Patent Publication Number: US-2018039728-A1

Title: Operating method of apparatus for analyzing genome sequences using distributed processing

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority under 35 U.S.C. §119 to Korean Patent Application No. 10-2016-0100882, filed on Aug. 8, 2016, in the Korean Intellectual Property Office, the disclosure of which is incorporated by reference herein in its entirety. 
     TECHNICAL FIELD 
     Exemplary embodiments of the present inventive concept relate to an operating method of an apparatus for analyzing a genome sequence, and more particularly to analyzing genome sequences using distributed processing. 
     DISCUSSION OF RELATED ART 
     A genome includes all genetic information regarding a living thing. Genome sequencing technologies include a deoxyribonucleic acid (DNA) chip and Next Generation Sequencing (NGS) technology, Next Next Generation Sequencing (NNGS). Next generation sequencing may be interchangeably used with large-scale parallel sequencing or second-generation sequencing. 
     Genome data may include several tens to several hundreds of gigabytes of data. In analysis of genome data, a plurality of individual tools may be employed. Thus, a genome analysis pipeline may include software for integrating these individual tools, managing input/output and automatizing a step prior to genome data analysis. 
     SUMMARY 
     An exemplary embodiment of the present inventive concept provides a method of analyzing read sequences at a relatively high speed based on information regarding the mapped read sequences. 
     According to an exemplary embodiment of the present inventive concept, an operating method of an apparatus for analyzing a genome sequence includes mapping a plurality of sequenced read sequences to a reference genome. The method includes calculating a number of mapped read sequences. The reference genome is divided into a plurality of first regions based on the number of mapped read sequences. The mapped read sequences in each of the plurality of first regions are analyzed by performing distributed processing on the plurality of first regions. 
     According to an exemplary embodiment of the present inventive concept, an operating method of an apparatus for analyzing a genome sequence includes receiving a plurality of read sequences of a genome. The plurality of received read sequences is mapped to a reference genome. The reference genome is divided into a plurality of regions. Frequency information regarding a depth of the mapped read sequences is extracted. A maximum depth is set based on the frequency information. The read sequences mapped to each of the regions are analyzed by performing distributed processing on the read sequences below the maximum depth. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other features of the inventive concept will become more apparent by describing in detail exemplary embodiments thereof, with reference to the accompanying drawing, in which: 
         FIG. 1  is a flowchart illustrating an operating method of an apparatus for analyzing a genome sequence according to an exemplary embodiment of the present inventive concept; 
         FIG. 2  is a flowchart illustrating genome sequence analysis an exemplary embodiment of the present inventive concept; and 
         FIG. 3  is a flowchart illustrating region generation of  FIG. 2  according to an exemplary embodiment of the present inventive concept; 
         FIG. 4  illustrates a reference genome divided into a plurality of first regions, an exemplary embodiment of the present inventive concept; 
         FIG. 5  is a flowchart illustrating a process of depth filtration an exemplary embodiment of the present inventive concept; 
         FIG. 6  illustrates a high depth interval (HD) divided from a reference genome according to an exemplary embodiment of the present inventive concept; 
         FIG. 7  is a flowchart illustrating genome sequence analysis according to an exemplary embodiment of the present inventive concept; 
         FIG. 8  is a distribution chart in a case where region generation and depth filtration according to an exemplary embodiment of the present inventive concept are not applied, and a distribution chart in a case where region generation and depth filtration according to an exemplary embodiment of the present inventive concept are applied; 
         FIG. 9  is a distribution chart illustrating a proceeding time according to regions of subsequent analysis operations in each of the cases of  FIG. 8  according to an exemplary embodiment of the present inventive concept; 
         FIG. 10  is a view of a distributed processing system for analyzing a genome sequence according to an exemplary embodiment of the present inventive concept; and 
         FIG. 11  illustrates a system for analyzing a genome sequence to which a method of analyzing a genome sequence is applied, according to an exemplary embodiment of the present inventive concept. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
       FIG. 1  is a flowchart illustrating an operating method of an apparatus for analyzing a genome sequence according to an exemplary embodiment of the present inventive concept. 
     Referring to  FIG. 1 , an operating method of an apparatus for analyzing a genome sequence may include genome sample sequencing S 10  and genome sequence analysis S 100 . 
     In sequencing S 10 , genome information may be acquired from a genome sample extracted from an individual, for example, blood, saliva, or other bodily tissues. A plurality of read sequences regarding the acquired genome information may be generated. The read sequence refers to a partial piece of a base sequence of the genome sample to be analysed using genome sequence analysis. The read sequences generated by sequencing S 10  may include a sequence of nucleotides or base pairs (bp) having an arbitrary length. AS an example, the read sequences may be from about 10 to about 2000 bp, about 15 to about 1500 bp, about 20 to about 1000 bp, about 20 to about 500 bp, or about 20 to about 200 bp. Several thousands or millions of read sequences may be generated by performing sequencing S 10 . 
     Sequencing S 10  is general gene sequencing known in the art and refers to an operation in which genome information such as a deoxyribonucleic acid (DNA) base sequence, may be acquired from the genome sample. 
     A genome is an aggregate of genetic materials from a particular organism. Each human cell includes 23 pairs of chromosomes (46 chromosomes, for a man, 22 pairs+XY, for a woman, 22 pairs+XX). A genome may refer to a set of genetic materials transmitted from a parent to a descendant. An example in which the genome includes DNA will be described in more detail below; however, exemplary embodiments of the present invention are not limited thereto. 
     In genome sequence analysis S 100 , the sequence of the genome is analyzed using the read sequences acquired by performing sequencing S 10 . In an exemplary embodiment of the present inventive concept, by performing genome sequence analysis S 100 , a variant of a wild type genetic sequence, such as a single nucleotide variant (SNV) or a copy number variant (CNV) may be analyzed. However, exemplary embodiments of the present invention are not limited thereto. 
     The term “SNV” refers to substitution of single nucleotide compared with a wild type genetic sequence. CNV refers to a gene that repetitively occurs due to loss or amplification of a larger region (e.g., a repeating gene sequence that appears as a different number of repeats than a number of repeats in a wild type genetic sequence). 
     In genome sequence analysis S 100  according to an exemplary embodiment of the present inventive concept, a reference genome may be divided into a plurality of regions based on information regarding read sequences mapped to a reference genome. An object of which a genome sequence is to be analyzed, may be set based on depth information regarding the mapped read sequences. Depth may refer to redundancy of coverage. For example, in next-generation sequencing, coverage may refer to average raw or aligned read depth, which may refer to the expected coverage on the basis of the number and the length of high-quality reads based on alignment with a reference sequence. 
     Genome sequence analysis S 100  will be described in more detail below with reference to  FIG. 2 . 
       FIG. 2  is a flowchart illustrating genome sequence analysis an exemplary embodiment of the present inventive concept 
       FIG. 2  is a flowchart of genome sequence analysis S 100  of  FIG. 1 . 
     Referring to  FIG. 2 , genome sequence analysis S 100  may include read alignment S 110 , region generation S 120 , deduplication S 130 , depth filtration S 140 , and variant calling S 150 . 
     In read alignment S 110 , the read sequences acquired by performing sequencing S 10  may be mapped to the reference genome. In region generation S 120 , the reference genome to which the read sequences are mapped, may be divided into a plurality of first regions. In deduplication S 130 , duplicated read sequences among the read sequences mapped to the reference genome may be removed. In depth filtration S 140 , an arbitrary interval may be removed from an object to be analyzed, using depth information regarding the read sequences mapped to the reference genome. In variant calling S 150 , a variant may be identified by comparison with the reference genome and analyzing the read sequences mapped to the reference genome. 
     In read alignment S 110 , a relatively large amount of read sequences acquired in sequencing (see S 10  of  FIG. 1 ) may be mapped to the reference genome. The reference genome may refer to a sequence in which nucleic acid information such as a base sequence are already known, such as through a genome project regarding the genome. Information regarding the reference genome may be acquired from a database (DB) already known in the art, such as National Center for Biotechnology Information (NCBI), Gene Expression Omnibus (GEO), Food and Drug Administration (FDA), My Cancer Genome, or Korea Food and Drug Administration (KFDA). As an example, the reference genome may be acquired from public genome data or public HAPMAP data. 
     Methods of read alignment S 110  may include a seed &amp; extend method, or a method using a partial combination sequence. 
     The seed &amp; extend method is a method in which read sequences generated from a genome sample are mapped to a certain position of a reference genome of which nucleic acid information regarding an individual is already known. As an example, in the seed &amp; extend method, the read sequences are compared with the reference genome and are mapped to the reference genome, and all the read sequences are not compared with the reference genome from the first time but a partial sequence or seed of the read sequences is extracted and the extracted partial sequence is compared with the reference genome. 
     The partial combination sequence in the method using the partial combination sequence refers to a sequence in which two or more adjacent partial seeds are combined with each other. The partial combination sequence may be a combination of at least two or more adjacent partial seeds among partial seeds. The adjacent partial seeds may include two or more partial seeds that overlap each other or are connected, two or more partial seeds of which a predetermined number overlap each other or are connected, or two or more partial seeds connected to each other while a predetermined number of partial seeds is lost. 
     In the case of the seed &amp; extend method and the method using the partial combination sequence, each of the read sequences may be independently mapped to each of the reference genomes of a particular chromosome. Thus, mapping operations between the read sequences may be independent operations without interaction. As an example, the mapping operation of each of the read sequences means that distributed processing may be performed using several nodes. 
     In region generation S 120 , the reference genome may be divided into a plurality of first regions for analysis through distributed processing. In an exemplary embodiment of the present inventive concept, the reference genome may be divided into a plurality of first regions based on information regarding the number of mapped read sequences. Region generation S 120  will be described in more detail below with reference to  FIGS. 3 and 4 . 
     After the reference genome is divided into the plurality of first regions, duplicated read sequences may be removed by performing deduplication S 130 . The duplicated read sequences may be generated during amplification caused by a polymerase chain reaction (PCR) in sequencing. The duplicated read sequences may be removed by performing distributed processing on each of the first regions. By reducing the number of extraneous read sequences in deduplication S 130 , a proceeding speed of subsequent operations may be increased. 
     In an exemplary embodiment of the present inventive concept, deduplication S 130  may be omitted. In this case, depth filtration S 140  may be performed after region generation S 120  is performed. 
     In depth filtration S 140 , depth information regarding the read sequences may be checked so that an arbitrary interval may be removed from the object of which a genome sequence is to be analyzed. In an exemplary embodiment of the present inventive concept, the depth information regarding the read sequences may be frequency information regarding depth. Depth filtration S 140  will be described in more detail below with reference to  FIGS. 5 and 6 . 
     A variant of the genome may be checked in variant calling S 150 . In an exemplary embodiment of the present inventive concept, variant calling S 150  may be performed by performing distributed processing on the first regions divided in region generation S 120 . After distributed processing is performed, information regarding a variant of the genome checked from each of the first regions may be integrated. In an exemplary embodiment of the present inventive concept, after depth filtration S 140  is performed, local realignment and/or base recalibration is further performed and then, variant calling S 150  may be performed. 
       FIG. 3  is a flowchart illustrating region generation of  FIG. 2  according to an exemplary embodiment of the present inventive concept.  FIG. 4  illustrates a reference genome divided into a plurality of first regions, an exemplary embodiment of the present inventive concept. 
       FIG. 3  illustrates a process of region generation S 120  of  FIG. 2 .  FIG. 4  is a view of a state in which a reference genome  30  is divided into a plurality of first regions RG by performing region generation S 120  according to an exemplary embodiment of the present inventive concept. 
     Referring to  FIG. 3 , region generation S 120  may include read counting S 122 , region calculation S 124 , and region extraction S 126 . 
     In read counting S 122 , the reference genome  30  may be divided into a plurality of second regions bin_RG, and information regarding the number of read sequences  20  may be included in each of the plurality of second regions bin_RG. In region calculation S 124 , the information regarding the number of read sequences  20  included in the second regions bin_RG may be integrated, and the average number of read sequences  20  may be calculated from the information regarding the number of read sequences  20  and information regarding the targeted number of first regions RG. In region extraction S 126 , the first regions RG may be extracted from the average number of read sequences  20 . 
     Referring to  FIGS. 3 and 4 , in read counting S 122 , the reference genome  30  may be divided into the plurality of second regions bin_RG. The plurality of second regions bin_RG may be arbitrary regions to be divided into a plurality of first regions RG. Each of the plurality of second regions bin_RG may have substantially a same length as each other; however, exemplary embodiments of the present invention are not limited thereto. Referring to  FIG. 4 , the first regions RG may include RG_ 1  to RG_N, and the second regions bin_RG may include bin_RG_ 1  to bin_RG_L. In an exemplary embodiment of the present inventive concept, N and L are natural numbers, in which L is an integer greater than or equal to N. 
     In read counting S 122 , the reference genome  30  may be divided into the plurality of second regions bin_RG, and the number of read sequences  20  included in each of the second regions bin_RG may be checked. The number of read sequences  20  may be checked using position information regarding the read sequences  20  mapped to the reference genome  30 . The position information may be position information in chromosomes, for example. In an exemplary embodiment of the present inventive concept, checking of the number of read sequences  20  included in each of the second regions bin_RG may be performed by performing distributed processing on each of the second regions bin_RG. 
     In read counting S 122 , the number of read sequences  20  included in each of the second regions bin_RG is checked, and the number of all the read sequences  20  mapped to the reference genome  30  may be calculated by integrating the information regarding the number of read sequences  20  included in each of the second regions bin_RG. After the number of all the read sequences  20  mapped to the reference genome  30  is calculated, the calculated number of read sequences  20  may be divided by the targeted number of first regions RG, thus calculating an average value. Referring to  FIG. 4 , the reference genome  30  may be divided into seven first regions RG. However, exemplary embodiments of the present invention are not limited thereto, and the number of first regions RG may be set, as desired. For example, the number of first regions RG may be set in consideration of a distributed processing capability. 
     If information regarding the average number of read sequences  20  suitable for the targeted number of first regions RG is checked in region calculation S 124 , in region extraction S 126 , the first regions RG may be extracted using the second regions bin_RG and the information regarding the average number of read sequences  20 . Each of the first regions RG may be extracted by sequentially merging the plurality of second regions bin_RG. In an exemplary embodiment of the present inventive concept, the plurality of second regions bin_RG may be integrated until the number of read sequences  20  mapped to each of the first regions RG reaches the average number based on information regarding the average number of read sequences  20  suitable for the targeted number of first regions RG. In an exemplary embodiment of the present inventive concept, the reference genome  30  may be divided into the plurality of first regions RG by integrating the second regions bin_RG, and information regarding the mapped read sequences  20  according to the first regions RG may be generated as a file. The file may be generated by performing distributed processing on the first regions RG. 
     According to an exemplary embodiment of the present inventive concept, the reference genome  30  may be divided into the plurality of first regions RG based on the number of mapped read sequences  20  so that the read sequences  20  having a substantially uniform number may be placed in each of the first regions RG. According to an exemplary embodiment of the present inventive concept, the read sequences  20  may be analyzed in a distributed processing environment based on the first regions RG to achieve increased consistency in a processing time for each of the first regions RG. Thus, the total genome data analysis time may be reduced. Also, a duty cycle of a resource of the distributed processing system may be reduced. 
       FIG. 5  is a flowchart illustrating a process of depth filtration an exemplary embodiment of the present inventive concept.  FIG. 6  illustrates a high depth interval (HD) divided from a reference genome according to an exemplary embodiment of the present inventive concept. 
       FIG. 5  is a detailed flowchart illustrating a process of filtration S 140 .  FIG. 6  is a view of a state in which a high depth interval (HD) is divided from the reference genome  30  by performing depth filtration  140  according to an exemplary embodiment of the present inventive concept. 
     Referring to  FIG. 5 , depth filtration S 140  may include depth collecting S 142 , depth calculation S 144 , and interval removal S 146 . 
     In depth collecting S 142 , depth information regarding the mapped read sequences  20  in each of the first regions RG of the reference genome  30  may be checked. In depth calculation S 144 , depth information regarding the mapped reference sequences  20  in each of the first regions RG of the reference genome  30  may be integrated, and a plurality of statistical values may be calculated based on the integrated depth information. In interval removal S 146 , an interval to be removed from an object to be analyzed may be set using the calculated statistical values. 
     Referring to  FIGS. 5 and 6 , in depth collecting S 142 , information regarding a depth of each of the read sequences  20  in each of the first regions RG of the reference genome  30  may be checked. The depth of the read sequences  20  change based on a position of the reference genome  30 . The term “position” may refer to distance from an arbitrary base to another arbitrary base and may include a plurality of bases. In an exemplary embodiment of the present inventive concept, the information regarding the depth may be frequency information. Depth collecting S 142  may be performed by performing distributed processing on each of the first regions RG. 
     In depth collecting S 142 , the depth information regarding the read sequences  20  in each of the first regions RG may be checked. In depth calculation S 144 , pieces of information (e.g., a depth or frequency of occurrence of each read sequence  20 ) may be integrated, and statistical values (e.g., average depth, maximum depth and/or minimum depth) may be calculated based on the pieces of information. In an exemplary embodiment of the present inventive concept, the depth information may be frequency information, and the statistical values calculated based on the frequency information may include modes, averages and/or standard deviations. The mode may be the greatest frequency or depth of a particular read sequence  20 . 
     In depth calculation  144 , statistical values may be calculated for the read sequences  20 . In interval removal S 146 , a reference value may be calculated based on the statistical values. In an exemplary embodiment of the present inventive concept, when the statistical values calculated in depth calculation S 144  include modes and/or standard deviations, the reference value may have a value of [2×mode+3×standard deviation]. However, the reference value is not limited thereto. 
     In the case of normal (e.g., wild type) DNA, after read alignment (e.g., S 110 ) is performed, the read sequences  20  may be mapped to the reference genome  30  to a relatively uniform depth. However, when a structure variant occurs in a DNA sequence, the DNA sequence may be moved to another position, and due to the moved DNA the depth of the mapped read sequences  20  may be twice or more the depth of the mapped read sequences  20  in a normal state position. In an exemplary embodiment of the present inventive concept, the formula [2×mode+3×standard deviation] may include ‘2×mode’ in consideration of the structure variant, and ‘3×standard deviation’ which represents 99.9% of a standard distribution. 
     Referring to  FIG. 6 , the read sequences  20  may be mapped to the reference genome  30 , and the reference genome  30  may be divided into a plurality of first regions RG. In an exemplary embodiment of the present inventive concept, the first regions RG may be divided based on the information regarding the number of mapped read sequences  20 . 
     The HD that exceeds the reference value calculated in depth calculation S 144  may be distinguished from other first regions RG. In an exemplary embodiment of the present inventive concept, the HD may have a depth that exceeds the reference value according to the formula [2×mode+3×standard deviation] and may be removed from the genome sequence analysis, by performing interval removal S 146 . 
     Referring to  FIG. 6 , one HD is distinguished from other first regions RG. However, exemplary embodiments of the present invention are not limited thereto, and a plurality of HDs may be distinguished by the reference value. 
       FIG. 7  is a flowchart illustrating genome sequence analysis according to an exemplary embodiment of the present inventive concept. 
       FIG. 7  is a flowchart illustrating a process of genome sequence analysis S 200  according to an exemplary embodiment of the present inventive concept. Referring to  FIG. 7 , descriptions of operations described above may be omitted, and thus duplicative descriptions may be omitted. 
     Referring to  FIG. 7 , genome sequence analysis S 200  may include read alignment S 210 , region generation S 220 , deduplication S 230 , depth filtration S 240 , local realignment S 250 , base recalibration S 260 , and variant calling S 270 . In local realignment S 250 , an unmapped portion in read alignment S 210  may be mapped again. In base recalibration S 260 , a base score may be recalibrated through an empirical model configuration. 
     Duplicated read sequences among the mapped read sequences may be removed in deduplication S 230 . In local realignment S 250 , an unmapped portion may be mapped again. In an exemplary embodiment of the present inventive concept, in read alignment S 210 , mapping need not be performed at a distal end of DNA, and in local realignment S 250 , the distal end of DNA, at which mapping is not performed, may be mapped again. However, exemplary embodiments of the present invention are not limited thereto. 
     After mapping of the read sequences in local realignment S 250 , base recalibration S 260  may be performed. Variant calling may depend on a base score of each of the read sequences. The base score may refer to an error score estimated in a sequencing machine per base. Since the error in the sequencing machine may occur due to technical factors, the estimation need not be exact. Thus, in base recalibration S 260 , an empirical model of error rates may be performed, and the base score may be applied to the empirical model and recalibrated. Generating the empirical model may include generating a table including error rates per base. 
     In an exemplary embodiment of the present inventive concept, local realignment S 250  and/or base recalibration S 260  may be performed through a genome analysis toolkit (GATK). However, exemplary embodiments of the present invention are not limited thereto. 
     The operating method of the apparatus for analyzing the genome sequence according to an exemplary embodiment of the present inventive concept described with reference to  FIGS. 1 through 7  may be implemented by program instructions that may be executed by various computer units and may be recorded on a computer-readable recording medium. The computer-readable recording medium may solely include program instructions, data files, and a data structure, or a combination thereof. The program instructions recorded on the computer-readable recording medium may be specifically designed and configured for this disclosure or publicly known to those skilled in the art of computer software and available. Examples of the computer-readable recording medium include magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as CD-ROMs or DVDs, magneto-optical media such as floptical disk, and a hardware device specially configured to store and execute program instructions, such as read-only memory (ROM), random-access memory (RAM), and flash memory. Examples of the program instructions include machine language codes made by a compiler and high-level language codes that may be executed by a computer using an interpreter, etc. 
       FIG. 8  is a distribution chart in a case where region generation and depth filtration according to an exemplary embodiment of the present inventive concept are not applied, and a distribution chart in a case where region generation and depth filtration according to an exemplary embodiment of the present inventive concept are applied. 
       FIG. 8  includes a distribution chart (a) and a distribution chart (b) illustrating a case where region generation (e.g. S 120 ) and depth filtration (e.g., S 140 ) according to an exemplary embodiment of the present inventive concept are not applied, and a case where region generation (e.g., S 120 ) and depth filtration (e.g., S 140 ) according to an exemplary embodiment of the present inventive concept are applied, respectively. Distribution chart (a) and distribution chart (b) represent the number of read sequences mapped to divided regions of a reference genome. The y-axis in each distribution chart represents the number of reads, and the x-axis represents divided regions of the reference genome. On the y-axis, the number of reads may be increased along an arrow direction. The divided regions of distribution chart (b) may be first regions (e.g., RG) according to an exemplary embodiment of the present inventive concept. In an exemplary embodiment of the present inventive concept, distribution chart (a) may be a case where the reference genome is divided into a plurality of regions having the same length. 
     Comparing (a) with (b), in the case of distribution chart (a), HDs of the read sequences compared to other regions exist. The distribution of read sequences mapped to each of the regions is not uniform in distribution chart (a). In distribution chart (b) according to an exemplary embodiment of the present inventive concept, the HDs of distribution chart (a) are eliminated, and the distribution of the read sequences mapped to each of the regions is more uniform compared to distribution chart (a). 
       FIG. 9  is a distribution chart illustrating a proceeding time according to regions of subsequent analysis operations in each of the cases of  FIG. 8  according to an exemplary embodiment of the present inventive concept. 
       FIG. 9  is a distribution chart illustrating a proceeding time of divided regions of a reference genome. The reference genome may be divided into a plurality of regions and then the regions may each be processed and subsequent analysis operations may be performed in each of the cases of  FIG. 8 . The subsequent analysis operations may be deduplication, local realignment, base recalibration and/or variant calling. Distribution chart (a 2 ) may be a distribution chart in which the subsequent analysis operations are performed in the case of distribution chart (a) of  FIG. 8 , and distribution chart (b 2 ) may be a distribution chart in which the subsequent analysis operations are performed in the case of distribution chart (b) of  FIG. 8 . The y-axis of each distribution chart represents time, and the x-axis represents divided regions of the reference genome. On the y-axis, time may be increased along an arrow direction. The divided regions of distribution chart (b 2 ) may be first regions (e.g., RG) according to an exemplary embodiment of the present inventive concept. In an exemplary embodiment of the present inventive concept, distribution chart (a 2 ) may be a case where the reference genome is simply divided into a plurality of regions having the same length. 
     Comparing (a 2 ) with (b 2 ), in the case of distribution chart (a 2 ), regions LOAD, in which analysis is performed for a longer time than other regions, exist. Also, the distribution of time required for analysis in each of the regions is not uniform. In the case of distribution chart (b 2 ), in which region generation (e.g., S 120 ) and/or depth filtration (e.g., S 140 ) are applied an exemplary embodiment of the present inventive concept, the regions LOAD, in which analysis is performed for a longer time, illustrated in distribution chart (a 2 ), are reduced, and the distribution of time required for analysis in each of the regions is more uniform compared to distribution chart (a 2 ). 
       FIG. 10  is a view of a distributed processing system for analyzing a genome sequence according to an exemplary embodiment of the present inventive concept. 
       FIG. 10  is a view of a configuration of a distributed processing system  300  for analyzing a genome sequence according to an exemplary embodiment of the present inventive concept. Referring to  FIG. 10 , the distributed processing system  300  for analyzing the genome sequence according to an exemplary embodiment of the present inventive concept may include a formatting unit  305 , a read alignment unit  310 , a region generation unit  320 , a deduplication unit  330 , a depth filtration unit  340 , a variant calling unit  350 , and a merging unit  360 . The distributed processing system  300  for analyzing the genome sequence according to an exemplary embodiment of the present inventive concept may perform operations to be time-sequentially performed by the method S 100  described in more detail with reference to  FIG. 2 . Duplicative descriptions of method S 100  to those described above may be omitted. Thus, the above descriptions of the method S 100  of analyzing the genome sequence may also be applied to the distributed processing system  300  for analyzing the genome sequence according to an exemplary embodiment of the present inventive concept. 
     The formatting unit  305  may include a plurality of formats FMT_ 1  to FMT_M. Pieces of information regarding the read sequences generated by sequencing and/or information regarding a base score may be stored in each of the formats FMT_ 1  to FMT_M. In an exemplary embodiment of the present inventive concept, the pieces of information may be encoded in a single ASCII code. The formats FMT_ 1  to FMT_M may be FASTQ or FASTA formats. However, exemplary embodiments of the present invention are not limited thereto. Referring to  FIG. 10 , the plurality of formats FMT_ 1  to FMT_M may be expressed as M. M may be a natural number that is equal or greater than 2. 
     The read alignment unit  310  may receive the sequenced information regarding the read sequences from the formatting unit  305  and may map the read sequences of the plurality of formats FMT_ 1  to FMT_M to a reference genome. In an exemplary embodiment of the present inventive concept, the read alignment unit  310  may include unit read alignments RA_ 1  to RA_M having the same number as that of the plurality of formats FMT_ 1 ˜FMT_M included in the formatting unit  305 . The unit read alignments RA_ 1  to RA_M included in the read alignment unit  310  may perform a distributed process. 
     The region generation unit  320  may divide the reference genome to which mapping of the read sequences is completed by the read alignment unit  310 , into a plurality of regions. 
     In an exemplary embodiment of the present inventive concept, the region generation unit  320  may include a read counter  322 , a region calculator  324 , and a region extractor  326 . 
     The read counter  322  may divide the reference genome into a plurality of second regions and may calculate information regarding the number of read sequences included in each of the second regions. In an exemplary embodiment of the present inventive concept, the read counter  322  may include unit read counters RC_ 1  to RC_M having the same number as that of the unit read alignments RA_ 1  to RA_M. Each of the unit read counters RC_ 1  to RC_M included in the read counter  322  may perform a distributed process. 
     The region calculator  324  may calculate a total number of read sequences by synthesizing the number of read sequences calculated by each of the unit read counters RC_ 1  to RC_M and may calculate the average number of read sequences by dividing the number of all the read sequences by a targeted number of first regions. 
     The region extractor  326  may extract the first regions so that the number of mapped read sequences in each of the first regions based on the average number of read sequences reaches the average number of read sequences. In an exemplary embodiment of the present inventive concept, the region extractor  326  may include unit region extractors RE_ 1  to RE_N having the same number as a targeted number of first regions. The region extractor  326  may generate information regarding the extracted first region, may perform distributed processing on the unit region extractors RE_ 1 ˜RE_N and may generate a file including information regarding the first regions. Regarding  FIG. 10 , a targeted number of first regions may be expressed as N. N may be a natural number that is equal to or greater than 2. 
     The deduplication unit  330  may remove duplicated read sequences by performing distributed processing on at least some of the N first regions. In an exemplary embodiment of the present inventive concept, the deduplication unit  330  may perform deduplication using SAMtools, SAMBLASTER and/or PICARD tools. However, exemplary embodiments of the present invention are not limited thereto. 
     The depth filtration unit  340  may remove a HD from an object to be analyzed, using the depth information regarding the read sequences. 
     In an exemplary embodiment of the present inventive concept, the depth filtration unit  340  may include a depth collector  342 , a depth calculator  344 , and an interval removal unit  346 . 
     The depth collector  342  may calculate information regarding the depth of the read sequences mapped to the reference genome by performing distributed processing on the N first regions. In an exemplary embodiment of the present inventive concept, the depth collector  345  may include N unit depth collectors DC_ 1  to DC_N having the same number as that of the first regions. Each of the unit depth collectors DC_ 1  to DC_N included in the depth collector  342  may perform a distributed process. 
     The depth calculator  344  may merge the information regarding the depth of the read sequences calculated from the unit depth collectors DC_ 1  to DC_N and may calculate statistical values based on the merged information. In an exemplary embodiment of the present inventive concept, the information regarding the depth may be frequency information, and the statistical values calculated based on the information regarding the depth may be modes, average values and/or standard deviations. 
     The interval removal unit  346  may perform distributed processing on the N first regions using the statistical values and may set an interval removed from the object to be analyzed. Setting of the interval to be removed may be performed using reference values calculated based on the statistical values calculated by the depth calculator  344 . The interval removal unit  346  may include N unit removal units IR_ 1  to IR_N having the same number as that of the first regions. 
     The variant calling unit  350  may identify a variant by performing distributed processing on the N first regions. In an exemplary embodiment of the present inventive concept, the variant calling unit  350  may include N unit calling units VC_ 1  to VC_N having the same number as that of the first regions. In an exemplary embodiment of the present inventive concept, an output of the depth filtration unit  340  may be an input of a local realignment unit and/or a base recalibration unit so that an output of the local realignment unit and/or the base recalibration unit may be an input of the variant calling unit  350 . 
     The merging unit  360  may merge information generated by the distributed processed described above and called by the variant calling unit  350 . In an exemplary embodiment of the present inventive concept, the information generated by the distributed processes described above and/or the merged information may be output in the form of a file. 
     In an exemplary embodiment of the present inventive concept, the distributed processing system  300  for analyzing the genome sequence described with reference to  FIG. 10  may perform genome sequence analysis in a pipeline manner. Thus, according to an exemplary embodiment of the present inventive concept, the reference genome may be divided into a plurality of first regions based on information regarding the number of mapped read sequences, and the read sequences may be analyzed by performing distributed processing on the first regions so that similarity of a processing time according to regions is increased and a performance time for the entire genome data analysis may be reduced. Also, a duty cycle of a resource of the distributed processing system may be reduced. 
     The distributed processing system  300  for analyzing the genome sequence may include hardware (e.g., or hardware components) for performing genome sequence analysis, software (e.g., or software components) for performing genome sequence analysis, and/or an electronic recording medium having a computer program code for performing genome sequence analysis recorded thereon. However, exemplary embodiments of the present invention are not limited thereto, and the distributed processing system  300  for analyzing the genome sequence may include a functional and/or structural combination of hardware or software for driving the hardware. 
       FIG. 11  illustrates a system for analyzing a genome sequence to which a method of analyzing a genome sequence is applied, according to an exemplary embodiment of the present inventive concept. 
       FIG. 11  illustrates a system  1000  for analyzing the genome sequence to which the method of analyzing the genome sequence according to an exemplary embodiment of the present inventive concept is applied. 
     Referring to  FIG. 11 , a genetic sample of a recipient (customer) who requests genome sequencing, for example, blood, saliva, or other bodily tissues, may be extracted at an authorized institution. The genetic sample may be the recipient&#39;s (customer&#39;s) DNA sample. 
     The recipient&#39;s DNA is a genetic material including the recipient&#39;s genetic information. DNA may be indicated as a kind of base sequence including four kinds of bases A, G, T, and C. A DNA sequence includes information regarding cells, tissues, etc. of an individual, and bases of the DNA sequence represent information regarding a connection order or an arrangement order of 20 kinds of amino acids that are protein query constituents of the individual. A particular genetic characteristic represented by the sequence of DNA that is a gene is determined according to information regarding bases in the DNA sequence. 
     For example, the individual&#39;s DNA sequence information includes information related to past and future diseases. Thus, if DNA sequence information regarding a person having a disease and DNA sequence information regarding a person having no disease can be accurately compared with each other and checked, the disease might be prevented, or an optimum treatment method at an initial stage of the disease can be selected. 
     A sequencing device  1010  may generate read sequences from the extracted genetic sample. A nucleic acid sequence analysis device  1020  may perform genome sequence analysis according to an exemplary embodiment of the present inventive concept. Thus, the nucleic acid sequence analysis device  1020  may divide a reference genome into a plurality of first regions based on information regarding the read sequences mapped to the reference genome. The nucleic acid sequence analysis device  1020  may perform distributed processing on the plurality of first regions and may then merge results of analysis processing of each of the first regions and analyzes them so that the result of analysis in which nucleic acid information regarding the recipient&#39;s genetic sample is compared with a reference genome (e.g., obtained from a genome database  10230 ). An analysis result database (DB)  1040  may store the result of analysis. 
     While the present inventive concept has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood that various changes in form and details may be made therein without departing from the spirit and scope of the present inventive concept.