CN113284552B - Screening method and device for micro haplotypes - Google Patents

Abstract

The invention discloses a screening method and a screening device of micro haplotypes, wherein the method comprises the following steps: acquiring data to be screened, and reading mark coordinates of a plurality of lines of single nucleotide polymorphism marks in the data to be screened; determining N primary micro-haplotypes according to the mark coordinates of the multi-row single nucleotide polymorphism marks; searching a reference sequence corresponding to each primary micro-haplotype, and calculating sequence characteristic parameters corresponding to each primary micro-haplotype by using each reference sequence; and screening the N primary micro-haplotypes according to the sequence characteristic parameters to obtain M target micro-haplotypes. The invention can accurately and rapidly screen micro haplotypes from genome or transcriptome data, shortens screening time and greatly improves screening efficiency.

Description

A micro-haplotype screening method and device

Technical field

The present invention relates to the technical field of forensic genetics, and in particular to a microhaplotype screening method and device.

Background technique

In forensic genetics, commonly used genetic markers include short tandem repeats (STR) and single nucleotide polymorphism (SNP) markers. However, in addition to the problems of high allelic mutation rate and unbalanced amplification, STR genetic markers also have an insurmountable bottleneck in expanding their application. That is, their sequence structure characteristics lead to easy replication slippage during the PCR process, resulting in stutter peak pairs. Data analysis causes interference, which is especially difficult when splitting mixed spots in forensic medicine. However, a single SNP genetic marker contains a small amount of genetic information, and it is often necessary to detect a large number of genetic markers to achieve a level comparable to STR identification capabilities.

Microhaplotype (MH) has the advantages of both STR and SNP without their drawbacks, and is an ideal forensic genetic marker. It is defined as a multi-allelic molecular marker composed of 2-5 SNPs within 200 bp in the genome. It was first proposed by the laboratory of Professor Kidd of Yale University in the United States. Since MH is in its early stages of development, there is currently no specific technical solution in the industry to search for it. Relevant researchers often manually search a set of data one by one, or hire bioinformatics professionals to assist in writing non-professional simple scripts. Find it.

Manual search is time-consuming, has low accuracy, and is easy to miss. Searching by writing non-professional scripts not only requires a lot of work, but also costs a lot, making it difficult to popularize and apply this technology.

Contents of the invention

The present invention proposes a microhaplotype screening method and device, which can quickly, efficiently and accurately screen MH loci from genetic data.

A first aspect of the embodiments of the present invention provides a microhaplotype screening method, which method includes:

Obtain the data to be screened and read the marker coordinates of multiple rows of single nucleotide polymorphism markers in the data to be screened;

Determine N preliminary microhaplotypes based on the marker coordinates of the multiple rows of single nucleotide polymorphism markers, where N is a positive integer greater than or equal to 1;

Search the reference sequence corresponding to each of the preliminary micro-haplotypes respectively, and use each of the reference sequences to calculate the sequence characteristic parameters corresponding to each of the preliminary micro-haplotypes;

According to the sequence characteristic parameters, M target micro-haplotypes are obtained from the N preliminary micro-haplotypes, where M is a positive integer greater than or equal to 1, and N is greater than or equal to M.

In a possible implementation of the first aspect, the determination of N primary microhaplotypes based on the marker coordinates of the multiple rows of single nucleotide polymorphism markers includes:

Divide the marker coordinates of the multiple rows of single nucleotide polymorphism markers into N sets of marker coordinate sets according to preset reference coordinate differences;

Store the single nucleotide polymorphism markers included in each set of marker coordinate sets into a preset python dictionary respectively;

Extract the single nucleotide polymorphism markers included in each set of marker coordinate sets from the preset python dictionary according to the preset storage quantity, and store the single-core polymorphism markers included in each set of marker coordinate sets. The nucleotide polymorphism marker was determined as a primary micro-haplotype, and N primary micro-haplotypes were obtained.

In a possible implementation of the first aspect, separately searching for reference sequences corresponding to each of the preliminary micro-haplotypes includes:

Create sequence files based on the first single nucleotide polymorphism marker coordinates and the terminal single nucleotide polymorphism marker coordinates of each of the primary microhaplotypes;

Input the sequence file into a preset sequence search tool to search for the reference sequence corresponding to each of the preliminary micro-haplotypes.

In a possible implementation of the first aspect, the sequence characteristic parameters include GC content values, repeated sequence characteristics and whole-genome multiple matching indicators;

The step of using each of the reference sequences to calculate the sequence characteristic value corresponding to each of the preliminary micro-haplotypes includes:

Using each of the reference sequences as a template, search for multiple similar sequences from the preset whole-genome data through BLAST analysis, and calculate the evaluation parameters of each similar sequence, where the evaluation parameters include expected values and score values;

The similarity number of the similar sequences obtained by statistical search based on the expected value and the score value, and the similarity number is the whole-genome multi-matching index;

Calculate the GC content value of each reference sequence separately;

Short tandem repeat sequence features are extracted from each reference sequence according to preset repeat sequence feature values.

In a possible implementation of the first aspect, the data to be screened includes genomic data and transcriptome data;

The step of reading the marker coordinates of multiple rows of single nucleotide polymorphism markers in the data to be screened includes:

When the data to be filtered is genomic data, read the marker coordinates of multiple rows of single nucleotide polymorphism markers in the genomic data;

When the data to be filtered is transcriptome data, the start coordinates and the end coordinates of the chromosomes contained in the transcriptome data are obtained, and the distance from the start coordinate to the end coordinate is used as the coordinate interval. In the coordinate interval, the marker coordinates of multiple target single nucleotide polymorphism markers whose coordinate values are within the coordinate interval are screened.

In a possible implementation of the first aspect, screening the N preliminary micro-haplotypes to obtain M target micro-haplotypes based on the sequence characteristic parameters includes:

Determine whether the GC content value corresponding to each of the preliminary micro-haplotypes meets the preset content value conditions, and determine whether the repeated sequence characteristics corresponding to each of the preliminary micro-haplotypes meet the preset target sequence characteristics. conditions, and determining whether the whole-genome multi-matching index meets the preset index conditions;

From the N primary micro-haplotypes, the GC content value corresponding to the primary micro-haplotype meets the preset content value conditions, and the repetitive sequence characteristics corresponding to the primary micro-haplotype meet the preset conditions. If the target sequence characteristic conditions and the whole-genome multi-matching index satisfy the preset index conditions, M target micro-haplotypes will be obtained.

In a possible implementation of the first aspect, the method further includes:

Obtain typing data, which is single nucleotide polymorphism marker typing data including a certain number of people;

Split the typing data into multiple population typing data according to the preset Thousand Genomes population source and sample name, wherein each population typing data includes the single nucleotide polymorphism corresponding to each sample Sex marker typing data.

In a possible implementation of the first aspect, the method further includes:

The target microhaplotype is used to calculate the corresponding forensic parameters, where the forensic parameters include allele typing and frequency, observed heterozygosity, expected heterozygosity, matching probability, polymorphic information content, and individual identification. Probability, triplet non-parent exclusion probability, doublet non-parent exclusion probability value and number of valid alleles.

In a possible implementation of the first aspect, after the step of determining the primary microhaplotype based on the multiple single nucleotide polymorphism marker coordinates, the method further includes:

Name each of the primary microhaplotypes.

A second aspect of the embodiment of the present invention also provides a microhaplotype screening device, which device includes:

A reading module, used to obtain the data to be screened and read the marker coordinates of multiple rows of single nucleotide polymorphism markers in the data to be screened;

A determination module, configured to determine N primary microhaplotypes based on the marker coordinates of the multiple rows of single nucleotide polymorphism markers, where N is a positive integer greater than or equal to 1;

A calculation module, configured to respectively search for the reference sequence corresponding to each of the preliminary micro-haplotypes, and to use each of the reference sequences to calculate the sequence characteristic parameters corresponding to each of the preliminary micro-haplotypes;

A screening module for screening the N primary micro-haplotypes to obtain M target micro-haplotypes based on the sequence characteristic parameters, where M is a positive integer greater than or equal to 1, and N is greater than or equal to M

Compared with the existing technology, the beneficial effect of the microhaplotype screening method and device provided by the embodiments of the present invention is that: the present invention can read the marker coordinates of single nucleotide polymorphism markers based on single nucleotide polymorphisms. The marker coordinates of the acid polymorphism markers are roughly screened to obtain the primary micro-haplotypes, and then the reference sequence of the primary micro-haplotypes is searched, the sequence feature values are calculated based on the reference sequences, and finally the target micro-haplotypes are screened based on the sequence feature values. type, achieving the effect of rapid screening of micro-haplotypes. The whole process is simple and fast, which can not only shorten the screening time and improve the screening efficiency, but also improve the accuracy of screening; and this application can realize the whole process of screening and evaluating MH from the original data of genome and transcriptome, forming a complete set of technical solutions, so that The existing technical solutions have been integrated and improved, greatly improving the practicality and flexibility of screening; at the same time, this application also provides a unified MH locus derived from the genome and transcriptome and the corresponding allele naming scheme to facilitate different Information exchange between laboratories and rapid computer data processing.

Description of the drawings

Figure 1 is a schematic flow chart of a microhaplotype screening method provided by an embodiment of the present invention;

Figure 2 is an operation flow chart of a microhaplotype screening method provided by an embodiment of the present invention;

Figure 3 is a schematic structural diagram of a microhaplotype screening device provided by an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

At present, there is no specific technical solution in the industry to search. Relevant researchers often manually search a set of data one by one, or hire bioinformatics professionals to assist in writing non-professional simple scripts to search. The manual search method is time-consuming, has low accuracy, and is easy to miss. Non-professionals are limited by their professional knowledge of forensic genetics. The scripts written to search are not only inaccurate but also costly, making it difficult to promote this technology. application.

In order to solve the above problems, a micro-haplotype screening method provided by the embodiments of the present application will be introduced and explained in detail through the following specific examples.

Referring to FIG. 1 , a schematic flow chart of a microhaplotype screening method provided by an embodiment of the present invention is shown.

As an example, the micro-haplotype screening method may include:

S11. Obtain the data to be filtered, and read the marker coordinates of multiple rows of single nucleotide polymorphism markers in the data to be filtered.

The data to be filtered is a human genome annotation file containing SNPs prepared in advance by the user. Specifically, it may be a file in a similar VCF format. The file can include (1) CHROM: chromosome; (2) POS: genome position; (3) ID: rsID number of the variant site, if not represented by "."; (4) REF: reference typing; ( 5) ALT: Variant typing; (6) QUAL: Call (name) the quality of this site; (7) FILTER: Filter the variant sites, if passed, it will be PASS, if not filtered It is "."; (8) INFO: detailed information of the variant. Specifically, users can download SNP-VCF files of thousands of genome data in advance.

In order to improve the screening efficiency, in actual operation, you can prepare an empty worksheet file in python software to store the SNP-VCF file; then import pandas, os, xlwt, xlsxwriter, openpyxl, openpyxl, csv and other python in python module; then use a for loop to read SNP-VCF files with different chromosome numbers one by one and use a for loop to read the SNP-VCF file of each chromosome line by line, and read the coordinates of multiple lines of single nucleotide polymorphism markers.

In this embodiment, the data to be filtered may include genomic data and transcriptome data, where, as an example, step S11 may include the following sub-steps:

Sub-step S111: When the data to be filtered is genome data, read the marker coordinates of multiple rows of single nucleotide polymorphism markers in the genome data.

In this embodiment, the marker coordinates are the coordinate difference between the coordinates of the single nucleotide polymorphism marker and the starting coordinates.

In the specific implementation, in order to facilitate screening, users can use python to set each chromosome to correspond to a SNP-VCF file, then use a for loop to read the SNP-VCF file of each chromosome line by line, and then use the split function to split the SNP-VCF file. The content is split to obtain the chromosome number of each chromosome, the name and location of the single nucleotide polymorphism marker (SNP), and other information, thereby obtaining multiple single nucleotide polymorphism markers (SNP).

In this embodiment, the specific search and reading can be: setting the initial SNP position coordinate to "loc=0", subtracting "loc" from the single nucleotide polymorphism marker (SNP) position coordinate obtained in the first row , get the marker coordinates of the first row of single nucleotide polymorphism markers, and then subtract "loc" from the position coordinates of the second row of single nucleotide polymorphism markers (SNP) to get the second row of single nucleotides The marker coordinates of the acid polymorphism markers, and so on, get the marker coordinates of the single nucleotide polymorphism markers in each row.

Sub-step S112: When the data to be filtered is transcriptome data, obtain the start coordinates and end coordinates of the chromosomes contained in the transcriptome data, and use the distance from the start coordinate to the end coordinate as the coordinates interval.

Sub-step S113: Screen the marker coordinates of multiple target single nucleotide polymorphism markers whose coordinate values are within the coordinate interval from the coordinate interval.

In this embodiment, the transcriptome data is data pre-organized by the user. The transcriptome data can be a file similar to the BED format, where the BED format file contains a total of six columns of data content, which are: (1) Chrom: chromosome number; (2) ChromStart: chromosome start coordinate; (3) ChromEnd : Chromosome end coordinate; (4) Name: row name: (5) Score: 0-1000, grayscale value displayed in the genome browser; (6) Strand: positive and negative strand markers.

In the specific implementation, its "ChromStart" and "ChromEnd" coordinates can be obtained, and the start mark coordinates and end mark coordinates can be obtained.

In actual operation, transcriptome data and genome data can be filtered and read at the same time. You can use a for loop to traverse the SNP-VCF file of "Substep S111" respectively, and combine it with if conditions to filter out the cSNPs from the transcriptome data source. Genetic markers.

Among them, the specific search process is: after using a for loop to read the BED file line by line, obtain the genome start and end coordinates of a certain transcriptome genetic marker (i.e. "ChromStart" and "ChromEnd"), and use "ChromStart" and "ChromEnd" ChromEnd" coordinates determine a coordinate interval, that is, the "ChromStart-ChromEnd" interval.

After the interval is determined, the coordinate calculation method of sub-step S111 can be used to calculate the marker coordinates of each row of single nucleotide polymorphism markers. The specific calculation method can refer to the above content. To avoid repetition, it will not be described again here.

In this embodiment, it can be determined whether the genetic marker coordinates corresponding to the chromosome are in the "ChromStart-ChromEnd" interval. If the genetic marker coordinates corresponding to the chromosome are in this interval, the single nucleotide polymorphism marker can be determined. (SNP) is cSNP.

In order to facilitate subsequent operations, based on the found cSNPs, the preset SNP-VCF format file can be spliced with the BED file of transcriptome data, and the output can be sorted and output to obtain a result file in VCF format. The result file can contain transcriptome cSNPs. VCF sample file.

S12. Determine N preliminary micro-haplotypes based on the multiple rows of single nucleotide polymorphism marker coordinates, where N is a positive integer greater than or equal to 1.

Since the single nucleotide polymorphism marker coordinates obtained by reading have multiple rows, a rough screening can be carried out based on the multiple rows of single nucleotide polymorphism marker coordinates to determine a certain number of primary microhaplotypes, and then from The target micro-haplotype is obtained by screening a certain number of primary micro-haplotypes, thereby improving the accuracy of screening.

In order to improve the efficiency of screening, as an example, step S12 may include the following sub-steps:

Sub-step S121: Divide the marker coordinates of the multiple rows of single nucleotide polymorphism markers into N sets of marker coordinates based on preset reference coordinate differences.

In this embodiment, a set of mark coordinates can be divided into a set of mark coordinates every 100 rows, every 50 rows, 200 rows or 500 rows, and the details can be adjusted according to actual needs.

Sub-step S122: Store the single nucleotide polymorphism markers included in each set of marker coordinates into a preset Python dictionary.

In order to facilitate computer calculation, in this embodiment, a traversal calculation can be performed to determine whether the SNP marker coordinates of each row belong to a set of SNP marker coordinates. When inputting the same set of single-core The coordinate set of nucleotide polymorphism markers can be stored in the preset python dictionary first.

For example, you can use the preset reference coordinate difference value of 200 to differ the coordinates of the single nucleotide polymorphism marker in the second row and the coordinates of the single nucleotide polymorphism marker in the first row to obtain the single nucleotide polymorphism marker in the second row. The coordinates of the nucleotide polymorphism marker and the coordinates of the single nucleotide polymorphism marker in the first row are 50, and 50 is less than 200. The coordinates of the single nucleotide polymorphism marker in the second row are the same as the coordinates of the single nucleotide polymorphism marker in the first row. The coordinates of the single nucleotide polymorphism markers in the first row can be divided into one group. The coordinates of the single nucleotide polymorphism markers in the first row and the coordinates of the single nucleotide polymorphism markers in the second row can be divided into The nucleotide polymorphism markers are stored in a group of the preset python dictionary, and then the coordinates of the single nucleotide polymorphism markers in the 3rd row are compared with the coordinates of the single nucleotide polymorphism markers in the 1st row. The obtained marker coordinates are 120, and 120 is less than 200. Then the single nucleotide polymorphism markers contained in the coordinates of the single nucleotide polymorphism marker in the third row are also stored in the previous group of the preset python dictionary. The difference between the single nucleotide polymorphism marker coordinates in the 4th row and the single nucleotide polymorphism marker coordinates in the 1st row results in a marker coordinate of 375, which is greater than the preset reference coordinate difference of 200, then determine the 1st row to The third line of single nucleotide polymorphism marker coordinates is a set of marker coordinates.

Then, recalculate the coordinates of the single nucleotide polymorphism marker in the fourth row as the coordinates of the initial single nucleotide polymorphism marker in the second set of marker coordinates (microhaplotype), and calculate the single nucleotide polymorphism marker in the fourth row. The coordinate difference between the coordinates of the nucleotide polymorphism marker and the coordinates of the single nucleotide polymorphism marker in row 5 is compared with the preset reference coordinate difference, and so on.

It should be noted that in actual operation, the preset reference coordinate difference can be 50, 80, 300, 550 or n, etc., which can be adjusted according to actual needs.

In a specific implementation, the preset python dictionary can be composed of "key" and "value", where the "key" can store the micro-haplotype loci contained in the single nucleotide polymorphism marker coordinates of each row. Name, "value" can store the attributes of each microhaplotype locus, such as the combination of position coordinates of each single nucleotide polymorphism marker, etc. "Key" and "value" have a one-to-one correspondence.

Sub-step S123: Extract the number of single nucleotide polymorphism markers included in each set of marker coordinates from the preset python dictionary according to the preset storage number, and store each set of marker coordinates The single nucleotide polymorphism markers included in the set are determined as a primary micro-haplotype, and N primary micro-haplotypes are obtained.

In actual operation, the storage quantity can be repeatedly judged for each set of marker coordinate sets. If the number of single nucleotide polymorphism markers stored in the marker coordinate set meets the minimum requirements set by the user, then the number of stored single nucleotide polymorphism markers stored in the marker coordinate set will be extracted and stored from the marker coordinate set. The corresponding number of single nucleotide polymorphism markers is used to obtain a preliminary micro-haplotype until the judgment of all marker coordinate sets is completed.

For example, you can use the if statement to determine "the number of single nucleotide polymorphism markers (SNPs) contained in the preset storage dictionary. If the number is greater than the minimum requirement of the preset storage quantity (for example, 3) , that is, a micro-haplotype (MH) is determined to be found, and the set of single nucleotide polymorphism marker coordinates is used as the primary micro-haplotype.

In this embodiment, by using a for loop combined with if conditions, and according to the standard of greater than or equal to 2 SNPs within a 200 bp range, the effect of efficient screening of microhaplotypes (MH) can be achieved.

In actual operation, in order to subsequently distinguish and organize each microhaplotype (MH), as an example, after step S12, the method may also include:

Step S21: Name each of the preliminary micro-haplotypes.

Specifically, the primary micro-haplotypes obtained through the above screening can be sorted, and at the same time, the chromosome number, user-defined micro-haplotype (MH) name, and the SNPs included in the micro-haplotype (MH) and The position coordinates and other information are written into the preset worksheet file to achieve the effect of adding a mark to each of the primary micro-haplotypes.

During subsequent management, the corresponding required microhaplotype (MH) can be found based on the name or number.

When naming, it can be based on forensic scientific research and practical application habits, and also taking into account the convenience of computers in processing large-scale information. In this embodiment, the present invention proposes a new naming method for MH loci.

For example, if it is a microhaplotype (MH) from genome sources, it can be named "mh21SHY5/15765902AC/15765915GAT/15766020AG/15766086CA", where "mh" is the abbreviation of microhaplotype (microhaplotype); mh The following number "21" represents the chromosome where the gene locus is located (the value of this part is a positive integer from 01 to 22 and X, Y, MT); the capital letter "SHY" represents the abbreviation of the name of the laboratory that discovered the marker; the following The number "5" is the numerical sequence number of the microhaplotype locus found, indicating that the marker is the fifth microhaplotype discovered by our laboratory on chromosome 21.

The content after "/" is the ascending position coordinate of the SNP contained in the MH, and after the corresponding coordinates is the reference type (first letter) and variant type (not the first letter) of the SNP. In this naming method, it can be seen as consisting of two parts, with the first "/" as the dividing line. The part before the first "/" is the brief name of the tag. For example, the mark can be simply called "mh21SHY5", which is conducive to oral or written communication among relevant personnel in forensic medicine practice; the content after the "/" is the complete name of the mark, which is conducive to different laboratories on the one hand. Comparing this type of genetic markers is also conducive to large-scale information display and processing by computers.

The above naming of MH is a more pertinent way, which can facilitate professionals' basic research on this type of markers, and is also conducive to large-scale forensic applications (including the establishment of a large database of MH markers) after the marker research matures.

In addition, similar to the naming of MH loci derived from genomes, MHs derived from transcriptomes also have the problem of standardized naming. Currently, there is no publicly recommended naming method at home and abroad. Here, this application also proposes a naming method that is consistent with the MH from which the genome is derived, for example, "mh21SHY1/H1_circ_007207/15024188CT/15024224GAC/15024284GC". Compared with the above-mentioned MH of genome origin, this naming method adds "H1_circ_007207" between the natural number number of the mark and the first SNP position coordinate, which represents the transcriptome genetic mark source of MH (in the example here: A circRNA molecule), and is also distinguished from the MH derived from the genome. The other meanings are the same as the nomenclature of the MH derived from the genome.

Likewise, alleles equivalent to MH loci of genomic and transcriptomic origin also need to be named. Optionally, the SNP typing can be used for naming. For example, the allele classification of the MH:mh21SHY5/15765902AC/15765915GAT/15766020AG/15766086CA sourced from the above genome can be directly written in ascending order of the SNP coordinates shown in the MH locus name, that is, "ATGC", which respectively represents 15765902 The type of 15765915 is "A", and the type of 15765915 is "T". The same applies to others.

The MH locus naming and corresponding allele naming method based on genome and transcriptome sources proposed by the present invention have simple rules and are easy to master. It is not only conducive to communication and promotion between different laboratories, but also conducive to computer batch information deal with.

S13. Search for the reference sequence corresponding to each of the preliminary micro-haplotypes, and use each of the reference sequences to calculate the sequence characteristic parameters corresponding to each of the preliminary micro-haplotypes.

The reference sequence may be a microhaplotype (MH) fasta format sequence. The sequence characteristic parameter may be a score value for subsequent forensic reference value calculation of the microhaplotype (MH).

In this example, bedtools software is used to search for the reference sequence corresponding to each of the preliminary micro-haplotypes, and then GRCh38_human_ref is used as the reference template sequence. The GRCh38_human_ref file is a standard reference file used for human gene alignment.

Specifically, in order to improve the search accuracy, step S13 may include the following sub-steps as an example:

Sub-step S131: Create a sequence file based on the first single nucleotide polymorphism marker coordinates and the terminal single nucleotide polymorphism marker coordinates of each of the preliminary micro-haplotypes.

Specifically, the position coordinates of the first row of SNPs and the position coordinates of the last row of SNPs of the primary micro-haplotype can be extracted.

In this embodiment, two lines of coordinates can be spliced, and the splicing result is a sequence file in BED format containing: "CHROM, start position of cMH, end position of cMH, cMH_name".

Sub-step S132: Input the sequence file into a preset sequence search tool, and search for the reference sequence corresponding to each of the preliminary micro-haplotypes.

Input the sequence file into the bedtools software, and the bedtools software will search for the fasta format sequence corresponding to the microhaplotype (MH) based on the entire human genome (GRCh38_human_ref).

The sequence of the microhaplotype (MH) (taking the fasta format as an example) itself will affect subsequent specific primer design and amplification, thereby affecting subsequent forensic applications. In order to determine whether the found MH sequence is unique in the genome, and to evaluate the accuracy of similar sequences calculated by the search, in this embodiment, the sequence characteristic parameters include GC content values, repeated sequence characteristics and whole-genome multiple matches. Indicators, where, as an example, step S13 may also include the following sub-steps:

Sub-step S134: Using each reference sequence as a template, search for multiple similar sequences from the preset whole-genome data through BLAST analysis, and calculate the evaluation parameters of each similar sequence. The evaluation parameters include expected values and obtained values. Score.

Specifically, the blastn algorithm in blastn software can be used to find similar sequences in the genome.

The blastn algorithm can be used to search for multiple sequences similar to the fasta format sequence of the microhaplotype (MH) within the entire human genome (GRCh38_human_ref).

In actual operation, you can set the E value threshold of the fasta format sequence of the primary microhaplotype (MH) in the blastn algorithm to 0.00001, then look for similar sequences, and select several from the results that are similar to the reference sequence. Similar sequences.

For example, some reference sequences can find 10-20 similar sequences or more similar sequences from the entire human genome.

While searching, the evaluation parameters of each found similar sequence can be calculated, and the evaluation parameters can specifically include the expected value (E) and the score value (S).

The score value is the similarity evaluation between the similar sequence and the reference sequence. The higher the score, the greater the degree of similarity between the reference sequence and the similar sequence. Specifically, the blastn algorithm can be used to find the score information of each similar sequence and obtain the score value of each similar sequence.

The expected value is the reliability evaluation of the score value. It is the possibility that, under random circumstances, the similarity between other sequences in the database and the reference sequence is greater than the similarity between the similar sequence and the reference sequence. The lower the value, the better. Specifically, the blastn algorithm can be used to find the expected evaluation information of each similar sequence and obtain the expected value corresponding to the similar sequence.

Sub-step S135: Statistically search the similarity number of the similar sequences based on the expected value and the score value, and use the similarity number as the whole-genome multi-matching index.

The whole-genome multiple matching index is the number of similar sequences whose expected value meets the preset expected value requirements and whose score meets the preset score value requirements. Since there are many similar sequences, and the evaluation parameters of each similar sequence have different levels, if Each similar sequence is used, which not only requires a large workload, but also reduces the accuracy and efficiency of screening.

In order to reduce the workload and improve the screening accuracy, the number of similar sequences required can be determined based on the expected value and score value.

Specifically, multiple similar sequences can be sorted according to the expected value and score value, and then a certain number of similar sequences can be screened. For example, 10 similar sequences, sort the 10 similar sequences from high to low according to the expected value and score value, then extract the top 5 similar sequences, use the top 5 similar sequences as the target similar sequences, and format them in XML format Output and save the first 5 similar sequences.

The higher the value of multiple matching sequences that meet specific score values and expected values across the genome, the higher the possibility of non-specific amplification during subsequent detection, and the more likely it is to interfere with the analysis of the results. ,

Sub-step S136: Calculate the GC content value of each reference sequence respectively.

The GC content value refers to the ratio of guanine and cytosine among the four bases of DNA. Specifically, the GC content value of the reference sequence of each microhaplotype (MH) can be calculated, because the GC content value has a greater impact on the PCR process.

Sub-step S137: Extract short tandem repeat sequence features from each reference sequence according to preset repeat sequence feature values.

Specifically, you can find whether the reference sequence contains sequences similar to short tandem repeats (STR).

For example, you can set the number of bases contained in the motif to 1-6 and the number of repetitions to more than 4 times, and then extract the short tandem repeat sequences (STR) in the reference sequence to obtain the short tandem repeat sequence.

Finally, the short tandem repeat sequence and GC content values can be adjusted and output into a new file: the file includes: mh_name, GC%, nuit_number, repeat-number.

S14. Screen the N preliminary micro-haplotypes to obtain M target micro-haplotypes according to the sequence characteristic parameters, where M is a positive integer greater than or equal to 1, and N is greater than or equal to M.

After obtaining the sequence characteristic value and GC content value of each primary micro haplotype (MH), it can be judged based on the sequence characteristic value and GC content value of each micro haplotype (MH). If each micro haplotype (MH) If the sequence feature value of the plotype (MH) meets the preset feature threshold, or the GC content value meets the preset content threshold, then the primary micro-haplotype is determined to be the target micro-haplotype.

Since the sequence characteristic parameters include GC content values, repeated sequence characteristics and genome-wide multiple matching indicators, step S14 may include the following sub-steps:

Sub-step S141: Determine whether the GC content value corresponding to each of the preliminary micro-haplotypes meets the preset content value conditions, and determine whether the repeated sequence characteristics corresponding to each of the preliminary micro-haplotypes meet the preset conditions. target sequence characteristic conditions, and determine whether the whole-genome multi-matching index meets the preset index conditions.

Sub-step S142: Screen M GC content values corresponding to the primary micro-haplotypes from the N primary micro-haplotypes that meet the preset content value conditions, and the GC content values corresponding to the primary micro-haplotypes meet the preset content value conditions. M target micro-haplotypes are obtained from the primary micro-haplotypes whose repetitive sequence characteristics satisfy the preset target sequence characteristic conditions and the whole-genome multi-matching index satisfies the preset index conditions.

Specifically, the GC content value, repetitive sequence characteristics and whole-genome multi-matching index of each primary microhaplotype can be compared with the corresponding preset content value, preset comparison sequence characteristics and preset quantitative value respectively. , when the GC content value meets the preset content value, the repetitive sequence characteristics meet the preset comparison sequence characteristics, and the whole-genome multi-matching index meets the preset quantitative value, the primary micro-haplotype is determined to be the target sequence characteristic parameter.

In order to facilitate the subsequent calculation of forensic parameters, as an example, the method may also include:

S15. Obtain typing data, which is single nucleotide polymorphism marker typing data including a certain number of people.

S16. Split the typing data into multiple population typing data according to the preset Thousand Genomes population source and sample name, where each population typing data includes the single nucleotide corresponding to each sample. Polymorphic marker typing data.

Specifically, the typing data is data collected or downloaded by the user in advance and contains a certain number of people and human genome data.

Split the typing data into several population typing data according to the preset 1000 Genomes population source and sample name.

For example, this classification data is composed of a total of 2,504 people from 26 groups. The classification data can be split into 26 group classification data according to the different sources of the groups.

In the specific implementation, the user can pre-set a TXT file containing the sample name and the corresponding population source information, and then open the individual original typing data of the Thousand Genomes one by one according to each chromosome, and then use a for loop to match and search one by one, according to The sample name recorded in TXT corresponds to the corresponding sample and population source, and finally the typing data is split into 26 population typing data.

In addition, since each of the population typing data includes the single nucleotide polymorphism marker typing data corresponding to each sample, in order to facilitate the combination of the above population typing data with the target microhaplotype obtained from the genome or transcriptome For subsequent calculations, the population typing data can be split into files in CSV format.

In order to calculate common forensic parameters for each population of each genome, as an example, the method may also include:

S17. Use the target microhaplotype to calculate the corresponding forensic parameters, where the forensic parameters include allele classification and frequency, observed heterozygosity value, expected heterozygosity value, matching probability, polymorphic information content, Individual identification probability, triplet non-paternal exclusion probability, doublet non-paternal exclusion probability value and number of effective alleles.

Specifically, the single nucleotide polymorphism markers (SNPs) of each population can be divided into Combination of typing data. If a CSV file is obtained by splitting the genome of a thousand people, all the SNP genotype information of the individual contained in the CSV file can be combined into the typing of the target MH (because each MH is composed of several adjacent SNPs), and combined into The corresponding MH genotype of each individual can facilitate subsequent calculations by converting the SNPs contained in MH into the standard "ATCG" base format (the original typing of SNP is named numerically. For the convenience of viewing, we convert it into ATCG base format).

Specifically, the forensic parameters may include allele typing and frequency, observed heterozygosity value, expected heterozygosity value, matching probability, polymorphic information content, individual identification probability, triplet non-parent exclusion probability, doublet non-parental probability. Parent exclusion probability value and number of valid alleles.

Specifically, the heterozygosity observation value h = the number of heterozygotes in the sample/the total number of individuals in the sample;

expected value of heterozygosity Among them, n is the total number of all alleles in the sample, k is the number of alleles or haplotypes, and p _i is the frequency of the i-th allele or haplotype in the sample;

Match probability Among them, n is the number of genotypes of a certain genetic marker, and p _i is the frequency of the i-th genotype in the population;

polymorphic information content Among them, n is the total number of all alleles in the sample, and p _i is the frequency of the i-th allele;

individual identification probability Among them, n is the number of genotypes of a certain genetic marker, and p _i is the frequency of the i-th genotype in the population;

Triplet non-parent exclusion probability Among them, n is the total number of all alleles in the sample, p _i and p _j are the frequencies of the i and j alleles respectively;

Doublet non-parent exclusion probability Among them, n is the total number of all alleles in the sample, p _i and p _j are the frequencies of the i and j alleles respectively;

effective number of alleles Among them, n is the total number of all alleles in the sample, and p _i is the frequency of the i-th allele.

Finally, the calculation results can be organized, saved and output. Specifically, the calculation results can be saved in CSV format to facilitate subsequent calls by users.

Referring to FIG. 2 , an operation flow chart of a microhaplotype screening method provided by an embodiment of the present invention is shown.

In actual operation, the data to be filtered can be obtained separately, where the data to be filtered can include genomic data and transcriptome data. If it is genomic data, the position coordinates of the corresponding single nucleotide polymorphism markers are read from the genomic data and Generate a VCF file. If it is transcriptome data, you can convert the transcriptome data into the corresponding BED file, and then transcribe it into a VCF file from the corresponding file; then roughly screen to obtain the primary micro haplotype; then search for the primary micro haplotype The fasta reference sequence corresponding to the ploidytype, and generate the sequence file corresponding to the fasta reference sequence; then you can search for the corresponding similar sequence, calculate the GC content value and short tandem repeat sequence corresponding to the fasta reference sequence, and filter out the target according to the set threshold Microhaplotype. Then, obtain and split the typing data containing multiple groups; finally, use the split group typing data to calculate the forensic parameters corresponding to the target microhaplotype.

In this embodiment, the embodiment of the present invention provides a microhaplotype screening method, the beneficial effect of which is that: the present invention can read the position coordinates of single nucleotide polymorphism markers based on single nucleotides. The position coordinates of the polymorphic markers are roughly screened to obtain the primary micro-haplotype. Then the reference sequence of the primary micro-haplotype is found, the sequence feature value is calculated based on the reference sequence, and finally the target micro-haplotype is screened based on the sequence feature value. , to achieve the effect of rapid screening of micro-haplotypes. The whole process is simple and fast, which can not only shorten the screening time and improve the screening efficiency, but also improve the accuracy of screening. Moreover, this application can realize the whole process of screening and evaluating MH from the original data of genome and transcriptome, forming a complete set of technical solutions, so that The existing technical solutions have been integrated and improved, greatly improving the practicality and flexibility of screening. At the same time, this application also provides a unified MH locus derived from the genome and transcriptome and the corresponding allele naming scheme to facilitate different Information exchange between laboratories and rapid computer data processing.

An embodiment of the present invention also provides a micro-haplotype screening device. Refer to FIG. 3 , which shows a schematic structural diagram of a micro-haplotype screening device provided by an embodiment of the present invention.

Wherein, as an example, the micro-haplotype screening device may include:

The reading module 301 is used to obtain the data to be screened and read the marker coordinates of multiple rows of single nucleotide polymorphism markers in the data to be screened;

The determination module 302 is configured to determine N preliminary micro-haplotypes based on the marker coordinates of the multiple rows of single nucleotide polymorphism markers, where N is a positive integer greater than or equal to 1;

The calculation module 303 is configured to separately search for the reference sequence corresponding to each of the preliminary micro-haplotypes, and use each of the reference sequences to calculate the sequence characteristic parameters corresponding to each of the preliminary micro-haplotypes;

The screening module 304 is used to screen the N primary micro-haplotypes to obtain M target micro-haplotypes according to the sequence characteristic parameters, where M is a positive integer greater than or equal to 1, and N is greater than or equal to M. .

Optionally, the determination module is also used to:

Divide the marker coordinates of the multiple rows of single nucleotide polymorphism markers into N sets of marker coordinate sets according to preset reference coordinate differences;

Store the single nucleotide polymorphism markers included in each set of marker coordinate sets into a preset python dictionary respectively;

Extract the single nucleotide polymorphism markers included in each set of marker coordinate sets from the preset python dictionary according to the preset storage quantity, and store the single-core polymorphism markers included in each set of marker coordinate sets. The nucleotide polymorphism marker was determined as a primary micro-haplotype, and N primary micro-haplotypes were obtained.

Optionally, the computing module is also used to:

Create sequence files based on the first single nucleotide polymorphism marker coordinates and the terminal single nucleotide polymorphism marker coordinates of each of the primary microhaplotypes;

Input the sequence file into a preset sequence search tool to search for the reference sequence corresponding to each of the preliminary micro-haplotypes.

Optionally, the sequence characteristic parameters include GC content values, repeated sequence characteristics and genome-wide multiple matching indicators;

The computing module is also used to:

Using each of the reference sequences as a template, search for multiple similar sequences from the preset whole-genome data through BLAST analysis, and calculate the evaluation parameters of each similar sequence, where the evaluation parameters include expected values and score values;

The similarity number of the similar sequences obtained by statistical search based on the expected value and the score value, and the similarity number is the whole-genome multi-matching index;

Calculate the GC content value of each reference sequence separately;

Short tandem repeat sequence features are extracted from each reference sequence according to preset repeat sequence feature values.

Optionally, the data to be screened includes genomic data and transcriptome data;

The reading module is also used to:

When the data to be filtered is genomic data, read the marker coordinates of multiple rows of single nucleotide polymorphism markers in the genomic data;

When the data to be filtered is transcriptome data, the start coordinates and the end coordinates of the chromosomes contained in the transcriptome data are obtained, and the distance from the start coordinate to the end coordinate is used as the coordinate interval. In the coordinate interval, the marker coordinates of multiple target single nucleotide polymorphism markers whose coordinate values are within the coordinate interval are screened.

Optionally, the screening module is also used to:

Determine whether the GC content value corresponding to each of the preliminary micro-haplotypes meets the preset content value conditions, and determine whether the repeated sequence characteristics corresponding to each of the preliminary micro-haplotypes meet the preset target sequence characteristics. conditions, and determining whether the whole-genome multi-matching index meets the preset index conditions;

The GC content values corresponding to the M preliminary micro haplotypes are selected from the N preliminary micro haplotypes to meet the preset content value conditions, and the repeat sequence characteristics corresponding to the preliminary micro haplotypes satisfy The preset target sequence characteristic conditions and the whole-genome multi-matching index are the primary micro-haplotypes that meet the preset index conditions, and M target micro-haplotypes are obtained.

Optionally, the device also includes:

A typing module is used to obtain typing data, which is single nucleotide polymorphism marker typing data including a certain number of people;

A splitting module, configured to split the typing data into multiple population typing data according to the preset Thousand Genomes population source and sample name, wherein each of the population typing data includes each sample corresponding to Single nucleotide polymorphism marker typing data.

Optionally, the device also includes:

A forensic parameter module is used to calculate corresponding forensic parameters using the target microhaplotype, where the forensic parameters include allele classification and frequency, observed heterozygosity, expected heterozygosity, matching probability, multiple State information content, individual identification probability, triplet non-parent exclusion probability, doublet non-parent exclusion probability value and number of effective alleles.

Optionally, after the step of determining the primary microhaplotype based on the multiple single nucleotide polymorphism marker coordinates, the device further includes:

Naming module for naming each of the primary microhaplotypes

Furthermore, embodiments of the present application also provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above implementation is implemented. The micro-haplotype screening method described in the example.

Further, embodiments of the present application also provide a computer-readable storage medium that stores computer-executable instructions, and the computer-executable instructions are used to cause the computer to execute the steps described in the above embodiments. Screening methods for microhaplotypes.

The above is the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also regarded as It is the protection scope of the present invention.

Claims (7)

1. A method for screening a microsloid comprising:

Acquiring data to be screened, and reading mark coordinates of a plurality of lines of single nucleotide polymorphism marks in the data to be screened;

determining N primary micro-haplotypes according to the marking coordinates of the multi-line single nucleotide polymorphism marks, wherein N is a positive integer greater than or equal to 1;

searching a reference sequence corresponding to each primary micro-haplotype, and calculating sequence characteristic parameters corresponding to each primary micro-haplotype by using each reference sequence;

screening the N primary selected micro-haplotypes according to the sequence characteristic parameters to obtain M target micro-haplotypes, wherein M is a positive integer greater than or equal to 1, and N is greater than or equal to M;

the sequence characteristic parameters comprise GC content values, repeated sequence characteristics and genome-wide multiple matching indexes;

the calculating the sequence characteristic value corresponding to each primary micro-haplotype by using each reference sequence comprises the following steps:

searching a plurality of similar sequences from preset whole genome data by BLAST analysis by taking each reference sequence as a template, and calculating evaluation parameters of each similar sequence, wherein the evaluation parameters comprise expected values and scoring values;

counting the number of the similar sequences obtained by searching based on the expected value and the score value, and taking the number of the similar sequences as a whole genome multiple matching index;

Respectively calculating GC content values of each reference sequence;

extracting short tandem repeat sequence features from each reference sequence according to a preset repeat sequence feature value;

the data to be screened comprises genome data and transcriptome data;

the reading of the mark coordinates of the multi-row single nucleotide polymorphism marks in the data to be screened comprises the following steps:

when the data to be screened is genome data, reading mark coordinates of a plurality of lines of single nucleotide polymorphism marks in the genome data;

when the data to be screened is transcriptome data, acquiring a start coordinate and a stop coordinate of a chromosome contained in the transcriptome data, and taking a distance from the start coordinate to the stop coordinate as a coordinate interval, and screening mark coordinates of a plurality of target single nucleotide polymorphism marks with the coordinate values in the coordinate interval from the coordinate interval;

the screening of M target micro-haplotypes from the N primary micro-haplotypes according to the sequence characteristic parameters comprises the following steps:

judging whether the GC content value corresponding to each primary micro-haplotype meets the preset content value condition, judging whether the repeated sequence characteristic corresponding to each primary micro-haplotype meets the preset target sequence characteristic condition, and judging whether the genome-wide multi-matching index meets the preset index condition;

And screening M preliminary micro-haplotypes of which the GC content values corresponding to the preliminary micro-haplotypes meet preset content value conditions, the repeated sequence characteristics corresponding to the preliminary micro-haplotypes meet preset target sequence characteristic conditions and the genome-wide multi-match indexes meet preset index conditions from the N preliminary micro-haplotypes to obtain M target micro-haplotypes.

2. The method according to claim 1, wherein determining N prime microscales based on the marker coordinates of the plurality of rows of single nucleotide polymorphism markers comprises:

dividing the marking coordinates of the multi-row single nucleotide polymorphism marks into N groups of marking coordinate sets according to a preset reference coordinate difference value;

storing the single nucleotide polymorphism markers contained in each set of marker coordinate sets into a preset python dictionary respectively;

and respectively extracting the single nucleotide polymorphism markers contained in each group of mark coordinate sets from a preset python dictionary according to the preset storage quantity, and setting the single nucleotide polymorphism markers contained in each group of mark coordinate sets as a primary micro-haplotype to obtain N primary micro-haplotypes.

3. The method according to claim 1, wherein the searching for the reference sequence corresponding to each of the preliminary micro-haplotypes comprises:

respectively preparing a sequence file according to the first single nucleotide polymorphism mark coordinate and the tail end single nucleotide polymorphism mark coordinate of each primary selection microsloid;

and inputting the sequence file into a preset sequence searching tool, and searching to obtain a reference sequence corresponding to each primary micro-haplotype.

4. The method of screening for micro-haplotypes according to claim 1, further comprising:

the method comprises the steps of obtaining typing data, wherein the typing data comprise single nucleotide polymorphism marking typing data of a plurality of crowds;

splitting the typing data into a plurality of group typing data according to preset thousand-person genome group sources and sample names, wherein each group typing data comprises single nucleotide polymorphism marking typing data corresponding to each sample.

5. The method of screening for micro-haplotypes according to claim 1, further comprising:

and calculating corresponding forensic parameters by adopting the target micro-haplotype, wherein the forensic parameters comprise allele typing and frequency thereof, heterozygosity observation value, heterozygosity expected value, matching probability, polymorphism information content, individual identification probability, triplet non-father exclusion probability, duplex non-father exclusion probability value and effective allele factors.

6. The method of claim 1, wherein after the step of determining N prime microscales based on the marker coordinates of the plurality of rows of single nucleotide polymorphism markers, the method further comprises:

each of the primary microsloids was named.

7. A screening apparatus for microsloids, said apparatus comprising:

the reading module is used for acquiring data to be screened and reading mark coordinates of a plurality of lines of single nucleotide polymorphism marks in the data to be screened;

the determining module is used for determining N primary micro-haplotypes according to the marking coordinates of the multi-line single nucleotide polymorphism marks, wherein N is a positive integer greater than or equal to 1;

the calculation module is used for searching the reference sequence corresponding to each primary micro-haplotype and calculating the sequence characteristic parameter corresponding to each primary micro-haplotype by using each reference sequence;

the screening module is used for screening M target micro-haplotypes from the N primary micro-haplotypes according to the sequence characteristic parameters, wherein M is a positive integer greater than or equal to 1, and N is greater than or equal to M;

the sequence characteristic parameters comprise GC content values, repeated sequence characteristics and genome-wide multiple matching indexes;

The calculating the sequence characteristic value corresponding to each primary micro-haplotype by using each reference sequence comprises the following steps:

searching a plurality of similar sequences from preset whole genome data by BLAST analysis by taking each reference sequence as a template, and calculating evaluation parameters of each similar sequence, wherein the evaluation parameters comprise expected values and scoring values;

counting the number of the similar sequences obtained by searching based on the expected value and the score value, and taking the number of the similar sequences as a whole genome multiple matching index;

respectively calculating GC content values of each reference sequence;

extracting short tandem repeat sequence features from each reference sequence according to a preset repeat sequence feature value;

the data to be screened comprises genome data and transcriptome data;

the reading of the mark coordinates of the multi-row single nucleotide polymorphism marks in the data to be screened comprises the following steps:

when the data to be screened is genome data, reading mark coordinates of a plurality of lines of single nucleotide polymorphism marks in the genome data;

when the data to be screened is transcriptome data, acquiring a start coordinate and a stop coordinate of a chromosome contained in the transcriptome data, and taking a distance from the start coordinate to the stop coordinate as a coordinate interval, and screening mark coordinates of a plurality of target single nucleotide polymorphism marks with the coordinate values in the coordinate interval from the coordinate interval;

The screening of M target micro-haplotypes from the N primary micro-haplotypes according to the sequence characteristic parameters comprises the following steps:

judging whether the GC content value corresponding to each primary micro-haplotype meets the preset content value condition, judging whether the repeated sequence characteristic corresponding to each primary micro-haplotype meets the preset target sequence characteristic condition, and judging whether the genome-wide multi-matching index meets the preset index condition;

and screening M preliminary micro-haplotypes of which the GC content values corresponding to the preliminary micro-haplotypes meet preset content value conditions, the repeated sequence characteristics corresponding to the preliminary micro-haplotypes meet preset target sequence characteristic conditions and the genome-wide multi-match indexes meet preset index conditions from the N preliminary micro-haplotypes to obtain M target micro-haplotypes.