KR101907650B1 - Method of non-invasive trisomy detection of fetal aneuploidy - Google Patents

Abstract

Noninvasive fetal chromosome analysis methods are provided for determining fetal chromosomal integrity using chromosome sequencing information obtained from biological samples isolated from maternal.

Description

METHOD OF NON-INVASIVE TRISOMY DETECTION OF FETAL ANEUPLOIDY BACKGROUND OF THE INVENTION 1. Field of the Invention < RTI ID = 0.0 >

Noninvasive fetal chromosome analysis methods are provided for determining fetal chromosomal integrity using chromosome sequencing information obtained from biological samples isolated from maternal.

Recently, due to the increase in the age of childbirth and the development of various prenatal diagnosis devices, interest in prenatal diagnosis is increasing day by day.

Prenatal diagnosis can be divided into invasive and noninvasive diagnostic methods. Invasive diagnostic methods include amniocentesis, percutaneous umblical blood sampling, chorionic villus sampling, fetal tissue collection, and may cause abortion, disease or malformations by shocking the fetus during the examination. Noninvasive diagnostic methods have been developed to overcome the problems of these invasive diagnostic methods.

The discovery of cell-free fetal DNA (cffDNA) in cell-free DNA (cfDNA) in maternal serum provided a powerful tool for the development of noninvasive prenatal genetic diagnosis. The application of this cffDNA to prenatal diagnosis was further accelerated by the introduction of massively parallel sequencing techniques such as Next Generation Sequencing (NGS).

In addition, several studies have demonstrated that fetal and maternal DNA is uniformly distributed throughout the entire genome by sequencing after total genome sequencing (WGS) and target enrichment of cffDNA (Lo YM et al. , Science translational medicine 2010; 2: 61ra91; Liao GJ et al., Clinical chemistry 2011; 57: 92-101; Kitzman JO et al., Science translational medicine 2012; 4: 137ra76).

Based on these studies, a method has been proposed that can detect fetal chromosomal anomalies from maternal and fetal cfDNA in maternal blood (e.g., plasma, serum, etc.). However, since the amount of cfDNA in the fetus present in maternal blood is relatively small, a method of generating and discriminating a large number of NGS leads is generally used. Since a large number of NGS leads leads to an increase in the cost of the experiment, a discriminating means capable of discriminating the fetal chromosome abnormally sensitively should be developed even at a low number of leads (Extremely Low Reads). Also, because of sequencing, library prep, and GC contents, bias occurs in large-scale parallel sequencing data such as next-generation sequencing, and it is also necessary to eliminate this bias for more accurate discrimination.

Therefore, it is required to develop a chromosome analysis technique that can discriminate accurately even at a low lead number and accurately remove the deviation of data for accurate fetal chromosome abnormality diagnosis.

Korean Patent No. 10-1516976

One example provides a noninvasive fetal chromosome analysis method for determining fetal chromosomal status using chromosome sequence information obtained from biological samples isolated from a mother.

In this specification, the non-invasive fetal chromosomal analysis method is a method of analyzing a sequence information for determining (discriminating, confirming, or diagnosing) the chromosomal integrity of a fetus, or a method for analyzing chromosomes in fetal chromosomes (discrimination, confirmation, ≪ / RTI > and they all have the same meaning.

The non-invasive fetal chromosomal analysis method is a method for identifying a chromosome of a fetus from DNA sequence information obtained from a biological sample separated from a mother by using a specific chromosome (for example, 13, 18 or 21 Chromosome) and the average number of leads existing in the merged bin generated from other chromosomes except for the above chromosome are removed to remove the deviation between the experiments, and a weighted average of the chromosomes And the probability of false positives is reduced by improving the reliability and specificity of the result using the ratio of the number of inter-leads.

The DNA sequence information obtained from the biological sample separated from the mother may be data generated by Whole Genome Sequencing (WGS) method of large scale parallel sequence analysis such as Next Generation Sequence Analysis (NGS).

In one embodiment, the non-invasive fetal chromosome assay method may comprise the steps of:

1-1) obtaining sequence information of polynucleotide fragments covering the entire genome from a test sample separated from a mother;

1-2) preparing sequence information of polynucleotide fragments covering the entire genome of the reference sample;

2-1) Sequence information of the polynucleotide fragments of the test sample obtained in the above step 1-1) is mapped to a reference genome sequence, and a bin number previously set for each chromosome is calculated Determining a polynucleotide fragment count to have the polynucleotide fragment count,

2-2) determining the number of reference polynucleotide fragments so as to have a predetermined bin number using sequence information of the polynucleotide fragments of the reference sample prepared in the step 1-2);

3-1) The number of the average polynucleotide fragments of the target chromosome to be tested for the number of the test polynucleotide fragment water is selected from the number of n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome Obtaining a ratio of the number of average polynucleotide fragments of each merged bin generated from the chromosome to the number of average number of test polynucleotide fragments (the ratio is obtained by number of merged bins);

3-2) The number of the average polynucleotide fragments of the target chromosome to be tested for the number of the reference polynucleotide fragments, and the number (n is an integer selected from 1 to 21) selected from among chromosomes other than the target chromosome Obtaining a ratio of the number of average polynucleotide fragments to the number of average polynucleotide fragments of each merged bin generated on chromosomes (the ratio is obtained by number of reference samples * merged bin);

4) obtaining a value of CV (Coefficient of Variation) by the ratio of the number of average reference polynucleotide fragments;

5-1) obtaining a ratio of the weighted average number of test polynucleotide fragments by selecting values corresponding to the upper N _CVs having a smaller CV value from the ratio of the average number of test polynucleotide fragments in step 3-1);

5-2) Using the numerical values corresponding to the upper N _CVs having the smallest CV value selected in the step 5-1) as the ratio of the average number of reference polynucleotide fragments in the step 3-2), the weighted average reference poly Obtaining a ratio of the number of nucleotide fragments; And

6) comparing the ratio of the weighted average number of test polynucleotide fragments obtained to the weighted average number of reference polynucleotide fragments obtained.

In one example, the step of comparing step 6) may be performed by obtaining the Z-score of the target chromosome using the ratio of the weighted average number of test polynucleotide fragments to the weighted average number of polynucleotide fragments.

In one example, the non-invasive fetal chromosome assay method further comprises, after step 6)

7) confirming whether or not the fetal chromosome is complementary to the fetus using the comparison result (for example, Z-score) of the ratio of the weighted average number of test polynucleotide fragments to the weighted average number of polynucleotide fragments obtained in the step 6)

. ≪ / RTI >

In the non-invasive fetal chromosomal analysis method, steps 1-1) and 1-2) may be performed continuously or in any order, and steps 2-1) and 2-2) may be performed simultaneously or sequentially And steps 3-1 and 3-2 may be performed continuously, regardless of the order or order. ,

In one example, the non-invasive fetal chromosome analysis method may be performed after steps 2-1) and 2-2) (and steps 3-1 and 3-2) And removing the bias of the number of test polynucleotide fragments obtained and the number of reference polynucleotide fragments. The bias removing step may be performed by applying SVD (Singular Value Decomposition) or the like.

The chromosome may be an autosomal chromosome and, in the case of humans, may be selected from the group consisting of chromosomes 1 to 22. The 'target chromosome' is a chromosome for confirming the chromosomal integrity of the fetus. For example, the chromosome may be a chromosome 13, 18 or 21 of a human, but is not limited thereto. May be selected from chromosomes. The 'n chromosomes selected from the chromosomes other than the target chromosome' are chromosomes selected from the rest of the autosomes other than the target chromosome to be checked for chromosomal integration (n is an integer selected from 1 to 21).

The test sample separated from the mother may be blood, plasma, or serum separated from the mother. Applicable maternal of the noninvasive fetal chromosome analysis method proposed in this specification may be a mother whose target chromosome is normal, that is, a mother who does not have the desired chromosomal insolubility.

Another example provides a computer readable method for determining chromosomal integrity of a fetus comprising the steps of:

A-1) The sequence information of the polynucleotide fragments of the test sample is mapped to the reference genome sequence, and the number of test polynucleotide fragments (bin number) determining a fragment count,

A-2) determining the number of reference polynucleotide fragments so as to have a preset bin number using sequence information of polynucleotide fragments of the reference sample;

B-1) The number of the average number of polynucleotide fragments of the target chromosome to be tested, of which number is n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome in the test polynucleotide fragment water Obtaining a ratio of the average number of polynucleotide fragments of each merged bin generated from the chromosome to the number of average number of test polynucleotide fragments;

B-2) The number of the average polynucleotide fragments of the target chromosome to be tested for the number of the reference polynucleotide fragments, wherein n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome Obtaining a ratio of the number of average polynucleotide fragments to the number of average polynucleotide fragments of each merged bin generated on chromosomes (the ratio is obtained by number of reference samples * merged bin);

C) obtaining a value of CV (Coefficient of Variation) by the ratio of the number of average reference polynucleotide fragments;

D-1) selecting numerical values corresponding to the upper N _CVs having a smaller CV value from the ratio of the average number of test polynucleotide fragments in the step B-1 to obtain a weighted average number of test polynucleotide fragments;

D-2) Using the numerical values corresponding to the upper N _CVs having the smallest CV value determined in the above step D-1 for the ratio of the average number of reference polynucleotide fragments in step B-2), the weighted average reference poly Obtaining a ratio of the number of nucleotide fragments;

E) comparing the ratio of the number of weighted average test polynucleotide fragments obtained to the weighted average number of reference polynucleotide fragments obtained; And

F) confirming whether or not the fetal chromosome is complementary to the fetus using the comparison result of the ratio of the weighted average number of test polynucleotide fragments obtained in step E) to the weighted average number of polynucleotide fragments (for example, Z-score).

The computer readable method may further comprise: a) after the steps A-1) and A-2) (and before the steps B-1 and B-2) And removing the bias of the number of reference polynucleotide fragments. The bias removing step may be performed by applying SVD (Singular Value Decomposition) or the like.

Another example provides a computer program stored on a computer readable storage medium for executing the steps of the computer readable method.

Another example provides a computer readable storage medium (or recording medium) containing a computer executable instruction for executing the steps of the computer readable method.

Definition of Terms

Unless otherwise defined, 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.

"Aneuploidy" means that the number of target chromosomes is different from the number of normal chromosomes (2), that is, 0, 1, or more than 3 (for example, 3) target chromosomes exist . Such chromosomal aberrations are very important for fetal diagnosis because they are associated with rare genetic diseases. For example, on a human chromosome basis, three chromosomes 13 (trisomy 13), Patau syndrome, three chromosomes 18 (trisomy 18), Edward syndrome, When there are three chromosomes 21 (trisomy 21), Down syndrome is caused.

A "reference genome sequence" refers to a genomic sequence database representing one species. Currently the human reference genome may be constructed on the basis of published genomic sequences (eg, UCSC, NCBI, etc.) such as Build 37 (build 37: GRCh37), hg18, hg19, hg38.

"Massively parallel sequencing" randomly decomposes a genome into numerous pieces (polynucleotide fragments), simultaneously reads out the sequence of each fragment, A method for sequencing nucleotide sequences that rapidly decode enormous genomic information by combining them using bioinformatics. Further explanations of large-scale parallel sequencing can be found in Rogers and Ventner, Nature (2005) 437: 326-327.

The term " about "as used herein to refer to a numerical value may be used to include a variation (variation) of 10%, 5%, or 3% of the stated value, unless otherwise defined.

Hereinafter, the present invention will be described in more detail.

Step 1): Covering the entire genome Step of obtaining sequence information of polynucleotide fragments

Sequence information of the polynucleotide fragments can be obtained by sequencing the template DNA selected from the sample.

The polynucleotide fragments are assigned to specific positions of each chromosome through mapping with a standard genomic sequence and cover the entire genome.

The nucleotide sequence of the polynucleotide fragments may be obtained by a large-scale parallel-type sequencing method, for example, a next-generation sequencing method. In this case, the polynucleotide fragment may be a read used in a next-generation sequencing analysis, wherein the number of polynucleotide fragments is a read count, and the average number of polynucleotide fragments may be an average number of leads.

In one embodiment, the polynucleotide fragments or lids are in the range of about 10 to about 2000 bp, about 10 to about 1000 bp, about 10 to about 500 bp, about 10 to about 300 bp, about 10 to about 200 bp, bp, about 25 to about 1000 bp, about 25 to about 500 bp, about 25 to about 300 bp, about 25 to about 200 bp, about 25 to about 100 bp, about 50 to about 2000 bp, about 50 to about 1000 bp, About 100 to about 1000 bp, about 100 to about 500 bp, about 100 to about 300 bp, about 100 to about 300 bp, about 50 to about 200 bp, about 50 to about 100 bp, about 50 to about 100 bp, About 200 bp, about 150 to about 2000 bp, about 150 to about 1000 bp, about 150 to about 500 bp, or about 150 to about 300 bp, and the lengths thereof may be the same or different. For example, the polynucleotide fragments or leads may each independently have a length of about 100 bp, about 200 bp, about 300 bp, about 400 bp, about 500 bp, or about 1000 bp.

At this time, polynucleotide fragments assigned to one or more chromosomes and / or polynucleotide fragments not assigned to any chromosome can be ignored and not considered in a later step.

The massive parallel sequencing can be performed using, for example, a 454 platform (Margulies, et al., Nature (2005) 437: 376-380), Illumina Genome Analyzer (or Solexa ™ platform), Illumina HiSeq2000, HisSeq2500, MiSeq, NextSeq500, Ion Proton, Ion S5, Ion S5XL, or SOLiD (Applied Biosystems) or Helicos True Single Molecule DNA sequencing technology (Harris, et al., Science (2008) 320: 106-109), a single molecule of Pacific Biosciences, And / or real-time (SMRT (TM)) technology. In addition, large-scale parallel sequencing analysis as possible on the basis of nanopore sequencing (Soni and Meller, Clin Chem (2007) 53: 1996-2001) allows high-order multiplexing of parallel- (Dear, Brief Funct. Genomic Proteomic (2003) 1: 397-416). Each of these platforms sequenced single molecules that are clonally expanded or unamplified of nucleic acid fragments. Sequence information of polynucleotide fragments can be obtained using commercially available sequencing instruments.

It will be apparent to those skilled in the art that the above sequence analysis may be performed by various other known sequence analysis methods and / or modified methods thereof.

1-1) Covering whole genome from test sample Obtaining the sequence information of the polynucleotide fragments

The test sample separated from the mother may be blood, plasma, or serum separated from the mother. The mother may be a human female, and the target chromosome to be checked for chromosomal aberration may be normal, that is, a mother with no chromosomal aberration. The blood, plasma, or serum may be separated by conventional methods, and may be pregnant 8-12, 12-16, 16-20, 20-24, 24-28, 28-32, 32-36, 36-40, or For example, 40 to 44 weeks, for example, between 8 and 28 weeks of gestation.

Obtaining the sequence information of the polynucleotide fragments covering the entire genome of the test sample,

i) carrying out a large-scale parallel sequencing analysis, such as next generation sequence analysis, on the test sample, or

ii) preparing the sequence information obtained in i) in a form stored in a data storage medium or obtaining it via a network data transmitting / receiving device

Lt; / RTI >

1-2) Covering the entire genome of a reference sample Obtaining the sequence information of the polynucleotide fragments

The reference sample is a genome pool that already knows the 'nucleotide sequence information of the genome and the sequence information of the polynucleotide fragments covering the entire genome' (hereinafter referred to as 'genome sequence information'), May be a genomic sequence information set obtained from normal pregnant mothers (e. G., From plasma or serum). The genome sequence information obtained from the normal pregnant mothers of the fetus having no chromosomal aberration of the target chromosome includes genomic sequence information obtained from the mothers obtained from the mothers of which the fetus has been confirmed to have no chromosomal aberration ≪ / RTI > The number of reference samples (corresponding to the number of mothers or the number of genomes) is not particularly limited, but may be selected from the range of about 50 to about 200,000 in consideration of the convenience of data processing and the accuracy of the result, (That is, the upper limit is 200,000), about 50 or more, about 100 or more, or about 200 or more. The reference sample may be selected from the genome sequence information group subdivided by race, such as Korean, Oriental, and Western, or may be selected so as to combine two or more races.

The step of preparing the sequence information of the polynucleotide fragments covering the entire genome of the reference sample includes obtaining the genome sequence information from the normal pregnant mothers of the fetus that does not have the abnormality of the target chromosome, And genome sequence information of the genome pool of the genome pool.

Step 2) The polynucleotide fragment can (polynucleotide fragment count), the result determining step

In step 2), sequence information of the polynucleotide fragments of each of the test sample and the reference sample is mapped to a reference genome sequence, and polynucleotides (hereinafter referred to as " polynucleotides " And determining the polynucleotide fragment count.

2-1) Test Polynucleotide  Determining the number of fragments

The above step 2-1) is a step for obtaining a bin number of an arbitrary number (B number) for the sequence information of the polynucleotide fragments covering the entire genome sequence mapped to the standard genomic nucleotide sequence obtained from the test sample (Polynucleotide fragment count or read count) to produce a polynucleotide fragment count vector (read count vector). The polynucleotide fragment count vector may be a polynucleotide fragment count vector or a read count vector. have.

For example, the number of polynucleotide fragments or the lead number vector (S) of a test sample can be expressed by the following equation:

(수식 1) (rc: read count; B: bin 개수)

(Formula 1) (rc: read count; B: number of bin)

In the above equation, rc denotes a read count, which is an experimentally obtained value.

In one example, the bin number is a value such that each bin comprises from about 10,000 to about 20,000,000, from about 20,000 to about 15,000,000, from about 30,000 to about 10,000,000, or from about 50,000 to about 1,000,000 nucleotides Can be selected. For example, the bin number may range from about 1 to about 30,000, from about 1 to about 10,000, from about 1 to about 5,000, from about 1 to about 1,000, from about 1 to about 500, from about 2 to about 30,000, from about 2 to about 10,000, From about 5,000 to about 5,000, from about 5 to about 1,000, from about 5 to about 500, from about 10 to about 30,000, from about 5,000, from about 2,000 to about 1,000, from about 2 to about 500, from about 5 to about 30,000, from about 5 to about 10,000, From about 10 to about 10,000, from about 10 to about 5,000, from about 10 to about 1,000, from about 10 to about 500, from about 20 to about 30,000, from about 20 to about 10,000, from about 20 to about 5,000, from about 20 to about 1,000, From about 50 to about 500, from about 50 to about 30,000, from about 50 to about 10,000, from about 50 to about 5,000, from about 50 to about 1,000, from about 50 to about 500, from about 100 to about 30,000, from about 100 to about 10,000, About 5,000, about 100 to about 1,000, or about 100 to about 500.

Refer to 2-2) Polynucleotide  Determining the number of fragments

The polynucleotide fragment count or the number of leads of the polynucleotide fragments is set so as to have B bin numbers for the sequence information of the polynucleotide fragments of the N reference sample groups selected in the secured reference pool. (or a read count) to generate a reference polynucleotide fragment number matrix (or a reference read count matrix).

For example, the number of polynucleotide fragments or the lead number matrix R of the reference sample can be expressed by the following Equation 2 and Equation 3:

(수식 2)

(Equation 2)

(수식 3)

(Equation 3)

(B: number of bin; N: number of reference sample)

Step a) Bias removal step

Step a) may be performed further between step 2) and step 3) in order to remove the bias from the obtained polynucleotide fragment number value to obtain a more accurate result.

The step a) may be performed by removing the bias by applying SVD (Singular Value Decomposition) to the number of test polynucleotide fragments and the number of reference polynucleotide fragments.

In one example, step a) may be performed by applying SVD, wherein i) the number of reference polynucleotide fragments and the number of test polynucleotide fragments are combined to form matrix X Ii) performing SVD on the combined matrix; iii) within 50%, within 45%, within 40%, within 35%, within 30% of the sum of the singular values for the decomposed diagonal matrix D , Within 25%, within 20%, within 15%, or within 10% such as 1 to 50%, 1 to 45%, 1 to 40%, 1 to 35%, 1 to 30%, 1 to 25% 5 to 35%, 5 to 35%, 5 to 30%, 5 to 25%, 5 to 20%, 1 to 20%, 1 to 15%, 1 to 10%, 5 to 50% 5 to 15%, or 5 to 10%; iv) generating a biased diagonal matrix D ^BR by replacing the value of the corresponding singular value in the matrix D by 0 , And v) generating a matrix X ^BR with the bias removed using the matrix D ^BR .

(수식 4-1)

(Expression 4-1)

(수식 4-2)

(Equation 4-2)

(수식 4-3)

(Equation 4-3)

(수식 5)

(Equation 5)

(수식 6)

(Equation 6)

(수식 7)

(Equation 7)

(Where UDV ^T is the matrix decomposed into the SVD before the bias removal and UD ^BR V ^T means the matrix decomposed after the bias removal)

As can be seen in FIG. 3, by applying SVD, it can be seen that the number of polynucleotide fragments (read count) remained constant regardless of the GC content, which indicates that the GC bias is removed by SVD application will be.

Step 3) Average Polynucleotide  snippet Number ratio  Steps to Obtain

In step 3), the average number of polynucleotide fragments of the target chromosome is compared with the average number of polynucleotide fragments of chromosomes other than the target chromosome, and the ratio is determined. Contributing to further improving the sensitivity of the results.

In the case of humans, it can be selected from the group consisting of chromosomes 1 to 22. The 'target chromosome' is a chromosome for confirming the chromosomal integrity of the fetus. For example, the chromosome may be a chromosome 13, 18 or 21 of a human, but is not limited thereto. May be selected from chromosomes. The 'n chromosomes selected from other chromosomes other than the target chromosome' are chromosomes selected from the rest of the autosomes other than the target chromosome to be checked for chromosomal isolation. n is an integer selected from 1 to 21; In one example, n can be used to determine the ratio of the average number of polynucleotide fragments to the average number of polynucleotide fragments of each of the 21 chromosomes, except for the target chromosome, of 21 human autosomal chromosomes.

The "average number of polynucleotide fragments" can be obtained by averaging the number of polynucleotide fragments or the number of leads present in the boundary of the target chromosome or merged bin.

The "average number of polynucleotide fragments of chromosomes other than the target chromosome" is an average value of the number of polynucleotide fragments corresponding to a merged bin in which each bin is integrated so as to have a predetermined length arbitrarily determined for each chromosome.

In one embodiment, the ratio of the average number of test polynucleotide fragments or the average number of reference polynucleotide fragments can be calculated by the following steps:

i) Determining mb _size , which is the average size of Merged Bin, divided by the total number of binomial multiplied by 22, the total number of autosomes, and k set in advance, and integrating the bins to have a length of mb _size for each chromosome

(수식 8); 및

(Eq. 8); And

ii) obtaining a mean value for each merged bin j of a chromosome other than chromosome i and a target chromosome to obtain a read count ratio

(수식 9).

(Equation 9).

* μ _chri is the average number of leads of the target chromosome i, and μ _mbj is the average number of leads of the merged bin j. The k value is a value selected by the user, and may be a value of 1 to 20, 1 to 15, 1 to 10, or 1 to 5, for example.

3-1) obtaining the ratio of the number of average test polynucleotide fragments

Wherein said step (3-1) is a step of selecting, from said test polynucleotide fragment water, n number of average polynucleotide fragments of the target chromosome to be tested, The ratio of the average number of polynucleotide fragments to the number of the average number of polynucleotide fragments of each of the merged bins generated in the chromosome of the number of merged bins (obtained as a number of merged bins).

Specifically, the above step 3-1) is a step of examining the number (or the number of test leads) of the average number of polynucleotide fragments of the target chromosome and n chromosomes except for the target chromosome in the number of test polynucleotide fragments (or the number of test leads) The average number of polynucleotide fragments (or the average number of leads) of the target chromosome is calculated by taking the number of the average polynucleotide fragments (or the average number of leads) (The ratio is obtained by the number of merged bins), the average number of test polynucleotide fragments (or the average test lead count ratio vector) (Case read count ratio vector) is calculated And the like. The mean test polynucleotide fragment number ratio vector (RCR _chri ) for other chromosomes of the i-th chromosome (chromosome i) can be expressed as mbm: merged bin number:

(수식 10)

(Equation 10)

3-2) Step of obtaining the ratio of the number of average reference polynucleotide fragments

Wherein said step (3-1) is a step in which, in said reference polynucleotide fragment water, n number of average polynucleotide fragments of the target chromosome to be tested for the test are selected from among chromosomes other than said target chromosome (n is selected from 1 to 21 (The number of reference samples) x the number of merged bin (mbm (number of reference samples)) is obtained by obtaining the ratio of the average number of polynucleotide fragments to the number of average polynucleotide fragments of each merged bin generated on chromosome ) ≪ / RTI >

Specifically, the above step 3-2) is a step of determining the number (or the number of reference leads) of the average polynucleotide fragments of the target chromosome and the number of reference polynucleotide fragments (or the number of reference leads) obtained from the N reference samples, The average number of polynucleotide fragments (or the average number of leads) / the number of merged bin average polynucleotide fragments (or the average number of leads) of the target chromosome is calculated by taking the number of mbm merged bin average polynucleotide fragments (or the average number of leads) The reference read count ratio matrix (or reference read count ratio matrix) (the ratio is obtained by the number of reference samples (N) * mbm), and the average read polynomial . [0033] FIG. The average reference polynucleotide fragment number ratio matrix (RCRM _chri ) for other chromosomes of the i-th chromosome i can be expressed by the following equation:

(Equation 11)

Step 4) Step of obtaining CV (Coefficient of Variation)

Step 4) is a step of obtaining a CV (Coefficient of Variation) value for each average polynucleotide fragment number ratio from the obtained average number of reference polynucleotide fragments.

Specifically, the above steps are performed on the reference sample group with respect to the ratio of the average number of polynucleotide fragments (average number of leads) and the number of merged bin average polynucleotide fragments (average number of leads) (RCRi, j) . ≪ / RTI > The CV (CV _chri ) for the i-th chromosome i can be obtained by the following equation:

(수식 12)

(Equation 12)

In the above equation,? RCR _{n, mbm} ΜRCR _{n and mbm} are the average of the lead counts for each chromosome and merged bin calculated for the reference sample group, .

Step 5) obtaining the ratio of the number of weighted average polynucleotide fragments

Step 5 is to increase the reliability and accuracy of the result in addition to step 3. The number of average polynucleotide fragments (mbn pieces) per target chromosome obtained is arbitrarily selected in descending order of CV, (The ratio of the number of weighted average polynucleotide fragments) obtained by multiplying the reciprocal number of CVs corresponding to the ratio of the number of fragments obtained in the above step.

5-1) obtaining the ratio of the number of weighted average test polynucleotide fragments

Specifically, in step 5-1), each chromosome chr _i The ratio of the average number of polynucleotide fragments of the upper N _CVs having a small CV value was selected based on the CV value calculated for the reference sample group. The ratio of the average number of polynucleotide fragments to the number of polynucleotide fragments And calculating the weighted average polynucleotide fragment number ratio (by multiplying the reciprocal number of CV by averaging) with the corresponding CV value. For example, the N _CV is an average polynucleotide fragment having a value of at least about 1.1 fold, at least about 1.3 fold, at least about 1.5 fold, at least about 1.7 fold, at least about 2 fold, or at least about 3 fold greater than the minimum value of Cv _chri the percentage value (RCR), for example, about 1.1 times to about 5 times the minimum contrast of Cv _chri, about 1.1 times to about 3 times, about 1.1 times to about 2 times, about 1.3 times to about 5 times, about 1.3 to about From about 1.5 times to about 3 times, from about 1.5 times to about 2 times, from about 1.7 times to about 5 times, from about 1.7 times to about 3 times, from about 1.3 times to about 2 times, from about 1.5 times to about 5 times, Average number of polynucleotide fragments (RCR) values ranging from about 1.7-fold to about 2-fold, from about 2-fold to about 5-fold, or about 2-fold to about 3-fold can be selected, And / or empirically appropriate values.

In one example, the ratio of the number of weighted average polynucleotide fragments (WRCR _chri ) of the i-th chromosome (chromosome i) can be obtained by the following equation:

(Equation 13)

5-2) obtaining the ratio of the number of weighted average reference polynucleotide fragments

For the reference samples, the ratio of the number of average polynucleotide fragments to the number of N polynucleotides of the upper N _CVs per each reference sample (N total) and each chromosome was calculated. The CV value corresponding to the ratio of the number of polynucleotide fragments was used as a weighted average To obtain a mean), the ratio weighted average polynucleotide fragment number ratio value can be calculated to generate the reference weighted average polynucleotide fragment number ratio vector.

In one example, the weighted average reference polynucleotide fragment number ratio vector (R _WRCRchri ) of the i-th chromosome i (chromosome i) can be calculated by the following equation:

(14)

Step 6) comparing the ratio of the number of weighted average polynucleotide fragments

Wherein the step of comparing step 6) comprises comparing the ratio of the weighted average number of test polynucleotide fragments to the ratio of the number of weighted average reference polynucleotide fragments, and the comparison may be performed by obtaining the Z-score of the target chromosome .

For example, the Z-score (Z _{CV-ratio.chri} ) can be calculated by the following equation (15) by comparing the ratio of the weighted average number of test polynucleotide fragments of the target chromosome (chromosome i) to the weighted average number of polynucleotide fragments have:

(수식 15)

(Equation 15)

는 가중 평균 참조 폴리뉴클레오타이드 단편 수 비율 벡터의 평균을 의미하고,

는 가중 평균 참조 폴리뉴클레오타이드 단편 수 비율 벡터의 표준편차를 의미한다.In Equation (15)

Means the average of the weighted average number of reference polynucleotide fragments,

Means the standard deviation of the weighted average reference polynucleotide fragment number ratio vector.

Step 7) Step of confirming fetal chromosomal integrity

The fetal chromosomal integrity can be determined based on the comparison result of the weighted average number of test polynucleotide fragments obtained in the step 6). That is, as a result of the ratio of the weighted average number of test polynucleotide fragments to the weighted average number of reference polynucleotide fragments, the proportion of the weighted average test polynucleotide fragments is significantly higher or lower than the ratio of the weighted average reference polynucleotide fragments, We believe that the possibility of a successful bid is high.

For example, when the polynucleotide fragment number ratio comparison is performed by the Z-score, it can be judged that the larger the Z-score value, the higher the probability of the fetal chromosomal aberration.

In one example, when the absolute value of the target chromosome (chromosome _{i) Z-score (Z CV} -ratio. Chri) for more than a certain value or more, e.g., about 3, chromosomal aneuploidy in the chromosome i of fetal chromosome of the test sample is Can be determined to exist:

(수식 16)

(Expression 16)

Each step of the non-invasive fetal chromosome analysis method described above can be performed through an information processing and reading apparatus such as a computer.

Another example of the present invention provides an information processing system (computer) for non-invasive fetal chromosome analysis. The system may be a system that includes means applied for use in the non-invasive fetal chromosome analysis method described above. The system

1) a computer-readable information storage medium comprising a sequencer or sequence information; And

2) an information processing and reading medium (computer) capable of receiving information from the sequence analyzer or reading information in the information storage medium,

. ≪ / RTI >

The system may further comprise a biological sample separated from the mother and / or a plurality of polynucleotide fragments (e.g., test sample polynucleotide fragments and / or reference sample polynucleotide fragments as described above).

On the other hand, the methods and information described herein can be implemented on a computer-readable medium known through a program capable of executing the steps described above. More specifically, the non-invasive fetal chromosome analysis method and / or the information obtained in each step described above may be implemented as a computer executable instruction, in whole or in part, on a known computer readable medium and / Lt; / RTI > For example, the methods described herein may be implemented in hardware. The hardware may be a specially designed hardware or firmware, such as a computer, a standard multi-purpose CPU, an application-specific integrated circuit (ASIC) or other hard-wired device, The term " computer " used may be for generic purposes.

Another example of the present invention provides a computer readable method for determination of chromosomal integrity of a fetus comprising the steps of:

A-1) The sequence information of the polynucleotide fragments of the test sample is mapped to the reference genome sequence, and the number of test polynucleotide fragments (bin number) fragment count) (the above-described step 2-1)),

A-2) determining the number of reference polynucleotide fragments so as to have a preset bin number using sequence information of polynucleotide fragments of a reference sample (step 2-2 described above);

B-1) The number of the average number of polynucleotide fragments of the target chromosome to be tested, of which number is n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome in the test polynucleotide fragment water Obtaining a ratio of the number of average polynucleotide fragments to the number of average polynucleotide fragments of each merged bin generated from the chromosome (the ratio is obtained by the number of merged bins) (step 3-1 described above) );

B-2) The number of the average polynucleotide fragments of the target chromosome to be tested for the number of the reference polynucleotide fragments, wherein n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome Obtaining the ratio of the number of the average polynucleotide fragments to the number of the average polynucleotide fragments of each merged bin generated on the chromosome (the ratio is obtained by the number of the reference samples * merged bin) Corresponds to step 3-2) described above);

C) obtaining the CV (Coefficient of Variation) value by the ratio of the number of average reference polynucleotide fragments (corresponding to step 4 described above);

D-1) obtaining a ratio of the weighted average number of test polynucleotide fragments by selecting values corresponding to the upper N _CVs having a lower CV value from the ratio of the average number of test polynucleotide fragments in the step B-1) 5-1));

D-2) Using the numerical values corresponding to the upper N _CVs having the smallest CV value determined in the above step D-1 for the ratio of the average number of reference polynucleotide fragments in step B-2), the weighted average reference poly Obtaining the ratio of the number of nucleotide fragments (corresponding to step 5-2 described above);

E) comparing the ratio of the obtained number of weighted average test polynucleotide fragments to the ratio of the number of weighted average reference polynucleotide fragments (step 6 described above); And

F) confirming whether the target chromosome of the fetus is complementary or not using the comparison result (for example, Z-score) of the ratio of the weighted average number of test polynucleotide fragments obtained in step E) to the weighted average number of polynucleotide fragments Corresponds to step 7) described above.

The computer readable method may further comprise: a) after the steps A-1) and A-2) (and before the steps B-1 and B-2) And removing the bias of the number of reference polynucleotide fragments. The bias removing step may be performed by applying SVD (Singular Value Decomposition) or the like.

The details of each step described above are as described above.

The computer-readable method may be embodied as a computer-executable program on a computer-readable medium.

Another example provides a computer program stored on a computer readable storage medium for executing the steps of the computer readable method. The computer program stored on the computer readable storage medium may be combined with hardware. A computer program stored in the computer-readable storage medium is a program for causing a computer to execute each step of the computer readable method as described above, wherein all of the above steps are executed by one program, Lt; RTI ID = 0.0 > and / or < / RTI >

Another example provides a computer readable storage medium (or recording medium) containing a computer executable instruction for executing the steps of the computer readable method.

The computer-executable program may be stored in a computer-readable storage medium (e.g., memory or the like) and may be embodied in software embodied on one or more processors. As is generally known, a processor may be coupled to one or more controllers, a calculation unit, and / or other units of a computer system, or may be ported to the appropriate firmware. When the program is transferred to the software, it is possible to use a random access memory (RAM), a read only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory Digital storage, solid state drive (SSD), Compact Flash (CF) memory, xD memory, etc.), magnetic disks, laser disks, or other storage media. The program or software stored in the computer-readable storage medium may be stored on a communication channel such as, for example, a telephone line, the Internet, a wireless connection, or the like, or via a transportable medium, such as a computer readable disk, a flash drive, And transmitted to the computer device through a known transmission method.

The various steps as described above may be implemented as a variety of commonly known blocks, operations, tools, modules, and techniques that may be implemented in hardware, firmware, software, or a combination of hardware, firmware, and / . Some or all of the blocks, operations, techniques, etc., when implemented in hardware, may be implemented in a custom IC, an application specific integrated circuit (ASIC), a field programmable logic array (FPGA), a programmable logic array (PLA) And the like. When implemented in software, the software may be stored in a computer-readable medium, such as a magnetic disk, an optical disk, or other storage medium, a RAM or ROM or flash memory, a processor, a hard disk drive, an optical disk drive, Lt; / RTI > In addition, the software may be communicated to a user or computer system through a known delivery method, including, for example, a computer readable disc or other portable computer storage mechanism.

The computer readable method, program, and storage medium may operate in a number of different general purpose or special purpose computing system environments or configurations. The computing system, environment, and / or architecture suitable for executing the computer readable method, program, and storage medium may be, for example, a personal computer (PC), a server computer, a portable or laptop device, Based systems, set top boxes, consumer electronics, network PCs, minicomputers, mainframe computers, and / or remote processing devices that include those systems or devices and are connected via a communication network Distributed computing environments, and the like, and the like. In both an integrated computing environment and a distributed computing environment, program modules may be located in local and remote computer storage media, including memory storage devices.

Computers typically include a variety of computer readable media. The computer-readable medium can be a computer-accessible and usable medium and can include volatile and non-volatile media, removable media, and non-removable media. For example, the computer readable medium may comprise computer storage media and / or communication media.

The computer storage media includes volatile or nonvolatile, and / or portable or non-removable media implemented in a method or technology for storage of information such as computer readable instructions, data structures, program modules and / or other data . Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory (e.g., USB memory, SD memory, SSD, CF memory, xD memory, etc.), magnetic disk, laser disk, or other memory, CD- ) Or other optical disk, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device, or any medium that can be used to store the desired information and is accessible by a computer But is not limited thereto.

The communication medium typically includes computer readable instructions, a data structure, a program module, or an information delivery medium that implements a data transmission or other transport mechanism of a modulated data signal such as a carrier wave information delivery media). The term "modulated data signal" means a signal having one or more characteristics set or changed in such a manner as to code information in the signal. For example, the communication media may be selected from one or more of wired media such as a wired network or direct-wired connection, and wireless media such as acoustic media, RF, infrared and other wireless media .

Combinations of one or more of the above media may also be included within the scope of computer readable media.

The noninvasive fetal chromosomal analysis method proposed in the present specification is a method for analyzing chromosomal integrity of a fetus from DNA sequence information obtained from a biological sample non-invasively isolated from a fetus, And the average number of leads of chromosomes other than the above chromosome is removed to eliminate the deviation between the experiments and the ratio of the weighted average number of interchromosomal leads to the CV (Coefficient of Variation) value is used to improve the reliability and specificity of the result And the probability of false positives can be reduced, so that it can be safely and accurately judged whether or not the chromosomes of the fetus are non-invasively harmless to the fetus.

FIG. 1 is a schematic diagram showing an example of each step of a non-invasive fetal chromosome aberration discrimination method according to an example.
FIG. 2 is a graph showing the Z-score obtained as a result of discrimination of fetal chromosomal integrity, wherein A is the result according to the conventional method and B is according to the method proposed in this specification.
FIG. 3 is a graph showing the manner of removing the GC bias before and after applying the SVD, wherein the Y axis indicates the read count fraction and the X axis indicates the GC content (GC content).

Hereinafter, the present invention will be described in more detail with reference to the following examples, which should not be construed as limiting the scope of the present invention. It will be apparent to those skilled in the art that the embodiments described below may be modified without departing from the essential spirit of the invention.

Example  1: Test sample preparation and nucleotide sequence analysis

Test subjects 10 ml of whole blood from 8 to 28 weeks of pregnancy was collected and 5 ml of plasma was isolated. From the separated plasma, cfDNA (cell-free DAN) was extracted using Qiagen's QIAamp Circulating Nucleic Acid Kit, and NGS library was generated using the extracted cfDNA and sequenced to Illumina's MiSeq NGS instrument to generate FASTQ data Respectively. At this time, the test was carried out so that the lead used had a length of 200 bp.

Example  2: Preparation of base sequence of reference sample

10 ml of whole blood was collected from the mothers (excluding the mothers to be tested), and 5 ml of plasma was separated. With reference to the method of Example 1, cfDNA was extracted from the separated plasma, NGS library was generated using the extracted cfDNA, and FASTQ data was generated by sequencing the NGS library. Among these data, maternal data confirmed to have no fetal chromosomal aberration was selected and used as a reference sample in the following test (reference sample count = 100).

Example  3: Lead count determination

The nucleotide sequence of the lead obtained from the prepared test sample is mapped to the standard genomic base sequence (hg18, hg19, or hg38; provided by NCBI), and about 100 to 30,000 bin numbers including 30,000 to 10,000,000 nucleotides bin number), and a read count vector (S) is generated as follows.

(수식 1) (rc: read count; B(bin 개수)=100~30,000)

(Formula 1) (rc: read count; B (number of bins) = 100 to 30,000)

Using the prepared reference sample base sequence information, a reference read number matrix R was generated as follows:

(수식 2)

(Equation 2)

(수식 3)

(Equation 3)

(B (number of bins): 100 to 30,000; N (number of reference samples): 100)

Example 4: Bias removal

The lead number vector (S) of the obtained test sample and the lead number matrix (R) of the reference sample were removed in the following manner.

First, a matrix X is generated by combining the reference lead number matrix and the test lead number vector, and SVD is performed on the combined matrix as follows:

(수식 4-1)(N: 100)

(Formula 4-1) (N: 100)

(수식 4-2)

(Equation 4-2)

(수식 4-3)

(Equation 4-3)

(B: 100 to 30,000; N: 100)

For the decomposed diagonal matrix D, the top s of singular values that are within 5 ~ 50% of the sum of the singular values are selected,

(수식 5)

(Equation 5)

After generating the diagonal matrix D ^BR with the bias removed by replacing the value of the corresponding singular value in the matrix D by 0,

(수식 6)

(Equation 6)

The matrix D ^BR was used to generate a biased matrix X ^BR :

(수식 7).

(Equation 7).

As described above, the GC content when the bias was removed by applying SVD was measured as the sum of the number of bases of guanine (G) and cytosine (C) per chromosome / the total number of bases per chromosome, This is shown in Fig. 3 in comparison with the case.

As can be seen in FIG. 3, it can be seen that, by applying SVD, the number of polynucleotide fragments (read count) remained constant regardless of the GC content. This result shows that the GC bias is eliminated by SVD application.

Example 5: Calculating the ratio of the number of average polynucleotide fragments

The mb _size , the average size of the Merged Bin, is divided by the total number of autosomes, 22, multiplied by the number of k pre-set, and the bins are combined to have a length of mb _size for each chromosome:

(수식 8).

(Equation 8).

(B = 100 to 30,000, k = 1 to 10)

The mean values of the merged bin j of chromosome 13, 18, or 21, and the chromosome other than the chromosome were determined, and the read count ratio was obtained.

(수식 9).

(Equation 9).

(μ _chri : average number of leads of target chromosome i, μ _mbj is the average number of leads of merged bin j; i: 13, 18, or 21).

The read count ratio for each chromosome (chr _i ) and merged bin was calculated for the experimental sample, and the average read test ratio vector (RCR _chri ) was generated as follows :

(수식 10)

(Equation 10)

(mbm: merged bin number)

For the reference samples, the read count ratio for each chromosome chr _i was calculated and the reference read count ratio matrix for each chromosome was generated as follows:

(Equation 11)

Example 6 Calculation of Coefficient of Variation (CV)

CV for each chromosome and merged bin lead count (RCR _{i, j} ) for the reference sample was calculated as follows:

(수식 12)

(Equation 12)

(σRCR _{n, mbm} : Standard deviation of the number of merged bin lead counts for each chromosome, μRCR _{n, mbm} calculated for the reference sample group, and the number of merged bin-specific leads for each reference chromosome group Average of rain)

Example 7: Calculation of the weighted average number of leads

In Example 6, each chromosome chr _i The number of lead N _CVs with the smallest CV value is selected based on the CV value calculated for the reference sample, and then the average lead number ratio of the test sample is weighted by the CV value corresponding to the lead number The average weighted average number of readings ratio was calculated by the following equation (13).

(Equation 13)

In the present embodiment, N _CV is Cv _chri To the number of leads having a value 1.1 to 5 times larger than the minimum value of the number of leads. ,

For the average lead count values of the reference samples, the same procedure as above was performed on the lead count value of the upper N _{CV of} each chromosome to generate the reference weighted average lead count vector as follows:

(14)

Example 8: Judgment of fetal chromosomal integrity

The Z-score was calculated as follows by comparing the weighted average number of leads of the chromosome experimental sample obtained in Example 7 and the weighted average number of leads of the reference sample:

(수식 15)

(Equation 15)

: 가중 평균 참조 폴리뉴클레오타이드 단편 수 비율 벡터의 평균;

: 가중 평균 참조 폴리뉴클레오타이드 단편 수 비율 벡터의 표준 편차) (

: Average of weighted average reference polynucleotide fragment number ratio vector;

: Standard deviation of weighted average reference polynucleotide fragment number ratio vector)

If the absolute value of the Z-score is 3 or more, it is determined that the fetus chromosome of the corresponding sample is biologically stable.

(수식 16)

(Expression 16)

The results obtained above are shown in Fig. 2 (lower part). For comparison, the step of removing the bias by applying the SVD of Example 4 [step a)], the step of calculating the ratio of the number of polynucleotide fragments of Example 5 [Step 3-1] and the step of 3-2) (Step 4), 5-1), and 5-2) of calculating the weighted average number of leads in Example 7, and calculating the weighted average number of leads in Example 7 -2)) and the reference lead number matrix obtained from the test lead number vector obtained in the step (2)), the Z-score was calculated by referring to Example 8 [Step 6] without an intermediate step and the fetal chromosomal integrity was determined 2 (upper part).

As shown in FIG. 2, the fetal chromosomal aberration was determined from maternal blood according to the method of this embodiment. As a result, in the conventional method, three of the 17 test samples confirmed to have fetal chromosomal integrity were not fetal chromosomal insoluble . However, the method according to the present embodiment judges that all of the 17 test samples have fetal chromosomal integrity. These results show that the accuracy of fetal chromosomal discrimination is improved by the method according to the present embodiment.

Claims (12)

A method for analyzing sequence information for chromosomal integration of a fetus, comprising the steps of:
1-1) obtaining sequence information of polynucleotide fragments covering the whole genome from a test sample which is blood, plasma or serum separated from a mother;
1-2) obtaining sequence information of polynucleotide fragments covering the entire genome of the reference sample;
2-1) Sequence information of the polynucleotide fragments of the test sample obtained in the above step 1-1) is mapped to a reference genome sequence, and a bin number previously set for each chromosome is calculated Determining a polynucleotide fragment count to have the polynucleotide fragment count,
2-2) determining the number of reference polynucleotide fragments so as to have a predetermined bin number using sequence information of the polynucleotide fragments of the reference sample prepared in the step 1-2);
3-1) The number of the average polynucleotide fragments of the target chromosome to be tested for the number of the test polynucleotide fragment water is selected from the number of n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome Obtaining a ratio of the number of the average polynucleotide fragments of each merged bin generated on the chromosome to the number of the average number of test polynucleotide fragments (the ratio is obtained by the number of merged bins);
3-2) The number of the average polynucleotide fragments of the target chromosome to be tested for isolation in the reference polynucleotide fragment water is calculated by dividing the number of n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome Obtaining a ratio of the number of average polynucleotide fragments to the number of average polynucleotide fragments of each merged bin generated on chromosomes (the ratio is obtained by number of reference samples * merged bin);
4) obtaining a CV (Coefficient of Variation) value by the ratio of the number of average reference polynucleotide fragments, wherein CV (CV _chri ) for the i-th chromosome i can be obtained by the following equation 12,

(Equation 12)
In the above equation, σRCR _{n and mbm} represent standard deviations of the number of merged bin-specific lead counts for each chromosome and reference samples, μRCR _{n and mbm} represent merged represents the average of the bin lead readings;
5-1) Selecting the values corresponding to the upper N _CVs having a smaller CV value for the CV value calculated in the step 4) from the ratio of the average number of test polynucleotide fragments in the step 3-1) 3-1) to the ratio of the average number of test polynucleotide fragments to the reciprocal of the CV value calculated in step 4) to obtain a weighted average number of test polynucleotide fragments;
5-2) Among the ratio of the average number of reference polynucleotide fragments in step 3-2), values corresponding to the upper N _CVs having a smaller CV value are selected from the CV values calculated in step 4) -2) to the ratio of the average number of reference polynucleotide fragments to the reciprocal of the CV value calculated in step 4) to obtain a weighted average number of reference polynucleotide fragments; And
6) comparing the ratio of the weighted average number of test polynucleotide fragments obtained to the weighted average number of reference polynucleotide fragments obtained. The method according to claim 1, wherein after the steps 2-1) and 2-2)
a) removing the bias of the number of test polynucleotide fragments and the number of reference polynucleotide fragments
/ RTI > The method of claim 1, further comprising: 3. The method of claim 2, wherein the bias elimination is performed by applying SVD (Singular Value Decomposition). ◈ Claim 4 is abandoned due to the registration fee. 4. The method according to any one of claims 1 to 3, wherein the chromosome is autosomal. ◈ Claim 5 is abandoned due to the registration fee. 4. The method according to any one of claims 1 to 3, wherein the target chromosome is chromosome 13, 18 or 21 of human. ◈ Claim 6 is abandoned due to the registration fee. 4. The method according to any one of claims 1 to 3, wherein the mother does not have the desired chromosomal complementarity. 4. An information processing system comprising means adapted to carry out the method for analyzing sequence information according to any one of claims 1 to 3. A computer readable method for determining chromosomal integrity of a fetus comprising the steps of:
A-1) The sequence information of the polynucleotide fragments of the test sample is mapped to the reference genome sequence, and the number of test polynucleotide fragments (bin number) determining a fragment count,
A-2) determining the number of reference polynucleotide fragments so as to have a preset bin number using sequence information of polynucleotide fragments of the reference sample;
B-1) The number of the average number of polynucleotide fragments of the target chromosome to be tested, of which number is n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome in the test polynucleotide fragment water Obtaining a ratio of the number of average polynucleotide fragments of each merged bin generated from the chromosome to the number of average number of test polynucleotide fragments (the ratio is obtained by number of merged bins);
B-2) The number of the average polynucleotide fragments of the target chromosome to be tested for the number of the reference polynucleotide fragments, wherein n (n is an integer selected from 1 to 21) selected from the chromosomes other than the target chromosome Obtaining a ratio of the number of average polynucleotide fragments to the number of average polynucleotide fragments of each merged bin generated on chromosomes (the ratio is obtained by number of reference samples * merged bin);
C) obtaining a value of CV (Coefficient of Variation) according to the ratio of the number of average reference polynucleotide fragments, wherein CV (CV _chri ) for the i-th chromosome i can be obtained by the following equation (12)

(Equation 12)
In the above equation, σRCR _{n and mbm} represent standard deviations of the number of merged bin-specific lead counts for each chromosome and reference samples, μRCR _{n and mbm} represent merged represents the average of the bin lead readings;
D-1) selecting the values corresponding to the upper N _CVs having a smaller CV value for the CV values calculated in the step C) from the ratio of the average number of test polynucleotide fragments in the step B-1) Multiplying the ratio of the number of the average number of test polynucleotide fragments of B-1) to the reciprocal of the CV value calculated in step C) to obtain a weighted average number of test polynucleotide fragments;
D-2) Among the ratios of the average number of reference polynucleotide fragments in the step B-2), values corresponding to the upper N _CVs having a smaller CV value are selected for the CV values calculated in the step C) -2) to the ratio of the average number of reference polynucleotide fragments to the reciprocal of the CV value calculated in step 4) to obtain a weighted average number of reference polynucleotide fragments;
E) comparing the ratio of the number of weighted average test polynucleotide fragments obtained to the weighted average number of reference polynucleotide fragments obtained; And
F) confirming whether or not the target chromosome of the fetus is complementary, using the ratio of the weighted average number of test polynucleotide fragments obtained in the step E) to the weighted average number of polynucleotide fragments obtained in the step E). ◈ Claim 9 is abandoned upon payment of registration fee. 9. The method according to claim 8, wherein after steps A-1) and A-2)
a) applying a Singular Value Decomposition (SVD) to remove the bias of the number of test polynucleotide fragments and the number of reference polynucleotide fragments. A computer program stored in a computer-readable storage medium for executing the steps of the computer readable method of claim 8 or 9 in combination with hardware. ◈ Claim 11 is abandoned due to registration fee. 9. The computer readable method according to claim 8, wherein the chromosome is an autosomal. ◈ Claim 12 is abandoned due to registration fee. 9. The computer readable method according to claim 8, wherein the target chromosome is chromosome 13, 18 or 21 of human.

Images