Method and device for determining group CNV

Abstract

The invention provides a method and device for determining a group CNV. The method comprises: (1) obtaining sequencing data of a plurality of samples and a corresponding region of at least one gene in a reference genome, the sequencing data comprising a sequence of a sequencing read segment and positioning information of the sequencing read segment on the reference genome; (2) for each given gene of the at least one gene, determining the sequencing depth of the plurality of samples in a corresponding region of each given gene; (3) determining a polymorphism distribution index of the given gene based on the sequencing depth of each of the plurality of samples in the given gene region; and (4) determining whether the given gene belongs to a population CNV based on the difference between the polymorphism distribution index and a first predetermined threshold. Through the method, rapid and extremely-low-memory group CNV detection is realized.

Description

Technical Field

[0001] The present application relates to the field of biotechnology, specifically to the field of genomics, and more specifically to a method, device, computer program product, server, and computer-readable storage medium for determining CNV of a population. Background Art

[0002] In recent years, with the rapid development and widespread application of genomic technology, the importance of copy number variation (CNV) in biological processes has gradually been recognized. CNV refers to the increase or decrease in the number of copies of large fragment sequences on the genome, which is a common form of genomic structural variation. CNV affects gene function by changing gene dosage and transcription structure, thereby regulating the plasticity of organisms, providing the main genetic basis for individual phenotypic diversity and population adaptive evolution. In the past few decades, CNV has been widely studied and has been confirmed in the etiology of many human diseases. However, due to the complexity and diversity of CNV, there are still many challenges in studying its function and mechanism of action. At present, CNVs with significant effects on phenotypes can be identified through intraspecific comparative genomic analysis. Therefore, in-depth research on CNV will help us better understand the impact of genomic structural variation on life processes and provide strong support for the protection of human health and biodiversity. However, there is currently a lack of effective solutions for the detection of population CNVs.

[0003] Therefore, there is an urgent need in this field to develop a new method to detect and analyze population CNVs. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0005] At present, there is no method for detecting gene CNVs, let alone methods for detecting group gene CNVs. Currently, CNV detection is mainly carried out through CNVnator software. CNVnator detects CNVs in the whole genome of an individual by sliding a window on the genome, that is, it needs to read all sequencing reads in the bam file. When there are multiple samples, the amount of data in the bam file is very large, which makes CNVnator detection extremely slow. In addition, CNV is detected for a sample, and it needs to be integrated into the group CNV format later. Furthermore, the bin_size is set to 100 by default, and the results obtained by CNVnator are screened at the same time, the multi-copy regions in the genome are removed, and the Read Depth value is set at the same time (the Read Depth of the normal chromosome region is defined as 1), and the regions with RD values ​​≤0.05 or ≥1.8 are retained.

[0006] In view of the shortcomings of the prior art, such as the lack of methods for studying population CNVs and the slow detection speed, the inventors have developed a method suitable for automatically studying population CNVs using population resequencing data (without the need to frequently adjust the bin size parameter). This method has low requirements on computer performance and can quickly complete the detection and analysis of gene copy number variations.

[0007] To this end, in the first aspect of the present application, the present application proposes a method for determining a population CNV. According to an embodiment of the present application, the method includes: (1) obtaining sequencing data of multiple samples and corresponding regions of at least one gene in a reference genome, wherein the sequencing data includes sequences of sequencing reads and location information of the sequencing reads on the reference genome; (2) for each given gene of the at least one gene, determining the sequencing depth of the multiple samples in the corresponding regions of each of the given genes; (3) based on the sequencing depth of each of the multiple samples in the given gene region, determining the polymorphism distribution index of the given gene; and (4) based on the difference between the polymorphism distribution index and a first predetermined threshold, determining whether the given gene belongs to a population CNV.

[0008] According to the embodiment of the application, the step of determining group CNV is significantly reduced using the above method, automatic analysis is realized, and the user's threshold of use is reduced. This method does not need to read all sequencing reads in the bam file, automatically detects whether there is an index bam.bai file, locates the bam file position according to the gene coordinates, and only needs to read the sequencing reads in the target gene region. Since the target gene region is only 1% of the genome, only reading the bam in these regions can greatly speed up (increase 100 times). In addition, the inventor selects multi-threaded concurrent reading bam files, and since bam files are generally very large, the waiting time is also the longest, and the present invention directly adopts multi-threaded (default 3 threads) concurrent speed-up processing, so that the multi-sample of the group can also complete rapid detection and analysis when the amount of data is very large.

[0009] According to an embodiment of the present application, the above method for determining a population CNV may also have at least one of the following additional technical features:

[0010] It should be noted that the sequencing data is provided in at least one of the bam, sam and cram formats of high-throughput sequencing results; the bam format is preferred. Since the bam file is already in binary format, the reading speed on the computer is usually faster than that of the sam file.

[0011] According to an embodiment of the present application, the sequencing data of at least 100 samples are obtained in step (1).

[0012] According to an embodiment of the present application, the sequencing data of at least 300 samples are obtained in step (1). According to an embodiment of the present application, the larger the sample size, the more representative the population CNV obtained.

[0013] According to an embodiment of the present application, in step (3), the sequencing depth is determined by the following steps: A. recording the position coordinates of the given gene based on a gene annotation file; B. reading the number of sequencing reads at each base position within a given region of the given gene in an alignment file based on the position coordinates, the alignment file including a bam file; C. calculating the average number of sequencing reads at all base positions within a given region of the given gene to obtain the sequencing depth.

[0014] Exemplarily, read the gene annotation file (such as GFF file), record the position coordinates of each gene in the genome, and merge and sort the overlapping gene regions, sort and store; further classify according to the location of the chr (chromosome) region based on the aforementioned coordinate position. Since each chr region is independent, the inventor uses the thread library in the c++ standard multithreading library to process each chr region concurrently and simultaneously to shorten the waiting time. It should be noted that all regions on a chr correspond to a handle of this sample comparison file (such as bam file). When processing a specific gene of the sample to be tested, according to the gene coordinate information, combined with the index file information of the comparison file (bam), the open comparison file handle is positioned at the starting position of the coordinate interval. Then, by applying the function in the htslib library, all the read information on the genome compared in this region is sequentially read (because read has been sorted, the read position is exited when it exceeds this region), and the data structure of hash_map is used to store the Count number covered by read at each site on a given gene, i.e., the depth of the site. Finally, the depth of all sites of a given gene is averaged, which is the average depth of this gene in this sample.

[0015] According to an embodiment of the present application, in step (2), it further includes determining the coverage of the multiple samples in the corresponding region of each of the given genes.

[0016] It should be noted that the coverage is very important for the quality assessment of genome sequencing data. High coverage means that the genomic region has been covered by multiple sequencing reads, which can improve the accuracy and reliability of the genome. Low coverage may lead to erroneous or missing genetic information, affecting subsequent biological analysis.

[0017] According to an embodiment of the present application, the method further includes filtering the sample data whose coverage is lower than a second predetermined threshold.

[0018] According to an embodiment of the present application, the second threshold is 0.5.

[0019] It should be noted that the above steps (determining sequencing depth and coverage) utilize data structures such as unordered_map (Nealis T. Ordered versus Unordered Map for Primitive Data Types [R]. ARMY ARMAMENT RESEARCH DEVELOPMENT AND ENGINEERING CENTER PICATINNY ARSENAL NJ FIRE CONTROL SYSTEMS AND TECHNOLOGY DIRECTORATE, 2015.), combined with multi-threaded thread and bam operations htslib (Bonfield JK, Marshall J, Danecek P, et al. HTSlib: C library for reading / writing high-throughput sequencing data [J]. Gigascience, 2021, 10 (2): giab007.) library related functions, the inventors have developed a multi-threaded concurrent algorithm for locating sequencing data in bam files in the target region, and the above steps are used to quickly count the sequencing depth and coverage of the corresponding region of the given gene. Through the above algorithm, fast data mapping can be achieved (unordered_map corresponds to a hash table, which can be quickly searched), while reducing the access to invalid sites and only accessing information about sites related to the gene region, thereby achieving a fast and extremely low memory usage effect.

[0020] According to an embodiment of the present application, in step (3), the polymorphism distribution index is determined based on the following formula:

[0021]

[0022] Among them, i, j represent the sample numbers; n represents the number of samples.

[0023] According to the embodiments of the present application, in view of the current lack of indicators for defining CNV polymorphisms of population genes, the inventors defined a population polymorphism distribution index LogPi for filtering CNVs of potential candidate genes.

[0024] Specifically, the CNV of sample i is recorded as CNV i If there is no CNV in this sample, then LogPi = 1, that is, near 1, log i=log10(CNV i *100+1). Then, the population CNV polymorphism LogPi is

[0025] That is, the CNV difference between two samples i and j is (Log i -Log j ) 2 The LogPi of a single sample is the average of the CNV differences of all samples.

[0026] According to an embodiment of the present application, the polymorphism index being greater than a first predetermined threshold is an indication that the gene region is a population CNV.

[0027] According to an embodiment of the present application, the first predetermined threshold is 0.1.

[0028] According to an embodiment of the present application, in step (3), it further includes determining the variance of the sequencing depths of multiple samples of the given gene.

[0029] It should be noted that the variance of the sequencing depths of multiple samples of the given gene is used to measure the quality and reliability of gene sequencing data. Specifically, if the variance of the sequencing depths of multiple samples of a given gene is small, it means that the sequencing depths of these samples are relatively uniform, and the stability and accuracy of the data are high; on the contrary, a large variance indicates that there are large differences in the sequencing depths of these samples, and the stability and accuracy of the data are low.

[0030] According to an embodiment of the present application, the variance being greater than a first predetermined threshold is an indication that the gene region is a population CNV.

[0031] According to an embodiment of the present application, the first predetermined threshold is 0.1.

[0032] It should be noted that before comparing the difference between the polymorphism distribution index and the first predetermined threshold, the sequencing depth and the coverage in the corresponding region of the given gene need to be standardized. The standardization process includes: based on the average depth of the given gene in the above-mentioned sample to be tested, when calculating the standardization of a specific sample (i), the depth of all genes (j) of this sample (i) is averaged and recorded as the sequencing depth of the sample (the calculation formula is as follows);

[0033]

[0034] Then the average depth of a given gene (j) is compared to the sequencing depth of the sample (the formula is as follows), and the normalization is completed;

[0035]

[0036] In a second aspect of the present application, the present application proposes a device for determining a population CNV. According to an embodiment of the present application, the device comprises:

[0037] A data acquisition unit, the data acquisition unit is used to acquire sequencing data of multiple samples and a corresponding region of at least one gene in a reference genome, the sequencing data including sequences of sequencing reads and location information of the sequencing reads on the reference genome;

[0038] A sequencing depth determination unit, the sequencing depth determination unit comprising a sequencing depth determination module, the sequencing depth determination unit being connected to the data acquisition unit and configured to determine, for each given gene of the at least one gene, the sequencing depth of the multiple samples in a corresponding region of each given gene;

[0039] a polymorphism distribution index determination unit, the polymorphism distribution index determination unit comprising a polymorphism distribution index determination module, the polymorphism distribution index determination unit being connected to the sequencing depth determination unit, and being used to determine the polymorphism distribution index of the given gene based on the sequencing depth of each of the multiple samples determined in the sequencing depth determination unit in the given gene region;

[0040] A population CNV detection unit, the population CNV detection unit is connected to the polymorphism distribution index determination unit, and is used to determine whether the given gene belongs to a population CNV based on a difference between the polymorphism distribution index and a first predetermined threshold.

[0041] It should be noted that, from a structural point of view ( Figure 1 ), the data acquisition unit S100 is connected to the sequencing depth determination unit S200, the sequencing depth determination unit S200 is connected to the polymorphism distribution index determination unit S300, and the polymorphism distribution index determination unit S300 is connected to the population CNV detection unit S400.

[0042] It should be noted that the sequencing depth determination unit in the above-mentioned device utilizes data structures such as unordered_map (Nealis T. Ordered versus Unordered Map for Primitive Data Types [R]. ARMY ARMAMENT RESEARCH DEVELOPMENT AND ENGINEERING CENTER PICATINNY ARSENAL NJ FIRE CONTROL SYSTEMS AND TECHNOLOGY DIRECTORATE, 2015.), combined with multi-threaded thread and bam operation htslib (Bonfield JK, Marshall J, Danecek P, et al. HTSlib: C library for reading / writing high-throughput sequencing data [J]. Gigascience, 2021, 10 (2): giab007.) library related functions, and the inventors have developed a multi-threaded concurrent algorithm for locating sequencing data in bam files in the target area, and the sequencing depth (or coverage) of the corresponding area of ​​the given gene is quickly counted through the above steps. Through the above algorithm, fast data mapping can be achieved (unordered_map corresponds to a hash table, which can be quickly searched), while reducing the access to invalid sites and only accessing information about sites related to the gene region, thereby achieving a fast and extremely low memory usage effect.

[0043] According to an embodiment of the present application, the above-mentioned apparatus for determining population CNV may also have at least one of the following additional technical features:

[0044] According to an embodiment of the present application, the sequencing depth is determined by the following steps: A. recording the position coordinates of the given gene based on a gene annotation file; B. reading the number of sequencing reads for each base position within a given region of the given gene in an alignment file based on the position coordinates, the alignment file including a bam file; C. calculating the average number of sequencing reads for all base positions within a given region of the given gene to obtain the sequencing depth.

[0045] According to an embodiment of the present application, the sequencing depth determination unit further includes a coverage confirmation module, which is used to determine the coverage of the multiple samples in the corresponding region of each of the given genes.

[0046] It should be noted that, from a structural point of view ( Figure 2), the data acquisition unit S100 is connected to the sequencing depth determination unit S200, the sequencing depth determination unit S200 is connected to the coverage confirmation module S201, the coverage confirmation module S201 is connected to the polymorphism distribution index determination unit S300, and the polymorphism distribution index determination unit S300 is connected to the population CNV detection unit S400.

[0047] According to an embodiment of the present application, the sequencing depth determination unit further includes a filtering processing module, which is connected to the coverage confirmation module and is used to filter the sample data whose coverage is lower than a second predetermined threshold.

[0048] It should be noted that, from a structural point of view ( Figure 3 ), the data acquisition unit S100 is connected to the sequencing depth determination unit S200, the sequencing depth determination unit S200 is connected to the coverage confirmation module S201, the coverage confirmation module S201 is connected to the filtering processing module S202, the filtering processing module S202 is connected to the polymorphism distribution index determination unit S300, and the polymorphism distribution index determination unit S300 is connected to the population CNV detection unit S400.

[0049] According to an embodiment of the present application, the second predetermined threshold is 0.5.

[0050] According to an embodiment of the present application, the polymorphism distribution index is determined based on the following formula:

[0051]

[0052] Among them, i, j represent samples.

[0053] It should be noted that the population polymorphism distribution index LogPi calculation formula defined by the inventors is used to filter CNVs of potential candidate genes.

[0054] According to an embodiment of the present application, the polymorphism index being greater than a first predetermined threshold is an indication that the gene region is a population CNV.

[0055] According to an embodiment of the present application, the first predetermined threshold is 0.1.

[0056] According to an embodiment of the present application, the polymorphism distribution index determination unit further includes a sequencing depth variance confirmation module, which is used to determine the variance of the sequencing depths of multiple samples of the given gene.

[0057] It should be noted that, from a structural point of view ( Figure 4), the data acquisition unit S100 is connected to the sequencing depth determination unit S200, the sequencing depth determination unit S200 is connected to the polymorphism distribution index determination unit S300, the polymorphism distribution index determination unit S300 is connected to the sequencing depth variance confirmation module S301, and the sequencing depth variance confirmation module S301 is connected to the population CNV detection unit S400.

[0058] According to an embodiment of the present application, the variance being greater than a first predetermined threshold is an indication that the gene region is a population CNV. According to an embodiment of the present application, the first predetermined threshold is 0.1.

[0059] It should be noted that, from a structural point of view ( Figure 5 ), the data acquisition unit S100 is connected to the sequencing depth determination unit S200, the sequencing depth determination unit S200 is connected to the coverage confirmation module S201, the coverage confirmation module S201 is connected to the filtering module S202, the filtering module S202 is connected to the polymorphism distribution index determination unit S300, the polymorphism distribution index determination unit S300 is connected to the sequencing depth variance confirmation module S301, and the sequencing depth variance confirmation module S301 is connected to the population CNV detection unit S400.

[0060] In a third aspect of the present application, the present application proposes a computer program product. According to an embodiment of the present application, the computer program product includes computer instructions, and when part or all of the computer instructions are run on a computer, the method described in the first aspect is executed.

[0061] In a fourth aspect of the present application, the present application proposes a server. According to an embodiment of the present application, the server comprises: a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the method described in the first aspect of the present application.

[0062] According to an embodiment of the present application, the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement a method for determining CNV of a population.

[0063] In a fifth aspect of the present application, the present application proposes a computer-readable storage medium. According to an embodiment of the present application, the storage medium includes computer instructions, and when the instructions are executed by a computer, the computer implements the method described in the first aspect.

[0064] It should be noted that in the present application, the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or equipment (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or equipment and execute instructions), or in combination with these instruction execution systems, devices or equipment. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or equipment or in combination with these instruction execution systems, devices or equipment. More specific examples of computer-readable media (non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program may be printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or processing in other suitable ways as necessary, and then stored in a computer memory. The various computer-readable storage media described herein may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0065] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0067] Figure 1 is a schematic diagram of a device for determining a population CNV according to an embodiment of the present application;

[0068] Figure 2 Schematic diagram of a device for determining population CNV according to an embodiment of the present application (including a coverage confirmation module);

[0069] Figure 3 Schematic diagram of a device for determining population CNV according to an embodiment of the present application (including a coverage confirmation module and a filtering processing module);

[0070] Figure 4 Schematic diagram of a device for determining population CNV according to an embodiment of the present application (including a sequencing depth variance confirmation module);

[0071] Figure 5 It is a schematic diagram of a device for determining a population CNV according to an embodiment of the present application (including a coverage confirmation module, a filtering processing module and a sequencing depth variance confirmation module);

[0072] Figure 6 It is the out.gene.stat.gz file format diagram described in Example 1 of the present application;

[0073] Figure 7 It is a diagram of the out.GeneDepth.mat.gz file format described in Example 1 of the present application;

[0074] Figure 8 It is a diagram of the Out.GeneCNV.Raw.mat.gz file format described in Example 1 of the present application;

[0075] Fig. 9 This is a diagram of the Out.GeneCNV.filter.mat.gz file format described in Example 1 of the present application. DETAILED DESCRIPTION

[0076] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0077] Definition and Description

[0078] Unless otherwise specified herein, the singular forms "a", "an", etc. include plural referents (more than one); "a set" or "a plurality" refers to two or more.

[0079] Unless otherwise specified herein, the terms “comprise” or “include” are open-ended expressions, that is, including the contents specified in the present invention but not excluding other contents.

[0080] Unless otherwise specified in this document, the terms "first", "second", "third", "fourth", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated; features specified as "first", "second", etc. may explicitly or implicitly include one or more of the said features.

[0081] Unless otherwise specified in this article, the term "population CNV" refers to the phenomenon that the copy number of a certain genomic region in a population varies. This variation may include an increase or decrease in the copy number of a DNA fragment, which is different from the copy number of a normal person. Population CNV is one of the important manifestations of genomic variation. Unlike the analysis of CNV of a single individual, it focuses on the variation of a certain genomic region in a population, and can provide important information for research in the fields of genomic genetics, evolution, and genetic epidemiology of diseases. Population CNV analysis needs to be based on a large amount of sample data. By comparing and analyzing a large amount of data, it can be found that certain structural variations are clustered in a specific population, and functional analysis related to genetics, cross-species evolution, and disease can be performed.

[0082] Unless otherwise specified herein, the term "sequencing" means sequence determination, which, together with "nucleic acid sequencing" or "gene sequencing", refers to the determination of the base order in a nucleic acid sequence.

[0083] Unless otherwise specified in this article, the term "sequencing depth" refers to the number of times the bases in a specific genomic region are sequenced during the genome sequencing process. The higher the sequencing depth, the higher the reliability and accuracy, and this value is usually expressed in the form of X-fold coverage. For example, if a genomic region is covered by 20 reads and the length of the region is 100bp, the sequencing depth of the region is 20X.

[0084] Unless otherwise specified in this article, the term "bam" file refers to a binary file generated after sequencing data is aligned. The bam file is the binary form of the sam (Sequence Alignment / Map) format file, that is, the bam file contains all the data of the alignment results. The bam file contains information such as the sequence and sequencing quality of each sequencing read, as well as information such as the alignment position and alignment method with the reference genome. The aligned bam file can be further used for various bioinformatics analyses, such as genotype analysis, resequencing, variation detection, RNA sequencing, etc.

[0085] Unless otherwise specified in this article, the term "sequencing read" is used interchangeably with the term "read" or "read segment", which refers to a nucleic acid sequence obtained during sequencing. This sequence is referred to as a "sequencing read segment" or "read" in this article. Usually, the read length of the sequencing reads of first-generation sequencing and third-generation sequencing is between several thousand and tens of thousands of bp, and the sequencing reads of second-generation sequencing are relatively shorter, averaging tens to hundreds of bp.

[0086] Unless otherwise specified in this article, the terms "reference genome" and "genome reference sequence" have the same meaning and refer to the genome sequence of the species corresponding to the known sample. It can be a genome sequence obtained through public channels or obtained through sequencing assembly. It can be the entire sequence of the genome or a partial sequence of the genome of interest. For example, when analyzing human samples, multiple versions of human genome sequences provided by public databases can be used, such as hg19.

[0087] Unless otherwise specified herein, the method described in this application is not limited to being applicable to population resequencing data, but can also be applied to the calculation of spatiotemporal single-cell gene expression levels, etc.

[0088] Methods for determining population CNVs

[0089] In a first aspect of the present application, the present application proposes a method for determining a population CNV. According to an embodiment of the present application, the method comprises:

[0090] (1) obtaining sequencing data of multiple samples and a corresponding region of at least one gene in a reference genome, wherein the sequencing data includes a sequence of a sequencing read and location information of the sequencing read on the reference genome;

[0091] (2) for each given gene of the at least one gene, determining the sequencing depth of the multiple samples in a corresponding region of each given gene;

[0092] (3) determining a polymorphism distribution index of the given gene based on the sequencing depth of each of the multiple samples determined in step (2) in the given gene region; and

[0093] (4) Determining whether the given gene belongs to a population CNV based on the difference between the polymorphism distribution index and a first predetermined threshold.

[0094] It should be noted that, in the present application, the source of the sequencing data is not particularly limited, and can be genome data that has been sequenced (such as bitter melon genome data, etc.), or can be sequencing data obtained by independently performing genome sequencing. In addition, there is no particular limitation on the sequencing platform, and it can be any common first-generation, second-generation or third-generation sequencing platform.

[0095] According to the embodiments of the present application, the method only needs to compare the data of files (such as bam, sam or cram, etc.) and gene information files (such as GFF or GTF, etc.) to obtain the population gene CNV file in one step. There is no need for too many parameter adjustment processes, and the method of use is simple; the method detects population gene CNV, rather than the CNV reintegration of the whole genome of an individual. Through automatic recognition of the bamheader sample name, the population format CNV is directly transferred out, and the downstream analysis does not require the user to reintegrate; based on the problem that the bam file is very large and there are many samples for population analysis, if the bam of all samples is read, it takes too long, this method combines data structure algorithms such as unordered_map and only needs to access the site-related information of the target (or given) gene region, thereby achieving fast and extremely low-memory population CNV detection.

[0096] According to the embodiments of the present application, when searching for a group to detect group CNV, from the current integration of individual CNV to the group, due to the uneven sequencing depth of some samples, many low-frequency CNVs are easily missed during individual detection. If there are important varieties in the sample, such as wild-type samples, missing these low-frequency CNVs may ignore the differences of these special samples. The inventors designed the polymorphism distribution index LogPi to filter out CNVs of potential candidate genes and greatly reduce the missed reading rate of low-frequency CNVs.

[0097] It should be noted that in order to avoid the influence of intron length on the sensitivity of CNV detection, the method described in the present application strips introns and uses sequencing data with only CDS sequences for analysis.

[0098] Device for determining CNV in a population

[0099] In a second aspect of the present application, the present application proposes a device for determining a population CNV. According to an embodiment of the present application, the device includes: a data acquisition unit, the data acquisition unit is used to acquire sequencing data of multiple samples and a corresponding region of at least one gene in a reference genome, the sequencing data including a sequence of a sequencing read and location information of the sequencing read on the reference genome;

[0100] a sequencing depth determination unit, the sequencing depth determination unit being connected to the data acquisition unit and configured to determine, for each given gene of the at least one gene, the sequencing depth of the plurality of samples in a corresponding region of each given gene;

[0101] a polymorphism distribution index determination unit, the polymorphism distribution index determination unit being connected to the sequencing depth determination unit, and configured to determine the polymorphism distribution index of the given gene based on the sequencing depth of each of the plurality of samples determined in the sequencing depth determination unit in the given gene region;

[0102] A population CNV detection unit, the population CNV detection unit is connected to the polymorphism distribution index determination unit, and is used to determine whether the given gene belongs to a population CNV based on a difference between the polymorphism distribution index and a first predetermined threshold.

[0103] According to the embodiments of the present application, the above-mentioned device is used to realize the automated detection of group CNV, which saves time and labor costs, and greatly improves the detection efficiency and accuracy. This device realizes multi-sample detection, which has obvious advantages for large-scale sample detection, can improve efficiency and reduce costs. And the device based on sequencing depth and polymorphism distribution index detection can better adapt to samples with large variation and quality differences, and has a wider applicability.

[0104] Computer program product

[0105] In a third aspect of the present application, the present application proposes a computer program product. According to an embodiment of the present application, the computer program product includes computer instructions, and when part or all of the computer instructions are run on a computer, a method for determining a population CNV is executed.

[0106] server

[0107] In a fourth aspect of the present application, the present application proposes a server. According to an embodiment of the present application, the server comprises: a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement a method for determining a population CNV.

[0108] According to an embodiment of the present application, the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement a method for determining CNV of a population.

[0109] Computer readable storage medium

[0110] In a fifth aspect of the present application, the present application proposes a computer-readable storage medium. According to an embodiment of the present application, the storage medium includes computer instructions, and when the instructions are executed by a computer, the computer implements a method for determining a population CNV.

[0111] It should be noted that the features and technical effects described in this article for different aspects can be used as reference for each other and will not be repeated here.

[0112] It should be noted that, for the convenience of description in the examples, PopGeneCNV is used in the examples of the present application to represent the above method for determining population CNV.

[0113] The present application is illustrated below by way of examples, but this should not be construed as limiting the scope of the subject matter of the present application to the following examples. All technologies implemented based on the above content of the present application belong to the scope of the present application. The compounds or reagents used in the following examples can be purchased from commercial sources or prepared by conventional methods known to those skilled in the art; the experimental instruments used can be purchased from commercial sources.

[0114] Embodiment 1:

[0115] The data in the examples of the present application come from the sequenced bitter melon genome data, from which the inventors selected the original data in bam format of 4 samples for verification.

[0116] 1. Parameter adjustment

[0117] Among them, the optional parameters of PopGeneCNV are shown in Table 1.

[0118] Table 1:

[0119]

[0120] 2. Data input

[0121] As shown below, -i: input bam file list (it is recommended to build an index bai, which can speed up the process); -g: input gene coordinate file, regular GFF and GTF files.

[0122]

[0123] 3. Output data

[0124] Based on the above input data, a total of 4 output files were obtained (Table 2).

[0125] Table 2:

[0126]

[0127] The output file format is as follows: Figures 6 to 9 shown.

[0128] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0129] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0130] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0131] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0132] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A method for determining CNV of a population, characterized in that include: (1) obtaining sequencing data of multiple samples and a corresponding region of at least one gene in a reference genome, wherein the sequencing data includes a sequence of a sequencing read and location information of the sequencing read on the reference genome; (2) for each given gene of the at least one gene, determining the sequencing depth of the multiple samples in a corresponding region of each given gene; (3) determining a polymorphism distribution index of the given gene based on the sequencing depth of each of the multiple samples in the given gene region; and (4) Determining whether the given gene belongs to a population CNV based on the difference between the polymorphism distribution index and a first predetermined threshold.

2. The method according to claim 1, characterized in that In step (3), the sequencing depth is determined by the following steps: A. recording the position coordinates of the given gene based on the gene annotation file; B. reading the number of sequencing reads at each base position within a given region of the given gene in an alignment file based on the position coordinates, wherein the alignment file includes a bam file; C. Calculate the average number of sequencing reads at all base positions in a given region of the given gene to obtain the sequencing depth.

3. The method according to claim 1, characterized in that In step (2), it further includes determining the coverage of the multiple samples in the corresponding region of each of the given genes.

4. The method according to claim 3, characterized in that Further comprising filtering the sample data whose coverage is lower than a second predetermined threshold; Optionally, the second predetermined threshold is 0.

5.

5. The method according to claim 1, characterized in that In step (3), the polymorphism distribution index is determined based on the following formula: Among them, i, j represent the sample numbers; n represents the number of samples; Optionally, the polymorphism index being greater than a first predetermined threshold is an indication that the gene region is a population CNV; Optionally, the first predetermined threshold is 0.

1.

6. The method according to claim 1, characterized in that In step (3), further comprising determining the variance of the sequencing depths of multiple samples of the given gene; Optionally, the variance being greater than a first predetermined threshold is an indication that the gene region is a population CNV; Optionally, the first predetermined threshold is 0.

1.

7. A device for determining CNV of a population, characterized in that The device comprises: A data acquisition unit, the data acquisition unit is used to acquire sequencing data of multiple samples and a corresponding region of at least one gene in a reference genome, the sequencing data including sequences of sequencing reads and location information of the sequencing reads on the reference genome; A sequencing depth determination unit, the sequencing depth determination unit comprising a sequencing depth determination module, the sequencing depth determination unit being connected to the data acquisition unit and configured to determine, for each given gene of the at least one gene, the sequencing depth of the multiple samples in a corresponding region of each given gene; a polymorphism distribution index determination unit, the polymorphism distribution index determination unit comprising a polymorphism distribution index determination module, the polymorphism distribution index determination unit being connected to the sequencing depth determination unit, and being used to determine the polymorphism distribution index of the given gene based on the sequencing depth of each of the multiple samples determined in the sequencing depth determination unit in the given gene region; A population CNV detection unit, the population CNV detection unit is connected to the polymorphism distribution index determination unit, and is used to determine whether the given gene belongs to a population CNV based on a difference between the polymorphism distribution index and a first predetermined threshold.

8. A computer program product, characterized in that The computer program product includes computer instructions. When part or all of the computer instructions are run on a computer, the method according to any one of claims 1 to 6 is executed.

9. A server, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is configured to execute the computer program to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The storage medium includes computer instructions. When the instructions are executed by a computer, the computer implements the method according to any one of claims 1 to 6.

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