplification and enrichment of emPCR beads, a large amount of template can be attached, immobilized on a polyacrylamide gel on a standard microscope slide (e.g., Polonator), and immobilized on an amino-coated glass surface (e.g., , Life/APG; Polonator), or deposited on individual PicoTiterPlate (PTP) wells (e.g., Roche/454) with NGS reaction This can be done.

Solid-phase amplification can also be used to generate templates for NGS. Typically, the front and back primers are covalently attached to the solid support. The surface density of the amplified fragment is defined as the ratio of primer to template on the support. Solid-phase amplification can generate millions of spatially separated template clusters (eg, Illumina/Solexa). The ends of the template cluster can hybridize to universal primers for NGS reactions.

Other methods for the preparation of cloned amplified templates include, for example, Multiple Displacement Amplification (MDA) (Lasken R. S. Curr Opin Microbiol. 2007; 10(5):510-6). MDA is a non-PCR based DNA amplification technique. The reaction involves annealing a random hexamer primer to a template and synthesizing DNA by a high-fidelity enzyme, typically Ф polymerase, at constant temperature. MDA can produce large-scale artifacts with a lower error frequency.

Template amplification methods such as PCR can bind the NGS platform to the target or enrich specific regions of the genome (eg, exons). Representative template enrichment methods include, for example, microdroplet PCR techniques (Tewhey R. et al., Nature Biotech. 2009, 27:1025-1031), custom-designed oligonucleotide microarrays (e.g., Roche/ NimbleGen oligonucleotide microarrays) and solution-based hybridization methods (eg, molecular inversion probes (MIPs)) (Porreca G. J. et al., Nature Methods, 2007, 4:931-936; Krishnakumar S. et al., Proc. Natl. Acad. Sci. USA, 2008, 105:9296-9310; Turner E. H. et al., Nature Methods, 2009, 6:315-316) and biotinylated RNA capture sequences ( Gnirke A. et al., Nat. Biotechnol. 2009;27(2):182-9).

Single-molecule templates are another type of template that can be used for NGS reactions. Spatially separated single molecule templates can be immobilized on a solid support by a variety of methods. In one approach, individual primer molecules are covalently attached to a solid support. The adapter is added to the template, and the template is then hybridized to the immobilized primer. In another approach, a single-molecule template is covalently attached to a solid support by priming and extending a single-stranded single-molecule template from an immobilized primer. The universal primer is then hybridized to the template. In another approach, a single polymerase molecule is attached to a solid support to which a primed template is attached.

sequencing and imaging. Representative sequencing and imaging methods for NGS include, but are not limited to, cyclic reversible termination (CRT), sequencing by ligation (SBL), single-molecule addition (pyrosequencing) pyrosequencing) and real-time sequencing.

CRT uses a reversible terminator in a cyclic method with minimal nucleotide inclusion, fluorescence imaging and cleavage steps. Typically, DNA polymerases include a single fluorescently modified nucleotide complementary to the complementary nucleotide of the template base in the primer. DNA synthesis is terminated after addition of a single nucleotide, and the uncontained nucleotides are washed away. Imaging is performed to determine the identity of the included labeled nucleotides. Then, in a cleavage step, the terminator/inhibitor and the fluorescent dye are removed. Representative NGS platforms using the CRT method include, but are not limited to, using a cloned amplified template method combined with a 4-color CRT method detected by total internal reflection fluorescence (TIRF). Illumina/Solexa Genome Analyzer (GA); and Helicos BioSciences/HeliScope using a single-molecule template method combined with a one-color CRT method detected by TIRF.

SBL uses a DNA ligase and either a 1-base-encoded probe or a 2-base-encoded probe for sequencing.

Typically, a fluorescently labeled probe hybridizes to a complementary sequence adjacent to the primed template. DNA ligases are used to ligate dye-labeled probes to primers. After the non-ligated probes are washed, fluorescence imaging is performed to determine the identity of the ligated probes. The fluorescent dye can be removed using a cleavable probe that regenerates the 5'-PO4 group for subsequent ligation cycles. Alternatively, the new primers can hybridize to the template after the old primers have been removed. Exemplary SBL platforms include, but are not limited to, Life/APG/SOLiD (Support Oligonucleotide Ligation Detection), which uses a two-base-encoded probe.

The pyrosequencing method is based on detecting the activity of DNA polymerase with another chemiluminescent enzyme. Typically, the method sequences a single strand of DNA by synthesizing the complementary strand along one base pair at a time and detecting the base actually added at each step. The template DNA is immobilized, and solutions of A, C, G and T nucleotides are sequentially added and removed from the reaction. Light is only produced when the nucleotide solution replenishes the unpaired base of the template. The sequence of the solution generating the chemiluminescent signal allows to determine the sequence of the template. Representative pyrosequencing platforms include, but are not limited to, Roche/454 using DNA templates prepared by emPCR with 1 to 2 million beads deposited in PTP wells.

Real-time sequencing involves imaging the continuous inclusion of dye-labeled nucleotides during DNA synthesis. Representative real-time sequencing platforms include, but are not limited to, individual zero-mode waveguide (ZMW) detectors attached to the surface to obtain sequence information when phosphate-linked nucleotides are included in the growing primer strand. Pacific Biosciences platform using DNA polymerase molecules; Life/VisiGen platform using genetically engineered DNA polymerase with attached fluorescent dye to create enhanced signal after nucleotide inclusion by fluorescence resonance energy transfer (FRET); and the LI-COR Biosciences platform using dye-quencher nucleotides in sequencing reactions.

Other sequencing methods of NGS include, but are not limited to, nanopore sequencing, sequencing by hybridization, nano-transistor array based sequencing, polony sequencing, scanning electron tunneling microscopy (STM) based sequencing and nanowire-molecular sensor-based sequencing.

Nanopore sequencing involves electrophoresis of nucleic acid molecules in solution through nano-scale pores that provide a highly enclosed space that can be analyzed in single-nucleic acid polymers. Representative methods of nanopore sequencing are described, for example, in Branton D. et al., Nat Biotechnol. 2008; 26(10):1146-53].

Sequencing by hybridization is a non-enzymatic method using DNA microarrays. Typically, a single pool of DNA is fluorescently labeled and hybridized to an array containing a known sequence. The hybridization signal from a given spot on the array can identify the DNA sequence. Binding of one strand of DNA to its complementary strand in a DNA double-strand is sensitive even to single-base mismatches when the hybrid region is short or a specified mismatch detection protein is present. Representative methods of sequencing by hybridization are described, for example, in Hanna G.J. et al., J. Clin. Microbiol. 2000; 38(7): 2715-21; and Edwards J.R. et al., Mut. Res. 2005; 573(1-2): 3-12).

Poloni sequencing is based on poloni amplification and followed by sequencing via multiple single-base-extension (FISSEQ). Poloni amplification is a method of amplifying DNA in situ on a polyacrylamide film. Representative poloni sequencing methods are described, for example, in US Patent Application Publication No. 2007/0087362.

Nano-transistor array-based devices such as Carbon NanoTube Field Effect Transistors (CNTFETs) can also be used for NGS. For example, DNA molecules are stretched and driven across nanotubes by micro-fabricated electrodes. DNA molecules come into sequential contact with the carbon nanotube surface, and a difference in current flow from each base is made due to charge transfer between the DNA molecule and the nanotube. DNA is sequenced by recording these differences. Representative nano-transistor array based sequencing methods are described, for example, in US Patent Publication No. 2006/0246497.

Scanning electron tunneling microscopy (STM) can also be used for NGS. STM forms an image of its surface using a piezo-electron-controlled probe that performs a raster scan of the specimen. STM can be used to image the physical properties of single DNA molecules, for example, by integrating an actuator-driven flexible gap with a scanning electron tunneling microscope, resulting in coherent electron tunneling imaging and spectroscopy. Representative sequencing methods using STM are described, for example, in US Patent Application Publication No. 2007/0194225.

Molecular-analysis devices consisting of nanowire-molecular sensors can also be used for NGS. Such devices can detect the interaction of nitrogenous substances disposed on nucleic acid molecules and nanowires such as DNA. Molecular guides are positioned to guide molecules near the molecular sensor to allow interaction and subsequent detection. Representative sequencing methods using nanowire-molecular sensors are described, for example, in US Patent Application Publication No. 2006/0275779.

Double-ended sequencing methods can be used for NGS. Double-ended sequencing uses blocking and unblocking primers to sequence both the sense and antisense strands of DNA. Typically, these methods include annealing an unblocked primer to the first strand of the nucleic acid; annealing a second blocking primer to the second strand of the nucleic acid; extending the nucleic acid along the first strand with a polymerase; terminating the first sequencing primer; deblocking the second primer; and extending the nucleic acid along the second strand. Representative double-stranded sequencing methods are described, for example, in US Pat. No. 7,244,567.

data analysis stage. After NGS reads are made, they are aligned or de novo assembled to a known reference sequence.

For example, identification of genetic modifications such as single-nucleotide polymorphisms and structural variants in a sample (e.g., a tumor sample) can be performed by aligning NGS reads to a reference sequence (e.g., a wild-type sequence). have. Sequence alignment methods for NGS are described, for example, in Trapnell C. and Salzberg S.L. Nature Biotech., 2009, 27:455-457.

Examples of de novo assemblies are described, for example, in Warren R. et al., Bioinformatics, 2007, 23:500-501; Butler J. et al., Genome Res., 2008, 18:810-820; and Zerbino. D.R. and Birney E., Genome Res., 2008, 18:821-829).

Sequence alignment or assembly can be performed using read data from one or more NGS platforms, for example by mixing Roche/454 and Illumina/Solexa read data. In the present invention, the alignment step is not limited thereto, but may be performed using the BWA algorithm and the hg19 sequence.

In the present invention, the step of confirming the position of the nucleic acid fragment of step (B) may preferably be characterized in that it is performed through sequence alignment, and the sequence alignment is read from the genome as a computer algorithm. The computational method or approach used for identity from where a sequence (e.g., from next-generation sequencing, e.g., a short-read sequence) is most likely derived by evaluating the similarity between a read sequence and a reference sequence. include Various algorithms can be applied to the sequence alignment problem. Some algorithms are relatively slow, but allow relatively high specificity. These include, for example, dynamic programming-based algorithms. Dynamic programming is a way to solve complex problems by breaking them down into simpler steps. Other approaches are relatively more efficient, but are typically not exhaustive. These include, for example, heuristic algorithms and probabilistic methods designed for large database searches.

Typically, there can be two steps in the alignment process: candidate screening and sequence alignment. Candidate screening reduces the search space for sequence alignments from the whole genome for a shorter enumeration of possible alignment positions. As the term suggests, sequence alignment involves aligning sequences with sequences provided in the candidate screening step. This can be done using a global alignment (eg, a Needleman-Wunsch alignment) or a local alignment (eg, a Smith-Waterman alignment).

Most attribute sorting algorithms can feature one of three types based on indexing methods: hash tables (e.g. BLAST, ELAND, SOAP), suffix trees (e.g. Bowtie, BWA), and merge sort. Algorithms based on (eg Slider). Short read sequences are typically used for alignment. Examples of sequence alignment algorithms/programs for short-read sequences include, but are not limited to, BFAST (Homer N. et al., PLoS One. 2009;4(11):e7767), BLASTN (on the World Wide Web). at blast.ncbi.nlm.nih.gov), BLAT (Kent W.J. Genome Res. 2002;12(4):656-64), Bowtie (Langmead B. et al., Genome Biol. 2009;10 (at blast.ncbi.nlm.nih.gov) 3):R25), BWA (Li H. and Durbin R. Bioinformatics, 2009, 25:1754-60), BWA-SW (Li H. and Durbin R. Bioinformatics, 2010;26(5):589-95) , CloudBurst (Schatz M.C. Bioinformatics. 2009;25(11):1363-9), Corona Lite (Applied Biosystems, Carlsbad, California, USA), CASHX (Fahlgren N. et al., RNA) , 2009; 15, 992-1002), CUDA-EC (Shi H. et al., J Comput Biol. 2010;17(4):603-15), ELAND (bioit.dbi.udel.edu on the World Wide Web) at /howto/eland), GNUMAP (Clement N.L. et al., Bioinformatics. 2010;26(1):38-45), GMAP (Wu T.D. and Watanabe C.K. Bioinformatics. 2005;21(9):1859-75), GSNAP (Wu T.D. and Nacu S., Bioinformatics. 2010;26(7):873-81), Geneious Assembler (Biomatters Ltd., Oakland, New Zealand), LAST, MAQ (Li H. et al. , Genome Res. 2008;18(11):1851-8), Mega -BLAST (at ncbi.nlm.nih.gov/blast/megablast.shtml on the World Wide Web), MOM (Eaves H.L. and Gao Y. Bioinformatics. 2009;25(7):969-70), MOSAIK (at bioinformatics.bc.edu/marthlab/Mosaik on the World Wide Web), Novoalign (on the World Wide Web at novocraft.com/main/index.php) in), PALMapper (at fml.tuebingen.mpg.de/raetsch/suppl/palmapper on the World Wide Web), PASS (Campagna D. et al., Bioinformatics. 2009;25(7):967-8 ), PatMaN (Prufer K. et al., Bioinformatics. 2008; 24(13):1530-1), PerM (Chen Y. et al., Bioinformatics, 2009, 25 (19): 2514-2521), ProbeMatch ( Kim Y.J. et al., Bioinformatics. 2009;25(11):1424-5), QPalma (de Bona F. et al., Bioinformatics, 2008, 24(16): i174), RazerS (Weese D. et al. , Genome Research, 2009, 19:1646-1654), RMAP (Smith A.D. et al., Bioinformatics. 2009;25(21):2841-2), SeqMap (Jiang H. et al. Bioinformatics. 2008;24:2395) -2396.), Shrec (Salmela L., Bioinformatics. 2010;26(10):1284-90), SHRiMP (Rumble S.M. et al., PLoS Comput. Biol., 2009, 5(5):e1000386), SLIDER (Malhis N. et al., Bioinformatics, 2009, 25 (1): 6-13), SLIM Search (Muller T. et al., Bioinformatics. 2001;17 Suppl 1:S182-9), SOAP (Li R. et al. , Bioinformatics. 2008;24(5):713-4), SOAP2 (Li R. et al., Bioinformatics. 2009;25(15):1966-7), SOCS (Ondov B.D. et al., Bioinformatics, 2008; 24(23) ):2776-7), SSAHA (Ning Z. et al., Genome Res. 2001;11(10):1725-9), SSAHA2 (Ning Z. et al., Genome Res. 2001;11(10): 1725-9), Stampy (Lunter G. and Goodson M. Genome Res. 2010, epub ahead of print), Taipan (at taipan.sourceforge.net on the World Wide Web), UGENE (World Wide On the web at ugene.unipro.ru), XpressAlign (on the World Wide Web at bcgsc.ca/platform/bioinfo/software/XpressAlign), and ZOOM (Bioinformatics Solutions, Inc., Waterloo, Ontario, Canada) Inc.)).

A sequence alignment algorithm may be selected based on a number of factors including, for example, sequencing technique, read length, number of reads, available computing resources, and sensitivity/scoring requirements. Different sequence alignment algorithms can achieve different speed levels, alignment sensitivity and alignment specificity. Alignment specificity refers to the percentage of target sequence residues aligned as found in the submission that are correctly aligned compared to the predicted alignment. Alignment sensitivity also refers to the percentage of target sequence residues aligned as found in normally predicted alignments that are correctly aligned in submission.

Alignment algorithms such as ELAND or SOAP can be used for the purpose of aligning short reads (eg, from Illumina/Solexa sequencers) against a reference genome when speed is the first factor to be considered. Alignment algorithms such as BLAST or Mega-BLAST use shorter reads (e.g., from Roche FLX), although these methods are relatively slower when specificity is the most important factor, for the purpose of similarity investigations. can be used Alignment algorithms such as MAQ or Novoalign take the quality score into account and thus can be used for single- or paired-end data when accuracy is essential (e.g. in fast-mass SNP searches). ). Alignment algorithms such as Bowtie or BWA use the Burrows-Wheeler Transform (BWT) and thus require a relatively small memory footprint. Alignment algorithms such as BFAST, PerM, SHRiMP, SOCS or ZOOM map the color space reads and thus can be used with ABI's SOLiD platform. In some applications, results from two or more sorting algorithms may be combined.

In the present invention, the length of the sequence information (reads) in step (B) is 5 to 5000 bp, and the number of sequence information used may be 50 to 5 million, but is not limited thereto.

In the present invention, the step of grouping the sequence information in step (C) may be performed based on the adapter sequence of the sequence information (reads). The FD value may be calculated for the selected sequence information by separately dividing the nucleic acid fragment aligned in the forward direction and the nucleic acid fragment aligned in the reverse direction, or the FD value may be calculated for the entire group.

In the present invention, it may be characterized in that it further comprises the step of separately classifying the nucleic acid fragments satisfying the mapping quality score of the aligned nucleic acid fragments prior to performing the step (C).

In the present invention, the mapping quality score may vary depending on a desired criterion, but may be preferably 15-70 points, more preferably 50-70 points, and most preferably 60 points.

In the present invention, the FD value of step (D) is the distance between the reference value of any one or more nucleic acid fragments selected from the i-th nucleic acid fragment and the i+1 to n-th nucleic acid fragment with respect to the obtained n nucleic acid fragments. It can be characterized as being defined.

In the present invention, the FD value is calculated by calculating the distance from the reference value of any one or more nucleic acid fragments selected from the group consisting of the first nucleic acid fragment and the second to n-th nucleic acid fragments with respect to the obtained n nucleic acid fragments. one or more values selected from the group consisting of sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation and coefficient of variation and/or one or more of these The reciprocal value of , a calculation result including a weight, and a statistical value, but not limited thereto, may be used as the FD value, but the present invention is not limited thereto.

In the present invention, the expression “one or more values and/or one or more inverse values thereof” is interpreted to mean that one or two or more of the numerical values described above may be used in combination.

In the present invention, the “reference value of the nucleic acid fragment” may be a value obtained by adding or subtracting an arbitrary value from the median value of the nucleic acid fragment.

The FD value can be defined as follows for the obtained n nucleic acid fragments.

FD = Dist(Ri~Rj) (1<i<j<n),

Here, the Dist function is the sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance , one or more values selected from the group consisting of standard deviation, median absolute deviation, and coefficient of variation, and/or one or more inverse values thereof, and calculation results including weights and statistics, but not limited thereto.

개의 핵산 단편 간 거리 조합이 가능하다. 즉, i가 1일 경우, i+1은 2가 되어 2 내지 n 번째 핵산 단편에서 선택되는 어느 하나 이상의 핵산 단편과의 거리를 정의 할 수 있다.That is, in the present invention, the FD value (Fragment Distance Value) refers to the distance between aligned nucleic acid fragments. Here, the number of cases of selection of nucleic acid fragments for distance calculation can be defined as follows. When there are a total of N nucleic acid fragments

Any combination of distances between nucleic acid fragments is possible. That is, when i is 1, i+1 becomes 2 to define a distance from any one or more nucleic acid fragments selected from the 2nd to nth nucleic acid fragments.

In the present invention, the FD value may be characterized by calculating a distance between a specific position inside the i-th nucleic acid fragment and a specific position inside any one or more of the i+1 to n-th nucleic acid fragments.

For example, if a nucleic acid fragment has a length of 50 bp and is aligned at position 4,183 on chromosome 1, the genetic position value that can be used to calculate the distance of this nucleic acid fragment is 4,183 to 4,232 on chromosome 1.

When the nucleic acid fragment having a length of 50 bp adjacent to the nucleic acid fragment is aligned at the 4,232th position of chromosome 1, the genetic position value that can be used for calculating the distance of this nucleic acid fragment is 4,232 to 4,281 of chromosome 1, and the FD between the two nucleic acid fragments The value can be from 1 to 99.

When another adjacent 50bp nucleic acid fragment is aligned at position 4123 of chromosome 1, the genetic position value that can be used to calculate the distance of this nucleic acid fragment is 4,123 to 4,172 of chromosome 1, and the FD value between the two nucleic acid fragments is 61 to 159, and the FD value with the first exemplary nucleic acid fragment is 12 to 110, the sum, difference, product, mean, logarithm of the product, logarithm of the sum, median, quantile, minimum, One or more values selected from the group consisting of maximum value, variance, standard deviation, median absolute deviation, and coefficient of variation and/or one or more inverse values thereof, and calculation results including but not limited to weights and statistics are used as FD values. may be, and preferably, it may be characterized as the reciprocal value of one value of the two FD value ranges, but is not limited thereto

Preferably, in the present invention, the FD value may be a value obtained by adding or subtracting an arbitrary value from the median value of the nucleic acid fragment.

In the present invention, the median value of FD means a value located at the center when the calculated FD values are arranged in the order of magnitude. For example, when there are three values such as 1, 2, 100, 2 is the median because 2 is the most central. If there are an even number of FD values, the median is determined by the average of the two middle values. For example, if there are FD values of 1, 10, 90, and 200, the median value is 50, which is the average of 10 and 90.

In the present invention, any of the above values can be used without limitation as long as they can indicate the position of the nucleic acid fragment, but preferably 0 to 5 kbp or 0 to 300% of the length of the nucleic acid fragment, 0 to 3 kbp or the length of the nucleic acid fragment. It may be 0 to 200%, 0 to 1 kbp, or 0 to 100% of the length of the nucleic acid fragment, more preferably 0 to 500 bp, or 0 to 50% of the length of the nucleic acid fragment, but is not limited thereto.

In the present invention, in the case of paired-end sequencing, the FD value may be derived based on position values of forward and reverse sequence information (reads).

For example, in a pair of 50 bp long paired-end reads, if the forward read is aligned at position 4183 of chromosome 1 and the reverse read is aligned at position 4349, both ends of this nucleic acid fragment become 4183 and 4349, A reference value that can be used for the nucleic acid fragment distance is 4183 to 4349. At this time, in another paired-end read pair adjacent to the nucleic acid fragment, when the forward read is aligned at the 4349th position of chromosome 1 and the reverse read is aligned at the 4515th position, the position value of the nucleic acid fragment is 4349 to 4515. The distance between these two nucleic acid fragments may be 0-333, and most preferably, the distance of the median value of each nucleic acid fragment may be 166.

In the present invention, when sequence information is obtained by the paired-end sequencing, in the case of a nucleic acid fragment having an alignment score of sequence information (reads) less than a reference value, the step of excluding from the calculation process may be further included. have.

In the present invention, in the case of single-end sequencing, the FD value may be derived based on one type of position value of forward or reverse sequence information (read).

In the present invention, in the case of the single-ended sequencing, when deriving a position value based on sequence information aligned in the forward direction, an arbitrary value is added, and when deriving a position value based on sequence information aligned in the reverse direction may be characterized by subtracting an arbitrary value, and the arbitrary value can be used without limitation as long as the FD value clearly indicates the position of the nucleic acid fragment, but preferably 0 to 5 kbp or the length of the nucleic acid fragment. 0 to 300%, 0 to 3 kbp or 0 to 200% of the length of the nucleic acid fragment, 0 to 1 kbp or 0 to 100% of the length of the nucleic acid fragment, more preferably 0 to 500 bp or 0 to 50% of the length of the nucleic acid fragment , but is not limited thereto.

Nucleic acids to be analyzed in the present invention may be sequenced and expressed in units called reads. These reads can be divided into single end sequencing reads (SE) and paired end sequencing reads (PEs) according to a sequencing method. SE-type read means that one of 5' and 3' of a nucleic acid molecule is sequenced for a certain length in a random direction, and PE-type read sequenced both 5' and 3' by a certain length. Because of this difference, it is well known to those skilled in the art that when sequencing in SE mode, one read is generated from one nucleic acid fragment, and in PE mode, two reads are generated from one nucleic acid fragment in pairs.

The ideal way to calculate the exact distance between nucleic acid fragments is to sequence the nucleic acid molecules from beginning to end, align the grid, and use the median (center) of the aligned values. However, technically, the above method has limitations due to limitations and cost aspects of sequencing technology. Therefore, sequencing is performed in the same way as SE and PE. In the PE method, the start and end positions of nucleic acid molecules can be known, so the exact position (median value) of the nucleic acid fragment can be determined through the combination of these values, but SE In the case of the method, there is a limitation in calculating the exact location (median) because only information from one end of the nucleic acid fragment can be used.

In addition, when calculating the distance of a nucleic acid molecule using the end information of all reads that have been sequenced (aligned) in both forward and reverse directions, inaccurate values may be calculated due to the factor of sequencing direction.

Therefore, for technical reasons of the sequencing method, the 5' end of the forward read has a smaller position than the central position of the nucleic acid molecule, and the 3' end of the reverse read has a larger value. Using this feature, if an arbitrary value (Extended bp) is added to the forward read and subtracted from the reverse read, a value close to the central position of the nucleic acid molecule can be estimated.

That is, the arbitrary value (Extended bp) may vary depending on the sample used, and in the case of cell-free nucleic acids, since the average length of the nucleic acid is known to be about 166 bp, it can be set to about 80 bp. If the experiment is performed through fragmentation equipment (ex; sonication), about half of the target length set in the fragmentation process can be set as extended bp.

In the present invention, the step of determining the chromosome abnormality in step (E) is

(E-i) determining a representative value (RepFD) of the FD value for the entire chromosome region or for each specific region;

(E-ii) Sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deriving a normalized factor by calculating one or more values selected from the group consisting of deviation, median absolute deviation, and coefficient of variation and/or one or more inverse values thereof;

(E-iii) calculating a representative value ratio (RepFD ratio) based on Equation 1 below;

Equation 1: RepFD ratio = RepFD Target genomic region / Normalized Factor

(E-iv) Comparing the RepFD ratio value of the normal reference group and the sample, calculating FDI (Fragments Distance Index):

It may be characterized in that it is carried out including

In the present invention, the representative value (RepFD) of step (E-i) is the sum, difference, product, mean, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation and coefficient of variation of the FD values. It may be characterized as one or more values selected from the group and/or one or more reciprocal values thereof, and preferably it may be characterized as a median value, an average value, or a reciprocal value of the FD values, but is not limited thereto.

In the present invention, the entire chromosome region or a specific genetic region can be used without limitation as long as it is a set of human nucleic acid sequences, but may preferably be a chromosomal unit or a specific region of some chromosomes, for example, a specific region for detecting numerical abnormalities. The region may be an autosomal that is considered to be euploid, and may be any genetic region except for regions with low uniqueness (centromere, telomere) in a specific region for detecting structural abnormalities, but is not limited thereto.

A specific region in the sample other than the entire chromosome region or specific genetic region to be analyzed in step (E-ii) is

a) randomly selecting an entire region of a chromosome to be analyzed or a region other than a specific genetic region;

b) determining a representative value of the RepFD value of the genetic region selected in step a) as a pre-normalized factor (PNF);

c) calculating a representative value ratio (RepFD ratio) based on Equation 2:

Equation 2: RepFD ratio = RepFD Target genomic region / PNF

d) calculating a coefficient of variation (Coefficient of Variance: SD / Mean) of the RepFD ratio value of a normal reference group; and

e) selecting by a method comprising the step of determining the genetic region having the smallest value among the coefficients of variation obtained by repeatedly performing steps a) to d) as the entire chromosome region or a specific region in the sample other than the specific genetic region can be characterized.

In the present invention, the repeated execution of step e) may be characterized as being 100 or more times, preferably between 10,000 and 1 million times, and most preferably 100,000 times, but is not limited thereto.

In the present invention, the step (E-iv) may be characterized in that the RepFD ratio value of the normal reference group is compared with the RepFD ratio value of the sample.

In the present invention, the method of comparing the RepFD ratio value of the normal reference group and the RepFD ratio of the sample can be used without limitation as long as it is a method that can confirm that the two values are statistically significantly different, but preferably average and A standard deviation-based Z-score, a median-based log ratio, or a likelihood ratio calculated through other classification algorithms may be selected, and most preferably, the Z-score calculation method based on the mean and standard deviation may be, but is not limited thereto.

In the present invention, the Fragments Distance Index is calculated by comparing the Rep FD ratio value of the normal reference group and the sample to be analyzed. A standard score method such as the Z score can be used for the comparison method, and the threshold is An integer or range such as infinity of positive and negative numbers is possible, and may be preferably 3, but is not limited thereto.

In another aspect, the present invention provides a decoding unit for decoding sequence information by extracting nucleic acids from a biological sample;

an alignment unit that aligns the translated sequence to a standard chromosomal sequence database; and

By measuring the distance between the aligned nucleic acid fragments with respect to the selected nucleic acid fragments, the FD value (Fragments Distance) is calculated, and the FDI value (Fragments Distance) for the entire chromosome region or for each specific genetic region based on the calculated FD value Index) and, when the FDI value is less than or greater than a reference value or section, relates to a chromosome abnormality detection apparatus comprising a chromosome abnormality determining unit that determines that there is a chromosome abnormality.

In another aspect, the present invention provides a computer-readable storage medium comprising instructions configured to be executed by a processor for detecting a chromosomal abnormality,

(A) extracting a nucleic acid from a biological sample to obtain a nucleic acid fragment to obtain sequence information;

(B) aligning the nucleic acid fragment to a standard chromosome sequence database (reference genome database) based on the obtained sequence information (reads);

(C) measuring the distance between the selected nucleic acid fragments (fragments), calculating the FD value (Fragments Distance); and

(D) The FDI value (Fragments Distance Index) is calculated for the entire chromosome region or for each specific genetic region based on the FD value calculated in step (C). determining that there is;

It relates to a computer-readable storage medium comprising instructions configured to be executed by a processor that detects a chromosomal abnormality through a chromosomal aberration.

Specifically, the computer-readable storage medium according to the present invention includes instructions configured to be executed by a processor for detecting a chromosomal abnormality,

(A) extracting a nucleic acid from a biological sample to obtain a nucleic acid fragment to obtain sequence information;

(B) aligning the nucleic acid fragment to a standard chromosome sequence database (reference genome database) based on the obtained sequence information (reads);

(C) grouping the aligned nucleic acid fragments into full sequence, forward sequence and reverse sequence based on the sequence information (reads);

(D) calculating the FD value (Fragments Distance) for each group by measuring the distance between the reference values of the aligned nucleic acid fragments for each of the grouped nucleic acid fragments; and

(E) Calculate each FDI value (Fragments Distance Index) for the entire chromosome region or for each specific region based on the FD value for each group calculated in step (D), so that each FDI value is less than or exceeding the reference value range , determining that there is a chromosomal abnormality;

It may be characterized by including, but not limited to, instructions configured to be executed by a processor for detecting a chromosomal abnormality.

In another embodiment of the present invention, it was confirmed that the distance between the reads can be calculated by adding or subtracting 50% of the average length of the analyte nucleic acid fragment from the median value of the aligned nucleic acid fragments to calculate the position values of both ends of the read ( 6).

Accordingly, the present invention in another aspect,

(A) extracting nucleic acids from a biological sample to obtain sequence information;

(B) aligning the obtained sequence information (reads) to a standard chromosome sequence database (reference genome database);

(C) calculating the RD value (Read Distance) by measuring the distance between the aligned reads with respect to the aligned sequence information (reads); and

(D) If the RDI value (Read Distance Index) is calculated for the entire chromosome region or for each specific region based on the RD value calculated in step (C), and the RDI value is less than or exceeding the reference value range, there is a chromosome abnormality It relates to a chromosomal abnormality detection method comprising the step of determining that it is.

In the present invention,

Step A) is

(A-i) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, amniotic fluid including oral cells, placental cells or fetal cells, tissue cells, and mixtures thereof;

(A-ii) removing proteins, fats, and other residues from the collected nucleic acids using a salting-out method, a column chromatography method, or a beads method; obtaining purified nucleic acids;

(A-iii) single-end sequencing or pair-end sequencing for purified nucleic acids or nucleic acids randomly fragmented by enzymatic digestion, pulverization, or hydroshear method end sequencing) preparing a library;

(A-iv) reacting the prepared library with a next-generation sequencer; and

(A-v) it may be characterized in that it is carried out in a method comprising the step of obtaining sequence information (reads) of nucleic acids in a next-generation gene sequencing machine.

In the present invention, the length of the sequence information (reads) in step (B) is 5 to 5000 bp, and the number of sequence information used may be 50 to 5 million, but is not limited thereto.

In the present invention, the step (C) may be characterized in that the step of grouping the aligned leads according to the aligned direction can be further used.

In the present invention, the step of grouping the reads may be additionally used, and in this case, the grouping criterion may be performed based on the adapter sequence of the aligned reads. The RD value can be calculated for the sequence information selected by separately dividing the reads aligned in the forward direction and the reads aligned in the reverse direction.

In the present invention, it may be characterized in that it further comprises the step of separately classifying the reads satisfying the alignment matching score (mapping quality score) of the aligned reads prior to performing the step (C).

In the present invention, the mapping quality score may vary depending on a desired criterion, but may be preferably 15-70 points, more preferably 50-70 points, and most preferably 60 points.

In the present invention, the RD value of step (C) is a nucleic acid at one of the values of both ends of the i-th read and any one or more reads selected from i+1 to n-th read with respect to the n reads obtained. It may be characterized in that it is calculated through the distance between values added or subtracted by 50% of the average length.

In the present invention, the RD value is calculated by calculating the distance between the first read and any one or more reads selected from the group consisting of the second to n-th reads with respect to the obtained n reads, and the sum, difference, and product thereof , mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deviation, median absolute deviation, coefficient of variation, reciprocal values and combinations thereof, calculation results including but not limited to weights, and statistics may be used as the RD value, but is not limited thereto.

In the present invention, the median value of RD means a value located at the center when the calculated RD values are arranged in the order of magnitude. For example, when there are an odd number of values such as 1, 2, 100, 2 is the median because 2 is the most central. If there are an even number of RD values, the median is determined by the average of the two middle values. For example, if there are RD values of 1, 10, 90, and 200, the median is 50, which is the average of 10 and 90.

In the present invention, the RD value is characterized by calculating the distance between the 5' or 3' end inside the i-th read and the 5' or 3' end of any one or more of the i+1 to n-th reads. can

For example, in a pair of 50 bp long paired-end reads, if the forward read is aligned at position 4183 of chromosome 1 and the reverse read is aligned at position 4349, both ends of this nucleic acid fragment become 4183 and 4349, A reference value that can be used for the nucleic acid fragment distance is 4183 to 4349. At this time, in another paired-end read pair adjacent to the nucleic acid fragment, when the forward read is aligned at the 4349th position of chromosome 1 and the reverse read is aligned at the 4515th position, the position value of the nucleic acid fragment is 4349 to 4515. The distance between these two nucleic acid fragments may be 0-333, and most preferably, the distance of the median value of each nucleic acid fragment may be 166. In the above example, when the average length of the nucleic acid fragment is 166, when 50% of the average length of the nucleic acid fragment is subtracted from the median value (4266), the position value of the first nucleic acid fragment is 4183, the position value of the second nucleic acid fragment is 4349, , at this time, the distance between the leads becomes 166 (4349 - 4183). On the other hand, when 50% is added from the median value, the position value of the first nucleic acid fragment is 4349 and the position value of the second nucleic acid fragment is 4515, and the distance between the reads is 166 (4515 - 4349).

In the present invention, the step of determining the chromosome abnormality in step (D) is

(D-i) determining a representative value of the RD value (RepRD) for each entire chromosome region or a specific genetic region;

(D-ii) Sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deriving a normalized factor by calculating one or more values selected from the group consisting of deviation, median absolute deviation, and coefficient of variation and/or one or more inverse values thereof;

(D-iii) calculating a representative value ratio (RepRD ratio) based on the following Equation 10;

Equation 10: RepRD ratio = RepRD Target genomic region / Normalized Factor

(D-iv) Comparing the RepRD ratio value of the normal reference group and the sample, calculating RDI (Read Distance Index):

It may be characterized in that it is carried out including

In the present invention, the representative value (RepRD) of step (D-i) is the sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deviation, median of the RD values. One or more values selected from the group consisting of absolute deviations and coefficients of variation and/or one or more inverse values thereof, and one or more selected from the group consisting of, but not limited to, statistics, preferably the RD values It may be characterized as a median value, an average value, or a reciprocal value thereof, but is not limited thereto.

In the present invention, the median value of RepRD means a value located at the most center when the calculated RepRD values are arranged in order of size. For example, when there are an odd number of values such as 1, 2, 100, 2 is the median because 2 is the most central. If there are an even number of RepRD values, the median is determined by the average of the two middle values. For example, if there are RepRD values of 1, 10, 90, and 200, the median value is 50, which is the average of 10 and 90.

In the present invention, the entire chromosome region or a specific genomic region can be used without limitation as long as it is a set of human nucleic acid sequences, but may preferably be a chromosomal unit or a specific region of some chromosomes, for example, numerical abnormalities A specific region for detecting whether or not there is an autosomal that is considered to be euploid, and a specific region for detecting a structural abnormality may be any genetic region except for a region with low uniqueness (centromere, telomere), but limited to this it's not going to be

A specific region in the sample other than the entire chromosome region or a specific genetic region to be analyzed in step (D-ii) is

a) randomly selecting an entire region of a chromosome to be analyzed or a region other than a specific genetic region;

b) determining a representative value of the RepRD value of the genetic region selected in step a) as a pre-normalized factor (PNF);

c) calculating a representative value ratio (RepRD ratio) based on Equation 11 below:

Equation 11: RepRD ratio = RepRD Target genomic region / PNF

d) calculating a coefficient of variation (Coefficient of Variance: SD / Mean) of the RepRD ratio value of a normal reference group; and

e) a method comprising the step of determining the genetic region having the smallest value among the coefficients of variation obtained by repeating steps a) to d) as the entire chromosome region or a region outside of a specific genetic region can

In the present invention, the repeated execution of step e) may be characterized as being 100 or more times, preferably between 10,000 and 1 million times, and most preferably 100,000 times, but is not limited thereto.

In the present invention, the step (iv) may be characterized in that the RepRD ratio value of the normal reference group is compared with the RepRD ratio value of the sample.

In the present invention, the method of comparing the RepRD ratio value of the normal reference group and the RepRD ratio of the sample can be used without limitation as long as it is a method that can confirm that the two values are statistically significantly different, but preferably the average and A standard deviation-based Z-score, a median-based log ratio, or a likelihood ratio calculated through other classification algorithms may be selected, and most preferably, the Z-score calculation method based on the mean and standard deviation may be, but is not limited thereto.

In the present invention, the Reads Distance Index is calculated by comparing the Rep RD ratio value of the normal reference group and the sample to be analyzed. A standard score method such as the Z score can be used for the comparison method, and the threshold is An integer or range such as infinity of positive or negative numbers is possible, and may preferably be -3 or 3, but is not limited thereto.

In another aspect, the present invention provides a decoding unit for extracting nucleic acids from a biological sample and deciphering sequence information;

an alignment unit that aligns the translated sequence to a standard chromosomal sequence database; and

By measuring the distance between reads aligned with respect to the selected sequence information (reads), calculating the RD value (Read Distance), and calculating the RDI value (Read Distance Index) for each genetic region based on the calculated RD value, To a chromosomal abnormality detecting apparatus comprising a chromosomal abnormality determining unit that determines that there is a chromosomal abnormality when the RDI value is less than or greater than a reference value or section.

In another aspect, the present invention provides a computer-readable storage medium comprising instructions configured to be executed by a processor for detecting a chromosomal abnormality,

(A) extracting nucleic acids from a biological sample to obtain sequence information;

(B) aligning the obtained sequence information (reads) to a standard chromosome sequence database (reference genome database);

(C) calculating the RD value (Read Distance) by measuring the distance between the reads aligned with respect to the selected sequence information (reads); and

(D) Calculating the RDI value (Read Distance Index) for each genetic region based on the RD value calculated in step (C), and when the RDI value is less than or exceeding the reference value range, determining that there is an abnormality in the chromosome To a computer-readable storage medium comprising instructions configured to be executed by a processor for detecting an abnormality in a chromosome, through the steps.

Example

Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not to be construed as being limited by these examples.

Example 1. Extracting DNA from blood, performing next-generation sequencing

The blood sample was collected by 10mL and stored in an EDTA tube. Within 2 hours after collection, only the plasma part was first centrifuged under the conditions of 1200g, 4℃ for 15 minutes, and then the first centrifuged plasma was collected at 16000g, 4℃ for 15 minutes. Plasma supernatant except for the precipitate was separated by second centrifugation under the conditions of Cell-free DNA was extracted from the isolated plasma using the Tiangenmicro DNA kit (Tiangen). For paired-end (PE) data generation, the library preparation process was performed using the MGIEasy Cell-free DNA Library Prep set kit (MGI), and the DNBseq G400 equipment (MGI) was used (50cycle * 2), single-end (SE) Data were produced using Nextseq500 (Illumina) equipment after library preparation using Truseq Nano DNA HT library prep kit (Illumina).

For PE data, sequence information for about 10 million nucleic acid fragments could be obtained, and for SE data, sequence information for about 1.3 million nucleic acid fragments could be obtained.

Example 2. Quality Control of Sequence Information Data and FD Value Calculation

The sequence information was pre-processed and the following series of procedures were performed before calculating the FD value. The library sequence was aligned based on the Hg19 sequence of the reference chromosome using the BWA-mem algorithm in the fastq format file generated by the next-generation sequencing (NGS) equipment. Since there is a possibility that an error may occur when aligning the library sequence, two processes were performed to correct the error. First, the duplicated library sequences were removed, and then sequences that did not reach 60 Mapping Quality Score among the library sequences aligned by the BWA-mem algorithm were removed.

The selected reads were grouped into forward and reverse reads according to the aligned directions, and the distance to the nearest read was calculated using Equation 3 below as an FD value, and the concept thereof is shown in FIGS. 2 and 3 . The D function of Equation 3 below is a function for calculating the difference value of the dielectric position. In Equation 3, a and b are the position values of the nucleic acid fragment, and in the case of PE sequencing, it may be any one of the minimum to the maximum value of the aligned position values of the two sequence information, and in the case of SE sequencing, the alignment of the sequence information It may be a position value or a value obtained by extending a specific value to a position value.

Equation 3: Fragment Distance (FD) = D(a,b) | a ∈Fi , b ∈Fi

Example 3. Confirmation of difference in FD value according to extension

Data produced by PE can know the position information of the start and end of the nucleic acid fragment, and the distance between each nucleic acid fragment can be calculated based on the intermediate position. In the data produced by PE, randomly grouped into For and Rev reads. For leads classified as For, the FD is calculated based on the 5' position of the For lead, and the Rev classified read is based on the 3' position of the Rev read. Then, extension was performed by adding 80 bp to the For read and subtracting 80 bp to the Rev read.

As a result of comparing the difference between the FD value of the above process and the FD value of the process of performing extension, it was confirmed that the FD value calculated after extension was similar to the centered FD value of PE as shown in FIG. 5, and extension was performed It was confirmed that there is a difference between the FD values of +166 and -166 for the FD values that are not.

Example 4. FDI Value Calculation

4-1. FDI Value Calculation for Chromosomal Numerical Abnormality Detection

The FDI value was calculated using the SE sequencing data, and the extension value was set to 80bp. For chromosomes to be checked for aneuploidy, the median ratio of the RepFD values of each selected chromosome set was defined as the Normalized Factor, and calculated by Equation 4 below.

Chromosomes to identify aneuploidies chromosome set 13 1,2,3,4,5,6,7,8,9,10,11,12,14,15,16,17,19,20,22 18 1,2,3,4,5,6,7,8,9,10,11,12,14,15,16,17,19,20,22 21 1,2,3,4,5,6,7,8,9,10,11,12,14,15,16,17,19,20,22

Equation 4: Normalized Factor = Median of RepFD _{selected chromosome set}

(selected chromosome set: the part corresponding to the chromosome set in the table above)

Using the FD and Normalized Factor calculated in Equations 3 and 4, the RepFD ratio was calculated in Equation 5.

Equation 5: RepFD ratio = RepFD _{Target chromosome} / Normalized Factor

The mean and standard deviation values of the RepFD ratio were calculated in a reference group of 2000 normal persons, and the Fragments Distance Index (FDI) value of the sample to be analyzed was calculated using Equation 6.

Equation 6: FDI = (MEAN(RepFD Ratio _reference - RepFD Ratio _sample -) / SD(RepFD Ratio _reference ))

The procedure of Equation 6 was performed when all nucleic acid fragments were used (Equation 7), when using nucleic acid fragments aligned in the forward direction (Equation 8), and when using nucleic acid fragments aligned in the reverse direction (Equation 9). .

Equation 7: FDI ^all = mean(RepFD ^all Ratio _reference ) - RepFD ^all Ratio _sample / SD(RepFD ^all Ratio _reference )

Equation 8: FDI ^For = mean(RepFD ^For Ratio _reference ) - RepFD ^For Ratio _sample / SD(RepFD ^For Ratio _reference )

Equation 9: FDI ^Rev = mean(RepFD ^Rev Ratio _reference ) - RepFD ^Rev Ratio _sample / SD(RepFD ^Rev Ratio _reference )

4-2. Confirmation of the performance of FDI values for detecting chromosomal numerical abnormalities

As a result of analysis of 88 clinical samples including 2,000 samples from the normal standard group and 6 samples from Trisomy, 100% sensitivity and 100% specificity were confirmed.

3 was used as the threshold value for positive discrimination for FDI21 ^all , FDI21 ^For , and FDI21 ^Rev.

For each sample, three FDI values are calculated for positive discrimination, and when all of them are 3 or more, it is determined as final positive. Of the 88 samples analyzed, 3 samples (G19NIPT261-3, G19NIPT261-10, G19NIPT261-13) were determined to be positive in one FDI value, but the remaining two FDI values were determined to be negative, and ultimately negative.

sample Result FDI21.All RDI21.For FDI21.rev G19NIPT264-22 T21 13.5189 18.9706 22.7449 G19NIPT261-27 T21 11.3797 15.7714 16.3134 G19NIPT262-11 T21 9.8365 13.4254 12.6415 G19NIPT263-21 T21 9.6024 13.3521 13.8184 G19NIPT264-29 T21 9.3665 12.3367 14.4694 G19NIPT261-3 T21 6.2652 7.9757 8.6841 G19NIPT263-12 N 2.8936 3.5516 1.5855 G19NIPT263-10 N 1.9594 2.6681 3.1888 G19NIPT263-13 N 1.7782 2.0879 3.8235

Example 5. Extracting DNA from blood for RD value-based analysis, performing next-generation sequencing

Blood from 400 normal subjects, 175 Trisomy 21, 67 Trisomy 18, and 26 Trisomy 13 was collected at 10 mL each and stored in an EDTA tube. After separation, the first centrifuged plasma was subjected to a second centrifugation under the conditions of 16000 g and 4° C. for 10 minutes to separate the plasma supernatant except for the precipitate. Cell-free DNA was extracted from the separated plasma using the Tiangenmicro DNA kit (Tiangen), and library preparation was performed using the Truseq Nano DNA HT library prep kit (Illumina), and then the Nextseq500 equipment (Illumina) was used for 75 Single Sequencing was performed in -end mode.

As a result, it was confirmed that about 13 million reads were produced per sample.

Example 6. Quality control of sequence information data and calculation of RD value

The sequence information was pre-processed and the following series of procedures were performed before calculating the RD value. The Bcl file (including sequencing information) generated by the next-generation sequencing (NGS) equipment was converted into fastq format, and the library sequence was aligned based on the reference chromosome Hg19 sequence using the fastq file using the BWA-mem algorithm. Since there is a possibility that an error may occur when aligning the library sequence, two processes were performed to correct the error. First, the overlapping library sequences were removed, and then sequences that did not reach 60 Mapping Quality Score among the library sequences aligned by the BWA-mem algorithm were removed.

After the selected reads were grouped into forward and reverse reads according to the aligned directions, the distance to the nearest read was calculated using Equation 12 below as the RD value, and the concept thereof is shown in FIG. 7 . The D function of 12 below is a function for calculating the difference value of the dielectric position.

Equation 12: Read Distance (RD) = D(a,b) | a ∈Ri , b ∈Fi

Example 7. RDI Value Calculation

7-1. RDI Value Calculation for Chromosomal Numerical Abnormality Detection

The median RD value for each chromosome was defined as RepRD. The median ratio of the RepRD values of each selected chromosome set for the chromosome to be checked for aneuploidy was defined as the Normalized Factor, and calculated by Equation 13 below.

Chromosomes to identify aneuploidies chromosome set 13 4,6 18 5,8 21 2,4,14,20

Equation 13: Normalized Factor = Median of RepRD _{selected chromosome set}

(selected chromosome set: the part corresponding to the chromosome set in the table above)

Using the RD and Normalized Factor calculated in Equations 12 and 13, the RepRD ratio was calculated in Equation 14.

Equation 14: RepRD ratio = RepRD _{Target chromosome} / Normalized Factor

The mean and standard deviation values of the RepRD ratio were calculated in a reference group of 400 normal people, and the Reads Distance Index (RDI) value of the sample to be analyzed was calculated using Equation 15.

Equation 15: RDI = RepRD Ratio _sample - MEAN(RepRD Ratio _reference ) / SD(RepRD Ratio _reference )

7-2. RDI Value Calculation for Chromosomal Structural Abnormalities

After uniformly dividing the chromosome into 50 kbase, the median RD value for each region was defined as RepRD. Also, as the Normalized Factor, the median value of autosomal RD values was used. The reference group used data from 437 normal women, and the mean and standard deviation of RepRD Ratio were calculated for each region of the chromosome. The RDI value was calculated by quoting Equation 15.

7-3. Check the performance according to the RD representative value (RepRD) calculation method (using the reciprocal of the median value)

After calculating the RD values of sequence information aligned in each genetic region (per chromosome), the reciprocal of the median of these values was defined as the RD representative value (RepRD). Here, the median value means a value located at the center when the calculated RD values are arranged in the order of their sizes. For example, when there are three values such as 1,2,100, 2 is the median because 2 is the most central.

If there is an even number of RD values, the median is determined by the average of the two values in the middle. For example, if there are RD values of 1,10,90,200, 50, the average of 10 and 90 located in the center, becomes the median .. 49 samples confirmed as Trisomy 21 and 3,448 samples confirmed as normal were used for the analysis samples, and the RepRD value was the reciprocal of the median of the RD values. As for the analysis method, the RDI value was calculated using the Z-score method using the average and standard deviation of the RepRD values of 3,448 normal samples. As a result of the analysis, it was possible to detect whether the sample had an abnormality in the number of chromosomes with an accuracy of about 0.999 (Table 4, FIG. 15).

Chromosome Accuracy (95% CI) Sensitivity Specificity AUC T21 0.9994 (0.9979, 0.9999) 0.9795 0.9997 1.0000

7-4. Performance check according to RD representative value (RepRD) calculation method (using average)

After calculating the RD values of the sequence information aligned in each genetic region (for each chromosome), the average value of these values was defined as the RD representative value (RepRD). Here, the average value is the arithmetic average of the calculated RD values. If there are RD values of 10, 50, 90, 50 (10+50+90)/3 becomes the RD representative value. RDI values were calculated using the Z-score method using the RepRD mean and standard deviation of the normal population using 1999 and T21 163 samples. The chromosomes used as the Normalized Factor were 2,7,9,12,14. As a result of the analysis, it was possible to detect an abnormality in the number of chromosomes in the sample with an accuracy of about 0.9995, and when the threshold was set to 4.0, it was confirmed that the sensitivity was 0.999 and the specificity was 1.000 (Table 5, Fig. 16).

Chromosome Accuracy Sensitivity Specificity AUC T21 0.9995 (0.9974,1.0000) 0.9999 1.0000 1.0000

7-5. Check the performance according to the RD representative value (RepRD) calculation method (using the reciprocal value of the average)

After calculating the RD values of sequence information aligned in each genetic region (for each chromosome), the reciprocal value of the average value of these values was defined as the RD representative value (RepRD). Here, the average value is the arithmetic average of the calculated RD values. If there are RD values of 10,50,90, 50 which is (10+50+90)/3 becomes the average value, and the inverse of this value 1/50 = 0.02 was used as the RD representative value. RDI values were calculated using the Z-score method using the RepRD mean and standard deviation of the normal population using 1999 and T21 163 samples. The chromosomes used as the Normalized Factor were 2,7,8,9,12,14. As a result of the analysis, it was possible to detect an abnormality in the number of chromosomes in the sample with an accuracy of about 0.9995, and when the threshold was set to 4.3, it was confirmed that the sensitivity was 0.993 and the specificity was 1.000 (Table 6, FIG. 17).

Chromosome Accuracy Sensitivity Specificity AUC T21 0.9995 (0.9974,1.0000) 0.9939 1.000 1.000

Example 8. Confirmation of the performance of RDI values for detecting chromosomal numerical abnormalities

8-1. Distribution of Read Count and Read Distance

In the analysis using the distance concept of sorted reads, the greater the number of reads produced, the shorter the distance between each reads will be. To confirm this, the distribution of the number of Reads and RepRD values for each chromosome was analyzed.

As a result, it was confirmed that the RepRD decreased as the overall number of Reads increased. In particular, it was confirmed that the relationship between the number of Reads and the RepRD value was not a linear relationship but a non-linear relationship, which is a result that the Reads distance concept reflects not only the number of simple Reads but also the sorted positions (FIG. 8).

Compared with the normal sample, it was confirmed that the RepRD values of the chromosomes identified as aneuploidy were low in Trisomy 13, 18 and 21 samples, respectively (FIG. 9).

8-2. Performance of Reads Distance Index, fetal fraction, clinical information, and relationship with existing G-score

In a fetal aneuploidy test using maternal blood, the fetal fraction and gestational age greatly affect the accuracy of the test. The higher the gestational age, the higher the fetal fraction tends to be, and the higher the fetal fraction, the higher the accuracy of the test. As a result of analyzing the distribution of the RDI _chr21 value of the Trisomy 21 sample, the maternal gestational age, and the fetal fraction, it was confirmed that the RDI _chr21 value decreased as the fetal fraction increased. In addition, in the relationship between the number of gestational weeks and the RDI _chr21 value, a tendency to decrease in samples over 15 weeks was confirmed. The tendency was confirmed (FIG. 10).

8-3. Positive discriminant clinical sample analysis result

Using the RDI value, the analysis performance of the normal group and the samples identified as each chromosomal aneuploidy was verified. As a result of comparing the analytical performance between normal and aneuploid samples after setting the RDI value as a cutoff (-3) of a certain standard, the accuracy of Trisomy 13 was 0.991, Trisomy 18 was 0.989, and Trisomy 21 was 0.998 (Table 7). In addition, it was confirmed that the AUC values were 0.999, 0.984, and 1.000 in Trisomy 13, 18, and 21, respectively (FIG. 11).

Chromosome Accuracy (95% CI) Sensitivity Specificity AUC T13 0.991 (0.976-0.997) 0.846 1.000 0.999 T18 0.989 (0.975-0.997) 0.925 1.000 0.984 T21 0.998 (0.99-1) 1.000 0.998 1.000

8-4. Performance check according to RDI calculation method

The log ratio analysis result using the median, which is different from the Z-score method using the mean and standard deviation of the RDI ratio of the normal reference group, was confirmed. Equation 16 was used for the log ratio analysis method.

Equation 16: RDI = log ₁₀ (RepRD Ratio _sample / Median(RepRD Ratio _reference ))

The same sample as used in Example 8-3 was used, and the analysis performance was compared after setting the RDI value to a predetermined cutoff (-0.0045). The performance was slightly different depending on the benign type, and the accuracy was 0.976 for Trisomy 21, 0.994 for Trisomy 18, and 0.991 for Trisomy 13 (Table 8).

Chromosome Accuracy (95% CI) Sensitivity Specificity AUC T13 0.991 (0.976-0.997) 0.846 1.000 0.999 T18 0.994 (0.981-0.999) 0.955 1.000 0.984 T21 0.976 (0.959-0.987) 1.000 0.965 1.000

8-5. Down sampling performance check

Using next-generation sequencing technology, the amount of data (number of reads) produced in a non-invasive method for determining fetal aneuploidy is known to be an important factor in accuracy. In this embodiment, the analysis performance of the RDI method according to the number of reads was calculated. As a criterion for analysis performance, the AUC value of the ROC analysis was used, and the in-silico random read selection method was used for the number of reads. One to ten million leads were randomly selected. As a result of the analysis using the aneuploidy sample No. 21, it was confirmed that the analysis performance decreased as the number of reads decreased ( FIG. 12 ).

Example 9. Confirmation of performance of RDI values for detecting chromosomal structural abnormalities

9-1. Distribution of Read Count and Read Distance

In order to see whether there is a chromosomal structural abnormality using RDI, it is necessary to divide the chromosome into an appropriate size, and in this embodiment, the chromosome section is divided into a size of 50k base. The distance between the leads is smaller as the number of leads increases, and the distance between the leads increases as the number of leads increases. As a result of examining the relationship between the number of reads and the distance corresponding to the divided section, it was confirmed that the read distance of the region in which the deletion of the structural abnormality of the chromosome was confirmed was longer than that of the region without the structural abnormality (FIG. 13).

9-2. Compare with Microarray's results

The analysis results of Microarray and RDI, which detect chromosomal structural abnormalities, were compared. The analysis sample was a sample in which a deletion of 3,897,640 bp in length was confirmed at the end of chromosome 1, and as a result of analysis using RDI, it was confirmed that a structural abnormality (deletion) was detected in a size of 3,700,000 bp in a similar region (FIG. 14).

As the specific parts of the present invention have been described in detail above, for those of ordinary skill in the art, it is clear that these specific descriptions are only preferred embodiments, and the scope of the present invention is not limited thereby. will be. Accordingly, it is intended that the substantial scope of the present invention be defined by the appended claims and their equivalents.

Claims (33)

delete delete delete (A) extracting a nucleic acid from a biological sample to obtain a nucleic acid fragment to obtain sequence information;
(B) confirming the position of the nucleic acid fragment in a standard chromosome sequence database (reference genome database) based on the obtained sequence information (reads);
(C) grouping the sequence information (reads) into full sequence, forward sequence and reverse sequence;
(D) using the grouped sequence information, defining a reference value of each nucleic acid fragment, measuring a distance between the reference values, and calculating an FD value (Fragments Distance) for each group; and
(E) Calculate each FDI value (Fragments Distance Index) for the entire chromosome region or for each specific region based on the FD value for each group calculated in step (D), so that each FDI value is less than or exceeding the reference value range In one case, a method of detecting a chromosomal abnormality comprising the step of determining that there is a chromosomal abnormality.
5. The method of claim 4, wherein step (A) is performed by a method comprising the following steps:
(Ai) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, amniotic fluid including oral cells, placental cells or fetal cells, tissue cells, and mixtures thereof;
(A-ii) removing proteins, fats, and other residues from the collected nucleic acids using a salting-out method, a column chromatography method, or a beads method; obtaining purified nucleic acids;
(A-iii) single-end sequencing or pair-end sequencing for purified nucleic acids or nucleic acids randomly fragmented by enzymatic digestion, pulverization, or hydroshear method end sequencing) preparing a library;
(A-iv) reacting the prepared library with a next-generation sequencer; and
(Av) acquiring sequence information (reads) of nucleic acids in a next-generation gene sequencing machine.
5. The method of claim 4, wherein the FD value of step (D) is the distance between the reference value of any one or more nucleic acid fragments selected from the i-th nucleic acid fragment and the i+1 to the n-th nucleic acid fragment with respect to the obtained n nucleic acid fragments. A method of detecting a chromosomal abnormality, characterized in that calculated through.
The method according to claim 6, wherein the reference value of the nucleic acid fragment is a value added or subtracted from a median value of the nucleic acid fragment.
The method according to claim 7, wherein the reference value of the nucleic acid fragment is derived based on the position values of forward and reverse sequence information (reads) in the case of paired-end sequencing. Way.
The method according to claim 8, further comprising excluding from the calculation process a nucleic acid fragment having an alignment score of sequence information (reads) less than a reference value.
The chromosomal abnormality detection according to claim 6, wherein the reference value of the nucleic acid fragment is derived based on one type of position value of forward or reverse sequence information (read) in case of single-end sequencing. How to.
The method of claim 10, wherein a random value is added when a position value is derived based on sequence information aligned in the forward direction, and a random value is subtracted when a position value is derived based on sequence information aligned in the reverse direction. A method for detecting chromosomal abnormalities, characterized in that.
The method of claim 7, wherein the arbitrary value is 30 to 70% of the average length of the nucleic acid to be analyzed.
The method of claim 7, wherein the arbitrary value is 0 to 5 kbp or 0 to 300% of the length of the nucleic acid fragment.
The method according to claim 4, wherein step (E) is performed by a method comprising the following steps:
(Ei) determining a representative value (RepFD) of FD values for the entire chromosome region or for each specific region;
(E-ii) Sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deriving a normalized factor by calculating one or more values selected from the group consisting of deviation, absolute deviation, coefficient of variation, reciprocal values thereof, and combinations thereof;
(E-iii) calculating a representative value ratio (RepFD ratio) based on Equation 1 below;
Equation 1: RepFD ratio = RepFD Target genomic region / Normalized Factor
(E-iv) Comparing the RepFD ratio value of the normal reference group and the sample, calculating FDI (Fragments Distance Index).
15. The method of claim 14, wherein the representative value (RepFD) of step (Ei) is the sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deviation, A method for detecting a chromosomal abnormality, characterized in that it is one or more values selected from the group consisting of median absolute deviation and coefficient of variation and/or one or more inverse values thereof.
The method of claim 15 , wherein the representative value (RepFD) of the step (Ei) is a median value, an average value, or a reciprocal value of the FD values.
The method according to claim 14, wherein the specific region in the sample other than the entire chromosome region to be analyzed or the specific genetic region to be analyzed in step (E-ii) is selected by a method comprising the following steps. :
a) randomly selecting an entire region of a chromosome to be analyzed or a region other than a specific genetic region;
b) determining the representative value (RepFD) of the genetic region selected in step a) as a pre-normalized factor (PNF);
c) calculating a representative value ratio (RepFD ratio) based on Equation 2:
Equation 2: RepFD ratio = RepFD Target genomic region / PNF
d) calculating a coefficient of variation (Coefficient of Variance: SD / Mean) of the RepFD ratio value of a normal reference group; and
e) determining the genetic region having the smallest value among the coefficients of variation obtained by repeating steps a) to d) as the entire chromosome region or a specific region in the sample other than the specific genetic region.
The method of claim 14, wherein in step (E-iv), the RepFD ratio value of a normal reference group is compared with the RepFD ratio value of the sample.
a decoding unit that extracts nucleic acids from a biological sample and deciphers sequence information;
an alignment unit that aligns the translated sequence to a standard chromosomal sequence database; and
By measuring the distance between the aligned nucleic acid fragments with respect to the selected nucleic acid fragments, the FD value (Fragments Distance) is calculated, and the FDI value (Fragments Distance) for the entire chromosome region or for each specific genetic region based on the calculated FD value Index) and, when the FDI value is less than or greater than the reference value or section, a chromosome abnormality detection device comprising a chromosome abnormality determining unit that determines that there is a chromosome abnormality.
A computer-readable storage medium comprising instructions configured to be executed by a processor for detecting a chromosomal abnormality,
(A) extracting a nucleic acid from a biological sample to obtain a nucleic acid fragment to obtain sequence information;
(B) aligning the nucleic acid fragment to a standard chromosome sequence database (reference genome database) based on the obtained sequence information (reads);
(C) measuring the distance between the selected nucleic acid fragments (fragments), calculating the FD value (Fragments Distance); and
(D) Based on the FD value calculated in step (C), the FDI value (Fragments Distance Index) is calculated for the entire chromosome region or for each specific genetic region. A computer readable storage medium comprising instructions configured to be executed by a processor that detects a chromosomal abnormality through the step of determining that there is a chromosomal abnormality.
The method according to claim 7, wherein when the arbitrary value is 50% of the average length of the nucleic acid to be analyzed, the calculated FD value is an RD value (Read Distance).
(A) extracting nucleic acids from a biological sample to obtain sequence information;
(B) aligning the obtained sequence information (reads) to a standard chromosome sequence database (reference genome database);
(C) calculating the RD value (Read Distance) by measuring the distance between the aligned reads with respect to the aligned sequence information (reads); and
(D) The RDI value (Read Distance Index) is calculated for the entire chromosome region or for each specific region based on the RD value calculated in step (C). A chromosomal abnormality detection method comprising the step of determining.
The method of claim 22, wherein step (A) is performed by a method comprising the following steps:
(Ai) obtaining nucleic acids from blood, semen, vaginal cells, hair, saliva, urine, amniotic fluid including oral cells, placental cells or fetal cells, tissue cells, and mixtures thereof;
(A-ii) removing proteins, fats, and other residues from the collected nucleic acids using a salting-out method, a column chromatography method, or a beads method; obtaining purified nucleic acids;
(A-iii) single-end sequencing or pair-end sequencing for purified nucleic acids or nucleic acids randomly fragmented by enzymatic digestion, pulverization, or hydroshear method end sequencing) preparing a library;
(A-iv) reacting the prepared library with a next-generation sequencer; and
(Av) acquiring sequence information (reads) of nucleic acids in a next-generation gene sequencing machine.
The method according to claim 22, wherein the step of grouping the reads aligned before the step (C) according to the aligned direction can be further used.
23. The method of claim 22, wherein the RD value of step (C) is, with respect to the n reads obtained, one of the values of both ends of the i-th read and any one or more reads selected from i+1 to n-th read. Chromosomal abnormality detection method, characterized in that calculated through the distance between the value plus or minus 50% of the average length of the nucleic acid.
26. The method of claim 25, wherein the RD value calculates the distance between the 5' or 3' end inside the i-th read and the 5' or 3' end of any one or more of the i+1 to n-th reads. Methods for detecting chromosomal abnormalities.
The method of claim 22, wherein step (D) is performed by a method comprising the following steps:
(Di) determining a representative value (RepRD) of the RD value for the entire chromosome region or for each specific region;
(D-ii) Sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deriving a normalized factor by calculating one or more values selected from the group consisting of deviation, median absolute deviation, and coefficient of variation and/or one or more inverse values thereof;
(D-iii) calculating a representative value ratio (RepRD ratio) based on the following Equation 10;
Equation 10: RepRD ratio = RepRD Target genomic region / Normalized Factor
(D-iv) Comparing the RepRD ratio value of the normal reference group and the sample, calculating RDI (Read Distance Index):
28. The method of claim 27, wherein the representative value (RepRD) in step (Di) is the sum, difference, product, mean, log of product, log of sum, median, quantile, minimum, maximum, variance, standard deviation, A chromosomal abnormality detection method, characterized in that it is one or more values selected from the group consisting of a median absolute deviation and a coefficient of variation and/or one or more inverse values thereof.
The method according to claim 28, wherein the representative value (RepRD) in step (Di) is a median value, an average value, or a reciprocal value of the RD values.
The method according to claim 27, wherein the specific region in the sample other than the entire chromosome region to be analyzed or the specific genetic region to be analyzed in step (D-ii) is selected by a method comprising the following steps:
a) randomly selecting an entire region of a chromosome to be analyzed or a region other than a specific genetic region;
b) determining a representative value of the RepRD value of the genetic region selected in step a) as a pre-normalized factor (PNF);
c) calculating a representative value ratio (RepRD ratio) based on Equation 11 below:
Equation 11: RepRD ratio = RepRD Target genomic region / PNF
d) calculating a coefficient of variation (Coefficient of Variance: SD / Mean) of the RepRD ratio value of a normal reference group; and
e) determining the genetic region having the smallest value among the coefficients of variation obtained by repeating steps a) to d) as the entire chromosome region or a specific region in the sample other than the specific genetic region.
The method of claim 27, wherein in step (D-iv), the RepRD ratio value of a normal reference group is compared with the RepRD ratio value of the sample.
a decoding unit that extracts nucleic acids from a biological sample and deciphers sequence information;
an alignment unit that aligns the translated sequence to a standard chromosomal sequence database; and
By measuring the distance between reads aligned with respect to the selected sequence information (reads), the RD value (Read Distance) is calculated, and the RDI value (Read Distance Index) for the entire chromosome region or for each specific genetic region based on the calculated RD value ), and when the RDI value is less than or greater than the reference value or section, a chromosome abnormality detection device comprising a chromosomal abnormality determining unit for determining that there is a chromosomal abnormality.
A computer-readable storage medium comprising instructions configured to be executed by a processor for detecting a chromosomal abnormality,
(A) extracting nucleic acids from a biological sample to obtain sequence information;
(B) aligning the obtained sequence information (reads) to a standard chromosome sequence database (reference genome database);
(C) calculating the RD value (Read Distance) by measuring the distance between the reads aligned with respect to the selected sequence information (reads); and
(D) Calculate the RDI value (Read Distance Index) for the entire chromosome region or for each specific genetic region based on the RD value calculated in step (C). A computer readable storage medium comprising instructions configured to be executed by a processor that detects a chromosomal abnormality through determining that there is a chromosomal abnormality.

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