WO2021077411A1 - Chromosome instability detection method, system and test kit - Google Patents

Abstract

A chromosome instability detection method, a system and a test kit, the detection method comprising: dividing a reference DNA sequence into a number n of regions; sequencing extracted DNA in a sample, obtaining a DNA sequence including multiple fragments; comparing the obtained DNA sequence and the reference DNA sequence, and outputting all fragment numbers, fragment start and termination positions and fragment nucleobase comparison results having a high degree of similarity; counting a number Ci of all highly similar fragments falling within a region numbered i; performing normalization of Ci; determining whether a normalized number Sigi is significantly higher than normalized numbers for other regions, and if so, reporting that an abnormality has occurred for region number i and the normalized number Sigi, and acquiring normalized numbers corresponding to all regions of the n regions in the present sample for which an abnormality has occurred; acquiring a sample again, and obtaining normalized numbers corresponding to abnormalities in the newly acquired sample; calculating a degree of similarity value for detection values of the former and latter samples; if the degree of similarity value is greater than a set value, outputting a chromosome instability detection result.

Description

Chromosome instability detection method, system and kit Technical field

The invention relates to the technical field of chromosome instability, in particular to a chromosome instability detection method and system and a kit containing the chromosome instability detection system.

Background technique

Chromosomal instability is usually associated with tumors, specifically including the deletion or amplification of copies of entire chromosomes or chromosomal fragments. The amplification and deletion of chromosomes or chromosome fragments that are related to tumorigenesis are often unique to tumorigenesis. The detection of chromosomal unstable regions of tumors is very important for the study of tumorigenesis and the development of tumor diagnostic technology.

Summary of the invention

In view of the problems and deficiencies in the prior art, the present invention provides a novel chromosome instability detection method, system and kit.

The present invention solves the above technical problems through the following technical solutions:

The present invention provides a method for detecting chromosome instability, which is characterized in that it includes the following steps:

S1. Perform interval division on all fragments of the reference DNA sequence to divide n intervals;

S2. Obtain a sample, extract the required DNA from the obtained sample, perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and number each fragment;

S3. Compare all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence one by one, and output all the fragments of the DNA sequence that are highly similar to the corresponding fragments of the reference DNA sequence, the fragment number, the starting position of the fragment, The result of comparing the end position of the fragment and the base of the fragment one by one;

_{S4. Count the number C i of} all highly similar fragments that fall into the i-th interval of the reference DNA sequence, 1≤i≤n;

S5. Perform normalization processing on the number C _i to obtain a normalized number Sig _i ;

S6. Determine whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals, and if so, report the abnormality in the i-th interval and report the normalized number Sig _i at the same time, thereby Obtain the normalized number corresponding to all the intervals in which abnormality occurs in the n intervals of the sample;

S7. Obtain the sample again, and obtain the normalized number corresponding to all the intervals in which the abnormality of the second sample occurs in the n intervals;

S8. Calculate the similarity value of the detection values of the two samples before and after based on the normalized number corresponding to the two samples before and after;

S9. Determine whether the similarity value is greater than the set value, and if so, output the chromosome instability detection result.

Preferably, step S6 is replaced by: performing interval combinations on n intervals to obtain k interval combinations, calculating the normalized number Sig _r corresponding to each interval combination in the k interval combinations, and judging the normalization of the r-th interval combination Whether the normalized number Sig _r is significantly higher than the normalized number of other k-1 interval combinations, if so, report the abnormality in the rth interval and report the normalized number Sig _r at the same time, so as to obtain the sample in k intervals The normalized number corresponding to all interval combinations in which anomalies occur in the combination, 1≤r≤k, 1≤k≤n;

Step S7 is replaced with: obtaining the sample again, obtaining the normalized number corresponding to all the interval combinations in which the abnormality occurs in the k interval combinations for the second sample.

Preferably, in step S2, the sample includes blood, biological tissue, exfoliated cells, intercellular fluid, pleural effusion, saliva or cerebrospinal fluid, and DNA sequence sequencing is performed using a DNA sequence sequencing system.

Preferably, in step S5, the algorithm used for normalization is a GC content algorithm, a secondary structure algorithm, or other DNA sequence feature algorithm.

Preferably, in step S6, _{the statistical method for judging whether the normalized number Sig i of the i} -th interval is significantly higher than the normalized number of _{other n-1 intervals includes that Sig i} deviates from other n-1 intervals The mean of the normalized number is not less than 2 times the variance.

Preferably, in step S6, _{the statistical method for judging whether the normalized number Sig r of the r} -th interval combination is significantly higher than the normalized number of other k-1 interval combinations includes a T-test algorithm.

Preferably, in step S8, the statistical calculation method for calculating the similarity value of the two sample detection values before and after includes but not limited to Pearson correlation coefficient and rank correlation coefficient.

The present invention also provides a chromosome instability detection system, which is characterized in that it includes a division module, an acquisition module, a comparison module, a counting module, a processing module, a judgment module, a calculation module and an output module;

The dividing module is used to divide all segments of the reference DNA sequence into intervals to divide n intervals;

The collection module is used to collect samples, extract the required DNA from the collected samples, and perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and perform fragment numbering on each fragment ；

The comparison module is used to compare all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence one by one, and output the fragment numbers and fragments of all the fragments of the DNA sequence that are highly similar to the corresponding fragments of the reference DNA sequence. The start position, the end position of the fragment and the base of the fragment are compared one by one;

_{The counting module is used to count the number C i of} all highly similar fragments falling in the i-th interval of the reference DNA sequence, 1≤i≤n;

The processing module is used _{for normalizing the number C i} to obtain the normalized number Sig _i ;

The judgment module is used to judge whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals, and if so, report that the i-th interval is abnormal and report the normalized number at the same time Sig _i , so as to obtain the normalized number corresponding to all the intervals in which abnormality occurs in the n intervals of this sample;

Calling the acquisition module, comparison module, counting module, processing module, and judgment module again to collect samples again, and obtain the normalized numbers corresponding to all the intervals in which the abnormality of the re-sample occurs in the n intervals;

The calculation module is configured to calculate the similarity value of the detection value of the two samples before and after based on the normalized number corresponding to the two samples before and after;

The output module is used to determine whether the similarity value is greater than the set value, and if so, output the chromosome instability detection result.

Preferably, the system further includes a combination module, which is used to combine n intervals to obtain k interval combinations, and calculate the normalized number Sig _{r corresponding to each interval combination in the k interval combinations.} ；

The judgment module is used to judge whether the normalized number Sig _{r of the r} -th interval combination is significantly higher than the normalized number of other k-1 interval combinations, and if so, report an abnormality in the r-th interval and report the normalization at the same time Reduce the number Sig _r , so as to obtain the normalized number corresponding to all the interval combinations in the k interval combinations of this sample, 1≤r≤k, 1≤k≤n;

The acquisition module, the comparison module, the counting module, the processing module, the combination module, and the judgment module are called again to acquire the samples again to obtain the normalized numbers corresponding to all the interval combinations in which the re-samples are abnormal in the k interval combinations.

Preferably, the sample includes blood, biological tissue, exfoliated cells, intercellular fluid, pleural effusion, saliva, or cerebrospinal fluid, and DNA sequence sequencing is performed using a DNA sequence sequencing system.

Preferably, the algorithm used for normalization is a GC content algorithm, a secondary structure algorithm, or other DNA sequence feature algorithm.

Preferably, the judging module is used to judge whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals. The statistical method used includes the _{deviation of Sig i} from other n- The mean value of the normalized number of an interval is not less than 2 times the variance.

Preferably, the statistical method used by the judging module for judging whether the normalized number Sig _{r of the r} -th interval combination is significantly higher than the normalized number of other k-1 interval combinations includes a T-test algorithm.

Preferably, the statistical calculation method used by the calculation module to calculate the similarity value of the detection values of the two samples before and after includes but not limited to Pearson correlation coefficient and rank correlation coefficient.

The present invention also provides a kit, which is characterized in that it includes the above-mentioned chromosome instability detection system.

On the basis of conforming to common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain preferred examples of the present invention.

The positive and progressive effects of the present invention are:

The invention collects samples, can accurately detect chromosomal unstable regions in the samples, and provides scientific basis for the next stage of clinical early diagnosis and formulation of individualized treatment plans.

Description of the drawings

Fig. 1 is a flowchart of a method for detecting chromosome instability according to Embodiment 1 of the present invention.

Figure 2 is a structural block diagram of the chromosome instability detection system according to Embodiment 1 of the present invention.

Fig. 3 is a flow chart of the method for detecting chromosome instability according to Embodiment 2 of the present invention.

Fig. 4 is a structural block diagram of a chromosome instability detection system according to Embodiment 2 of the present invention.

Detailed ways

In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

Example 1

As shown in Figure 1, this embodiment provides a method for detecting chromosome instability, which includes the following steps:

Step 101: Perform interval division on all fragments of the reference DNA sequence to divide n intervals.

Step 102: Obtain a sample, extract the required DNA from the obtained sample, perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and perform fragment numbering on each fragment;

In step 102, the sample includes blood, biological tissue, exfoliated cells, intercellular fluid, pleural effusion, saliva, or cerebrospinal fluid.

DNA sequence sequencing is performed using a DNA sequence sequencing system, which includes but is not limited to Illumina HiSeq, NextSeq, Life Ion, BGISEQ200, BGISEQ500, BGISEQ2000, PacBio, MinIon, Nanopore, RocheSolid, etc.

Step 103: Perform a base-by-base comparison of all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence, and output the fragment numbers and the starting positions of the fragments that are highly similar to the corresponding fragments of the reference DNA sequence. , Fragment termination position and fragment base one-by-one comparison result.

_{Step 104: Count the number C i of} all highly similar fragments that fall into the i-th interval of the reference DNA sequence, and 1≤i≤n.

_{Step 105:} Perform normalization processing on the number C i to obtain a normalized number Sig _i .

In step 105, the algorithm used for normalization is a GC content algorithm, a secondary structure algorithm, or other DNA sequence feature algorithm.

Step 106: Determine whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals, and if so, report an abnormality in the i-th interval and report the normalized number Sig _i at the same time, In this way, the normalized number corresponding to all the intervals in which abnormality occurs in the n intervals of the sub-sample is obtained.

In step 106, the statistical methods for judging whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals include, but are not limited to, the _{deviation of Sig i} from other n-1 intervals. The mean of the normalized number is not less than 2 times the variance.

Step 107: Obtain the samples again, and obtain the normalized numbers corresponding to all the intervals in which the abnormality occurs in the n intervals of the second sample.

Step 108: Calculate the similarity value of the detection value of the two samples before and after based on the normalized number corresponding to the two samples before and after.

In step 108, statistical calculation methods for calculating the similarity values of the detection values of the two samples before and after include but are not limited to Pearson correlation coefficient and rank correlation coefficient.

Step 109: Determine whether the similarity value is greater than the set value, and if so, output the chromosome instability detection result.

As shown in Figure 2, this embodiment also provides a chromosome instability detection system, which includes a dividing module 11, an acquisition module 12, a comparison module 13, a counting module 14, a processing module 15, a judgment module 16, a calculation module 17, and Output module 18.

The dividing module 11 is used to divide all segments of the reference DNA sequence to divide n intervals.

The collection module 12 is used to collect samples, extract the required DNA from the collected samples, and perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and perform fragmentation on each fragment. Numbering. Samples include blood, biological tissues, exfoliated cells, intercellular fluid, pleural effusion, saliva or cerebrospinal fluid, and DNA sequence sequencing is performed using a DNA sequencing system.

The comparison module 13 is used to compare all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence one by one, and output the fragment numbers of all the fragments of the DNA sequence that are highly similar to the corresponding fragments of the reference DNA sequence, Fragment start position, fragment end position and fragment base are compared one by one.

_{The counting module 14 is used to count the number C i of} all highly similar fragments falling in the i-th interval of the reference DNA sequence, 1≤i≤n.

The processing module 15 is used to _{normalize the number C i} to obtain the normalized number Sig _i , and the algorithm used for the normalization is a GC content algorithm, a secondary structure algorithm or other DNA sequence feature algorithms.

The judgment module 16 is used to judge whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals, and if so, report the abnormality in the i-th interval and report the normalization at the same time The number Sig _i , so as to obtain the normalized number corresponding to all the intervals in the n intervals in which the sample is abnormal.

The judgment module 16 is used to judge whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals. The statistical method used includes the _{deviation of Sig i} from other n-1 intervals. The mean of the normalized number is not less than 2 times the variance.

The collection module 12, the comparison module 13, the counting module 14, the processing module 15 and the judgment module 16 are called again to collect the samples again, and obtain the normalized numbers corresponding to all the intervals in which the abnormality of the re-samples occurs in the n intervals.

The calculation module 17 is configured to calculate the similarity value of the detection value of the two samples before and after based on the normalized number corresponding to the two samples before and after. The statistical calculation method used by the calculation module to calculate the similarity value of the detection value of the two samples before and after includes but not limited to the Pearson correlation coefficient and the rank correlation coefficient.

The output module 18 is used to determine whether the similarity value is greater than the set value, and if so, output the chromosome instability detection result.

This embodiment also provides a kit, which includes the above-mentioned chromosome instability detection system.

Example 2

As shown in FIG. 3, this embodiment provides a method for detecting chromosome instability, which includes the following steps:

Step 201: Perform interval division on all fragments of the reference DNA sequence to divide n intervals.

Step 202: Obtain a sample, extract the required DNA from the obtained sample, perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and perform fragment numbering on each fragment;

In step 202, the sample includes blood, biological tissue, exfoliated cells, intercellular fluid, pleural effusion, saliva, or cerebrospinal fluid.

DNA sequence sequencing is performed using a DNA sequence sequencing system, which includes but is not limited to Illumina HiSeq, NextSeq, Life Ion, BGISEQ200, BGISEQ500, BGISEQ2000, PacBio, MinIon, Nanopore, RocheSolid, etc.

Step 203: Perform a base-by-base comparison of all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence, and output the fragment numbers and the starting positions of the fragments that are highly similar to the corresponding fragments of the reference DNA sequence. , Fragment termination position and fragment base one-by-one comparison result.

_{Step 204: Count the number C i of} all highly similar fragments that fall into the i-th interval of the reference DNA sequence, and 1≤i≤n.

_{Step 205:} Perform normalization processing on the number C i to obtain a normalized number Sig _i .

In step 205, the algorithm used for normalization is a GC content algorithm, a secondary structure algorithm, or other DNA sequence feature algorithm.

Step 206, the n intervals for interval combinations to obtain k intervals combination, calculate the k on interval of each interval corresponding to a combination of a normalized number Sig _r, determines the r th interval combinations normalized number Sig _r whether Significantly higher than the normalized number of other k-1 interval combinations. If so, report the r-th interval with an abnormality and report the normalized number Sig _r at the same time, so as to obtain all the abnormalities in the k interval combinations of this sample The normalized number corresponding to the interval combination, 1≤r≤k, 1≤k≤n.

In step 206, the statistical method for judging whether the normalized number Sig _{r of the r} -th interval combination is significantly higher than the normalized number of other k-1 interval combinations includes, but is not limited to, the T-test algorithm.

Step 207: Obtain the sample again, and obtain the normalized number corresponding to all the interval combinations in which the abnormality occurs in the k interval combinations for the second sample.

Step 208: Calculate the similarity value of the detection value of the two samples before and after based on the normalized number corresponding to the two samples before and after.

In step 208, statistical calculation methods for calculating the similarity values of the detection values of the two samples before and after include but are not limited to Pearson correlation coefficient and rank correlation coefficient.

Step 209: Determine whether the similarity value is greater than the set value, and if so, output the chromosome instability detection result.

As shown in Figure 4, this embodiment also provides a chromosome instability detection system, which includes a dividing module 21, an acquisition module 22, a comparison module 23, a counting module 24, a processing module 25, a combination module 26, a judgment module 27, Calculation module 28 and output module 29.

The dividing module 21 is used to divide all segments of the reference DNA sequence to divide n intervals.

The collection module 22 is used to collect samples, extract the required DNA from the collected samples, and perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and perform fragmentation on each fragment. Numbering. Samples include blood, biological tissues, exfoliated cells, intercellular fluid, pleural effusion, saliva or cerebrospinal fluid, and DNA sequence sequencing is performed using a DNA sequencing system.

The comparison module 23 is used to compare all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence one by one, and output the fragment numbers of all the fragments of the DNA sequence that are highly similar to the corresponding fragments of the reference DNA sequence. Fragment start position, fragment end position and fragment base are compared one by one.

_{The counting module 24 is used to count the number C i of} all highly similar fragments falling in the i-th interval of the reference DNA sequence, 1≤i≤n.

The processing module 25 is used to _{normalize the number C i} to obtain the normalized number Sig _i , and the algorithm used for the normalization is a GC content algorithm, a secondary structure algorithm or other DNA sequence feature algorithms.

The combination module 26 is configured to perform interval combinations on n intervals to obtain k interval combinations, and calculate the normalized number Sig _r corresponding to each interval combination in the k interval combinations.

The judging module 27 is used to judge whether the normalized number Sig _{r of} the rth interval combination is significantly higher than the normalized number of other k-1 interval combinations, and if so, report that the rth interval is abnormal and report the normalized number at the same time. The number Sig _r is unified, so as to obtain the normalized number corresponding to all the interval combinations in the k interval combinations of the sample, 1≤r≤k, 1≤k≤n.

The judging module 27 is used for judging whether the normalized number Sig _{r of the r} -th interval combination is significantly higher than the normalized number of other k-1 interval combinations. The statistical methods used include but are not limited to the T-test algorithm.

The collection module 22, the comparison module 23, the counting module 24, the processing module 25, the combination module 26, and the judgment module 27 are called again to collect the samples again to obtain the results corresponding to all the interval combinations in which the abnormality occurs in the k interval combinations. A number.

The calculation module 28 is configured to calculate the similarity value of the detection value of the two samples before and after based on the normalized number corresponding to the two samples before and after. The statistical calculation method used by the calculation module to calculate the similarity value of the detection value of the two samples before and after includes but not limited to the Pearson correlation coefficient and the rank correlation coefficient.

The output module 29 is used for judging whether the similarity value is greater than the set value, and if so, outputting the chromosome instability detection result.

This embodiment also provides a kit, which includes the above-mentioned chromosome instability detection system.

Although the specific embodiments of the present invention are described above, those skilled in the art should understand that these are only examples, and the protection scope of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principle and essence of the present invention, but these changes and modifications all fall within the protection scope of the present invention.

Claims (15)

A method for detecting chromosome instability, which is characterized in that it comprises the following steps: S1. Perform interval division on all fragments of the reference DNA sequence to divide n intervals; S2. Obtain a sample, extract the required DNA from the obtained sample, perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and number each fragment; S3. Compare all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence one by one, and output all the fragments of the DNA sequence that are highly similar to the corresponding fragments of the reference DNA sequence, the fragment number, the starting position of the fragment, The result of comparing the end position of the fragment and the base of the fragment one by one; _{S4. Count the number C i of} all highly similar fragments that fall into the i-th interval of the reference DNA sequence, 1≤i≤n; S5. Perform normalization processing on the number C _i to obtain a normalized number Sig _i ; S6. Determine whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals, and if so, report the abnormality in the i-th interval and report the normalized number Sig _i at the same time, thereby Obtain the normalized number corresponding to all the intervals in which abnormality occurs in the n intervals of the sample; S7. Obtain the sample again, and obtain the normalized number corresponding to all the intervals in which the abnormality of the second sample occurs in the n intervals; S8. Calculate the similarity value of the detection values of the two samples before and after based on the normalized number corresponding to the two samples before and after; S9. Determine whether the similarity value is greater than the set value, and if so, output the chromosome instability detection result. The method for detecting chromosome instability according to claim 1, wherein step S6 is replaced by: performing interval combinations on n intervals to obtain k interval combinations, and calculating the corresponding index for each interval combination in the k interval combinations. the number a of Sig _r, determines whether or not the r-th sections combined normalized number Sig _r significantly higher than the number of the other k-1 intervals composition normalized, if the report r intervals abnormality and also reported normalized Reduce the number Sig _r , so as to obtain the normalized number corresponding to all the interval combinations in the k interval combinations of this sample, 1≤r≤k, 1≤k≤n; Step S7 is replaced with: obtaining the sample again, obtaining the normalized number corresponding to all the interval combinations in which the abnormality occurs in the k interval combinations for the second sample. The method for detecting chromosome instability according to claim 1 or 2, wherein in step S2, the sample includes blood, biological tissue, exfoliated cells, intercellular fluid, pleural effusion, saliva, or cerebrospinal fluid, using DNA sequence The sequencing system performs DNA sequence sequencing. The method for detecting chromosome instability according to claim 1 or 2, wherein in step S5, the algorithm used for normalization is a GC content algorithm, a secondary structure algorithm, or other DNA sequence feature algorithms. The method for detecting chromosome instability according to claim 1, wherein in step S6, it is determined whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals The statistical method includes that the _{deviation of Sig i} from the mean of the normalized number of other n-1 intervals is not less than 2 times the variance. The method for detecting chromosome instability according to claim 2, wherein in step S6, it is determined whether the normalized number Sig _{r of the r} -th interval combination is significantly higher than that of other k-1 interval combinations. Statistical methods for changing the number include the T-test algorithm. The method for detecting chromosome instability according to claim 1 or 2, characterized in that, in step S8, the statistical calculation method for calculating the similarity value of the two sample detection values before and after includes but not limited to Pearson correlation coefficient, rank Correlation coefficient. A chromosome instability detection system, which is characterized in that it includes a division module, an acquisition module, a comparison module, a counting module, a processing module, a judgment module, a calculation module, and an output module; The dividing module is used to divide all segments of the reference DNA sequence into intervals to divide n intervals; The collection module is used to collect samples, extract the required DNA from the collected samples, perform DNA sequence sequencing on the extracted DNA to obtain a set of DNA sequences containing multiple fragments, and number each fragment ； The comparison module is used to compare all the fragments of the obtained DNA sequence with the corresponding fragments of the reference DNA sequence one by one, and output the fragment numbers and fragments that are highly similar to the corresponding fragments of the reference DNA sequence. The start position, the end position of the fragment and the base of the fragment are compared one by one; _{The counting module is used to count the number C i of} all highly similar fragments falling in the i-th interval of the reference DNA sequence, 1≤i≤n; The processing module is used _{for normalizing the number C i} to obtain the normalized number Sig _i ; The judgment module is used to judge whether the normalized number Sig _{i of the i} -th interval is significantly higher than the normalized number of other n-1 intervals, and if so, report that the i-th interval is abnormal and report the normalized number at the same time Sig _i , so as to obtain the normalized number corresponding to all the intervals in which abnormality occurs in the n intervals of this sample; Calling the acquisition module, comparison module, counting module, processing module, and judgment module again to collect samples again, and obtain the normalized numbers corresponding to all the intervals in which the abnormality of the re-sample occurs in the n intervals; The calculation module is configured to calculate the similarity value of the detection value of the two samples before and after based on the normalized number corresponding to the two samples before and after; The output module is used to judge whether the similarity value is greater than the set value, and if so, output the chromosome instability detection result. The chromosome instability detection system according to claim 8, wherein the system further comprises a combination module, the combination module is used to combine n intervals to obtain k interval combinations, and calculate k intervals _{The normalized number Sig r} corresponding to each interval combination in the combination; The judgment module is used to judge whether the normalized number Sig _{r of the r} -th interval combination is significantly higher than the normalized number of other k-1 interval combinations, and if so, report an abnormality in the r-th interval and report the normalization at the same time Reduce the number Sig _r , so as to obtain the normalized number corresponding to all the interval combinations in the k interval combinations of this sample, 1≤r≤k, 1≤k≤n; The acquisition module, the comparison module, the counting module, the processing module, the combination module, and the judgment module are called again to collect the samples again to obtain the normalized numbers corresponding to all the interval combinations in which the re-samples are abnormal in the k interval combinations. The chromosome instability detection system according to claim 8 or 9, wherein the sample includes blood, biological tissue, exfoliated cells, intercellular fluid, pleural effusion, saliva, or cerebrospinal fluid, and the DNA sequence is performed using a DNA sequence sequencing system Sequencing. The chromosome instability detection system according to claim 8 or 9, wherein the algorithm used for normalization is a GC content algorithm, a secondary structure algorithm, or other DNA sequence feature algorithm. The chromosome instability detection system according to claim 8, wherein the judgment module is used to judge whether the normalized number Sig _{i of the i} -th interval is significantly higher than that of other n-1 intervals. The statistical method used for the number includes that the _{deviation of Sig i} from the mean value of the normalized number of other n-1 intervals is not less than 2 times the variance. The chromosome instability detection system according to claim 9, wherein the judgment module is used to judge whether the normalized number Sig _{r of the r} -th interval combination is significantly higher than that of other k-1 interval combinations. The statistical methods used to change the number include the T-test algorithm. The chromosome instability detection system according to claim 8 or 9, wherein the statistical calculation method used by the calculation module to calculate the similarity value of the test values of the two samples before and after includes but not limited to Pearson correlation coefficient, Rank correlation coefficient. A kit, characterized in that it comprises the chromosome instability detection system according to any one of claims 8-14.

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