KR102258897B1 - Error recovery method in genome sequence analysis and genome sequence analysis apparatus - Google Patents

Abstract

The present invention relates to an error handling method in nucleotide sequence analysis, comprising the steps of: acquiring, by an analysis device, sequence information of a plurality of reads for a sample; generating, by the analysis device, a plurality of tasks for processing data using sequence information of at least some of the plurality of reads; generating, by the analysis device, a hash value for the plurality of tasks based on sequence information input to each task; identifying, by the analysis device, a target task having the same hash value as a hash value of a specific task from among the pre-processed task group including tasks for which data processing is completed with respect to a specific task in which an error occurs among the plurality of tasks; and performing, by the analysis device, analysis by replacing an output value of the target task with an output value of the specific task. The present invention is to recover an error using the analyzed result when an error occurs in the analysis process.

Description

Error handling method and nucleotide sequence analysis apparatus in nucleotide sequence analysis {ERROR RECOVERY METHOD IN GENOME SEQUENCE ANALYSIS AND GENOME SEQUENCE ANALYSIS APPARATUS}

Techniques to be described below relate to techniques for handling errors occurring in nucleotide sequence analysis.

Next-generation sequencing technology (next generation sequencing, NGS) can decode large amounts of genomic data in a shorter time than traditional genome sequencing techniques. NGS is also called high-throughput sequencing (HTS) or massively parallel sequencing (MPS). Although NGS analysis processes large amounts of data faster than conventional techniques, NGS analysis requires considerable computing resources and time to analyze large amounts of data of several tens of gigabytes to hundreds of gigabytes or more based on complex data analysis techniques.

Korea Patent Publication No. 2019-0010404 (2019.01.30)

The NGS analysis process analyzes sequences through a plurality of tasks. For example, the analysis apparatus may perform continuous tasks according to a predetermined order. If an error occurs in a specific task, the analysis process is performed again from the beginning. In this case, there is a problem in that the resources and time invested in the analysis process are wasted.

The technique described below intends to provide a technique for recovering an error by using the analyzed result when an error occurs during the NGS analysis process.

An error handling method in nucleotide sequence analysis includes the steps of, by an analysis device, obtaining sequence information of a plurality of reads for a sample, wherein the analysis device analyzes data using sequence information of at least some of the plurality of reads generating a plurality of tasks to be processed; generating, by the analysis device, a hash value based on sequence information input to each task for the plurality of tasks; Identifying a target task having the same hash value as the hash value of the specific task from among the pre-processed task group including tasks for which data processing has been completed with respect to a specific task in which an error has occurred, and the analysis apparatus output value of the target task and performing analysis by substituting the output value of the specific task.

The nucleotide sequence analysis device includes an input device for receiving sequence information and analysis commands of a plurality of reads for a sample, a storage device for storing the sequence information and a program code for constantly analyzing the sequence information, and the plurality of reads With respect to a specific task in which an error occurs among a plurality of tasks that process data using sequence information of at least some of the reads, a hash value identical to the hash value of the specific task among the pre-processed task group including tasks for which data processing is completed and an arithmetic unit for performing analysis by replacing an output value of a target task having a value with an output value of the specific task.

The hash value is calculated based on sequence information input to the task, and the sequence information includes an identifier of a read and a sequence of the read.

The technique to be described below solves an error as a result of analysis in consideration of the characteristics of the gene sequence and the characteristics of the analysis. The techniques described below allow effective gene sequencing without wasting the resources and time administered.

1 is an example of an NGS pipeline.
2 is an example of a nucleotide sequence analysis process consisting of a plurality of tasks.
3 is an example of a flowchart for an error handling process in nucleotide sequence analysis.
4 is an example of an error handling process in nucleotide sequence analysis.
5 is an example of a nucleotide sequence analyzer.

The technology to be described below may be modified in various ways and may have various embodiments, and specific embodiments will be illustrated in the drawings and described in detail. However, this is not intended to limit the technology to be described below with respect to a specific embodiment, and it should be understood to include all changes, equivalents, or substitutes included in the spirit and scope of the technology to be described below.

Terms such as 1st, 2nd, A, B, etc. may be used to describe various components, but the components are not limited by the above terms, and only for the purpose of distinguishing one component from other components. Is only used. For example, a first component may be referred to as a second component, and similarly, a second component may be referred to as a first component without departing from the scope of the rights of the technology described below. The term and/or includes a combination of a plurality of related listed items or any of a plurality of related listed items.

In terms of terms used herein, the singular expression should be understood to include the plural expression unless the context clearly dictates otherwise, and terms such as "comprises" include the described feature, number, step, operation, element. , parts or combinations thereof are to be understood, but not to exclude the possibility of the presence or addition of one or more other features or numbers, step operation components, parts or combinations thereof.

Prior to the detailed description of the drawings, it is intended to clarify that the division of the constituent parts in the present specification is merely divided by the main function that each constituent part is responsible for. That is, two or more constituent parts to be described below may be combined into one constituent part, or one constituent part may be divided into two or more for each more subdivided function. In addition, each of the constituent units to be described below may additionally perform some or all of the functions of other constituent units in addition to its own main function, and some of the main functions of each constituent unit are different. It goes without saying that it can also be performed exclusively by.

In addition, in performing the method or operation method, each of the processes constituting the method may occur differently from the specified order unless a specific order is clearly stated in the context. That is, each of the processes may occur in the same order as the specified order, may be performed substantially simultaneously, or may be performed in the reverse order.

Genomic data refers to genetic information calculated by analyzing a sample. For example, genomic data includes a nucleotide sequence obtained from deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or protein (protein) from cells, tissues, etc., gene expression data, genetic variation with standard genomic data, and DNA methylation (methylation). ) and the like.

Bioinformatics can be said to be a knowledge system that structures information related to biological molecules on a large scale. A bioinformatics pipeline refers to an analysis component including software, a script, and the like for processing and analyzing data of biological information in a preset order. Therefore, the technology to be described below can be said to be a method of recovering errors in the bioinformatics analysis pipeline and analyzing genomic data. However, for the convenience of the following description, NGS will be mainly described.

NGS analysis is a representative technique for calculating genomic data through sample analysis. NGS analysis is a technique that divides the genome into countless fragments (reads) and deciphers the genome by combining each nucleotide sequence. NGS analysis is used for gene sequencing and gene mutation detection. Gene sequencing includes Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES), and Targeted Gene Sequencing (TGS).

The analysis device refers to a device that processes and analyzes genome data. The analysis device may be implemented in various forms, such as a computer device, a PC, a smart device, a server on a network, and the like. The analyzer may be a device for analyzing a sample (eg, NGS equipment). The analysis device may be a separate device that receives and processes data from the NGS equipment.

1 is an example of an NGS pipeline. 1 is an example of a general NGS pipeline and an example showing an important pipeline. The NGS analysis is analyzed using a large number of reads.

In Fig. 1, the NGS pipeline starts from FASTQ (110). FASTQ is genomic analysis raw data for a sample (individual). FASTQ is a file including an identifier (ID) for each of a plurality of reads, a nucleotide sequence, and a quality index of the read. Although not shown in FIG. 1, FASTA is generated before FASTQ, and FASTA consists only of an identifier and a base sequence of a read. The analyzer generates a FASTA file, and then creates a FASTQ file. FASTQ is a text-based file and corresponds to the de facto standard file format for NGS analysis.

The analyzer maps (aligns) the reads included in the FASTQ of the sample to the reference genome (120). The reference genome refers to a genome sequence used for aligning reads, and generally, a standard nucleotide sequence that has been completed sequencing and constructed as a public database is used.

The analysis device generates a result of mapping to the reference genome in a sequence alignment/map (SAM) format file, and converts the SAM into a binary alignment/map (BAM) format file (130). BAM corresponds to initial data to which reads are mapped.

The analysis apparatus may perform post-processing on the BAM file ( 140 ). For example, the analyzer can evaluate and correct that the leads are properly aligned. The analysis device may perform local realignment using information about the expected insertion/deletion location.

The analysis device may detect a variant (variant calling) in the analysis process ( 150 ). Variant detection is the process of detecting sequence differences between a sequence generated from a sample and a reference genome. The analysis device stores the detected mutation as a file in a VCF (variant call format) format (160). The VCF contains information about the location of the mutation in the sample genome.

Thereafter, the analysis device may perform additional work on the mutation information ( 170 ). For example, the analyzer may filter out variants that are considered false positives. Alternatively, the analysis device may add related information about the mutation (variant annotation). Alternatively, the analyzer may assign a priority to the mutation.

The analyzer typically generates FASTA, FASTQ, BAM, SAM, and VCF files during sample sequencing. The analysis apparatus generates a file of the above format for a large number of reads, and may generate a plurality of tasks to perform the task in units of tasks.

2 is an example of a nucleotide sequence analysis process consisting of a plurality of tasks. The file processed by the analysis device may be at least one of FASTA, FASTQ, BAM, SAM, and VCF.

2(A) is an example of processing input data as a continuous task. The analysis apparatus processes task 1 (Task 1) to task n (Task n). At this time, each task is performed sequentially. For example, the analysis device performs task 1 , and the output output from task 1 is input to task 2 . In this case, data processed in task 1 , task 2 , task 3 , ..., and task n may have a certain correlation. That is, sequence information received by a plurality of tasks may be the same or similar.

2(B) is an example of processing input data in a parallel processing method. The analysis device may process data in parallel in order to process a large amount of data. 2(B) shows 1, 2, ..., k parallel processing processes, and each process consists of n consecutive tasks. An individual process may use some of the samples' reads as input data. Some of the plurality of reads may have similar sequences. Furthermore, some of the plurality of reads may have the same sequence. Accordingly, not only the same process, but also sequences processed in different processes may be similar or identical. Accordingly, that is, sequence information received by a plurality of tasks may be the same or similar.

The analysis apparatus processes the reads as a plurality of tasks in a single process or a parallel process, and in this case, at least some of the plurality of tasks may process the same sequence according to the same command. Based on this, when an error occurs in the nucleotide sequence analysis as follows, the analysis device may continue the analysis by replacing the analysis result of the corresponding task without restarting the analysis from the beginning.

3 is an example of a flowchart for an error handling process 200 in nucleotide sequence analysis. The analysis device acquires sequence information of a plurality of reads ( 210 ). Here, the sequence information may be included in files of various formats. For example, the analysis device may acquire sequence information included in at least one file of FASTA, FASTQ, BAM, SAM, and VCF. The analysis device can resolve errors in the analysis of sequence information included in the file at any point during the analysis process. That is, the analysis apparatus may recover an error in at least one process (block) of the bioinformatics analysis pipeline in the manner described below.

The analysis device generates a task and performs analysis (220). The analysis apparatus generates a plurality of tasks, and the plurality of tasks may be tasks that are generated sequentially or simultaneously.

A task can be defined based on two pieces of information. A task may be defined as (i) input sequence information and (ii) a data processing command for the sequence information. Sequence information is a sequence processed by a corresponding task, and may consist of at least one read. Commands include quality evaluation of base sequences (eg, FASTQ generation), read mapping, duplicate sequence removal, realignment, base recalibration, mutation detection, and the like.

The analysis device generates a hash value for each task for each task ( 230 ). The analysis device stores the hash value for each task and the output value of the task in the storage medium (230). The analysis device generates a hash value based on the information defining the task. For example, the analysis device may generate a hash value based on sequence information used in the task. Also, the analysis device may generate a hash value based on sequence information and commands used in the task. Since both sequence information and commands can be expressed as digital values, the analyzer can convert a specific task into a hash value.

The analysis device monitors whether an error occurs in a specific task in the process of processing data using the task (240). If no error occurs (NO in 240), the analyzer continues to generate tasks and analyzes the genome data.

If an error occurs (YES in 240), the analyzer compares the hash value of the specific task in which the error has occurred with the hash values of the pre-stored tasks (250). The analysis device checks whether there is a hash value identical to the hash value of a specific task among the hash values stored in the storage medium (250). A task having the same hash value as a specific task is called a target task. If there is no identical hash value (NO of 250), the analysis device determines that an error has occurred, and proceeds with the process after the error has occurred. For example, the analysis device may restart analysis from the beginning.

If there is the same hash value (YES in 250), the analysis device extracts the output value of the target task having the same hash value as the specific task from the storage medium. The analysis device replaces the output value of the specific task with the output value of the target task (260). Since the specific task and the target task have the same sequence to be used and the command to be processed, the analysis device can replace the result of the specific task in which an error has occurred with the result of the target task. Of course, the sequences used in the specific task and the target task may be sequences in the same region, or reads using only different regions may be the same.

If the analysis is not finished (NO in 270), the analysis device repeatedly performs the analysis and error recovery while creating the task.

4 is an example of an error processing process 300 in nucleotide sequence analysis. In FIG. 4, a plurality of analysis devices such as analysis device A and analysis device K are exemplified.

The storage medium is a device for storing hash values and output values for tasks. The storage medium may be a device embedded in or connected to the analysis device or a device (database, server, etc.) connected to a network. The storage medium may include various storage media such as a non-volatile memory and a hard disk.

(i) The storage medium may store hash values and output values for tasks generated while one analysis device performs a specific analysis. In this case, the storage medium stores values for intra-analysis tasks.

(ii) In addition, the storage medium may store hash values and output values for tasks generated while a plurality of analysis devices perform specific genome data analysis, respectively. In this case, the storage medium may store not only intra-analysis tasks, but also inter-analysis tasks. The sequence information analyzed by the inter-analysis tasks may be collected from different samples (individuals). For example, since a specific individual (human) has a shared gene sequence, at least one read used by different tasks may be the same.

The process in which the analysis device A recovers the error of the task will be mainly explained. Analysis device A generates sequence information (310). The sequence information may be at least one of FASTA, FASTQ, BAM, SAM, and VCF. Even if not for NGS analysis, the sequence information may be in various formats depending on the analysis pipeline, and the sequence information generated by the analysis device A may be information included in a specific file used in the analysis pipeline.

The analysis device A processes input data for each task while generating a plurality of tasks ( 320 ). 4 shows an example in which the analysis device A generates tasks 1 to n.

Assume that an error occurred in task 3. Based on the hash value (#0031) of task 3 in which the error occurred, the analysis device A checks whether there is a task having the same hash value in the storage medium. The storage medium has the same hash value. In this case, the analysis device A extracts an output value (output 3) for the same hash value (#0031). The analysis device A replaces the extracted output value (output 3) with the result value of task 3 ( 330 ).

The analysis apparatus inputs output 3, which is the result value of task 3, to the next task to perform subsequent analysis, and when task n is performed, the analysis is terminated (340).

5 is an example of the nucleotide sequence analyzer 400 . The nucleotide sequence analyzer 400 is an apparatus for analyzing the above-described genomic data. The nucleotide sequence analyzer 400 performs genome analysis by recovering a task in which an error has occurred according to the above-described method.

The analysis device 400 may be physically implemented in various forms. For example, the analysis device 400 may have the form of a computer device such as a PC, a server on a network, a chipset dedicated to data processing, and the like. The computer device may include a mobile device such as a smart device or the like.

The analysis device 400 may include a storage device 410 , a memory 420 , an arithmetic device 430 , an interface device 440 , a communication device 450 , and an output device 460 .

It is assumed that the analysis device 400 has generated raw data (eg, FASTA or FASTQ) for a sample to be analyzed.

The storage device 410 stores the genome data generated by the analysis device 400 . The storage device 410 may store information on a plurality of leads. Taking NGS as an example, the storage device 410 may store a file in at least one format of FASTA, FASTQ, BAM, SAM, and VCF.

The storage device 410 may store sequence information and a program (code) for constantly analyzing sequence information. The storage device 410 may store a program for recovering an error of a task as in the above-described method.

The storage device 410 may store a hash value and an output value for the task. Although the storage device 410 is illustrated as a single object, it may be composed of a variety of physically different media.

The storage device 410 may store a result of analyzing the sequence information of the sample (sequencing result, mutation detection result, etc.).

The memory 420 may store data and information generated while the analysis device 400 analyzes the genetic data.

The interface device 440 is a device that receives predetermined commands and data from the outside. The interface device 440 may receive sequence information of the sample from a physically connected input device or an external storage device. The interface device 440 may receive a command for processing sequence information. The command may be at least one of quality evaluation, read mapping, duplicate sequence removal, rearrangement, base re-correction, mutation detection, and the like for a nucleotide sequence.

The communication device 450 means a configuration for receiving and transmitting certain information through a wired or wireless network. The communication device 450 may receive sequence information of the sample from an external object. The communication device 450 may receive a command for processing sequence information. The communication device 450 may transmit a sample analysis result to an external object.

The communication device 450 and the interface device 440 are devices that receive predetermined data or commands from the outside. The communication device 450 or the interface device 440 may be referred to as input devices.

The output device 460 is a device that outputs certain information. The output device 460 may output an interface necessary for a data processing process, an analysis result, and the like.

The computing device 430 may analyze the genome data using a program stored in the storage device 410 , and may recover a task in which an error has occurred.

The computing device 430 may generate a plurality of tasks for processing data by using sequence information of at least some of the plurality of reads. The computing device 430 may process data by performing any one of a plurality of tasks or a plurality of tasks at the same time.

The computing device 430 may store a hash value and an output value for each task in the storage device 410 or a separate storage medium.

The computing device 430 may monitor whether an error occurs in the task being analyzed.

The arithmetic unit 430 compares the hash value of the specific task in which the error has occurred and the hash value of the tasks (task group) pre-stored in the storage device 410 or the storage medium, and checks whether there is a hash value identical to the hash value of the specific task. do.

When there is a target task having the same hash value as the hash value of the specific task among the pre-stored task groups, the computing device 430 replaces the output value of the specific task with the output value of the target task. In other words, the arithmetic unit 430 overwrites the output value of the specific task with the output value of the target task.

The computing device 430 may continue the analysis process while inputting the output value of a specific task to another task.

The computing device 430 may be a device such as a processor, an AP, or a chip embedded with a program that processes data and processes a predetermined operation.

In addition, the nucleotide sequence analysis method or the nucleotide sequence error recovery method as described above may be implemented as a program (or application) including an executable algorithm that can be executed on a computer. The program may be provided by being stored in a temporary or non-transitory computer readable medium.

The non-transitory readable medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short moment, such as a register, a cache, and a memory. Specifically, the various applications or programs described above are CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM (read-only memory), PROM (programmable read only memory), EPROM (Erasable PROM, EPROM) Alternatively, it may be provided by being stored in a non-transitory readable medium such as an EEPROM (Electrically EPROM) or a flash memory.

Temporarily readable media include Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (Enhanced) SDRAM, ESDRAM), Synchronous DRAM (Synclink DRAM, SLDRAM) and Direct Rambus RAM (Direct Rambus RAM, DRRAM) refers to a variety of RAM.

The present embodiment and the drawings attached to this specification merely clearly show a part of the technical idea included in the above-described technology, and within the scope of the technical idea included in the specification and drawings of the above-described technology, those skilled in the art can easily It will be apparent that all inferred modified examples and specific embodiments are included in the scope of the above-described technology.

Claims (12)

acquiring, by the analysis device, sequence information of a plurality of reads for the sample;
generating, by the analysis device, a plurality of tasks for processing data using sequence information of at least some of the plurality of reads;
generating, by the analysis device, a hash value for the plurality of tasks based on sequence information input to each task;
identifying, by the analysis apparatus, a target task having the same hash value as the hash value of the specific task from among the pre-processed task group including tasks for which data processing is completed with respect to a specific task in which an error has occurred among the plurality of tasks; and
Comprising the step of the analysis device replacing the output value of the specific task with the output value of the target task, and performing analysis using the replaced output value,
The sequence information includes an identifier of each read and a sequence of each read,
At least two or more tasks among the plurality of tasks have the same processing command and sequence information used for each task,
The output value is an error processing method in nucleotide sequence analysis, which is a result of processing data by task. The method of claim 1,
The plurality of tasks is an error handling method in sequencing analysis including at least two or more tasks from a task group including tasks performed at the same time and tasks performed at different points. The method of claim 1,
The analysis apparatus generates the hash value by using the sequence information for each of the plurality of tasks and a data processing command constituting the task. The method of claim 1,
The method for processing errors in nucleotide sequence analysis further comprising the step of the analyzing apparatus storing a hash value and an output value for each of the plurality of tasks in a storage medium. The method of claim 1,
The method for handling errors in nucleotide sequence analysis in which the sequence information is stored in a file that is at least one of file formats including FASTA, FASTQ, SAM, BAM, and VCF. The method of claim 1,
The pre-processing task group is
contains tasks in the analysis in which the particular task is used, or
A method for handling errors in sequencing, comprising tasks in the analysis and tasks in a separate analysis not related to the specific task. an input device for receiving sequence information and analysis commands of a plurality of reads for a sample;
a storage device for storing the sequence information and a program code for constantly analyzing the sequence information; and
With respect to a specific task in which an error occurs among a plurality of tasks for processing data using sequence information of at least some of the reads among the plurality of reads, a hash of the specific task from among the pre-processed task groups including tasks for which data processing is completed A computing device for checking an output value of a target task having the same hash value as a value, replacing the output value of the specific task with an output value of the target task, and performing analysis using the replaced output value,
The hash value is calculated based on sequence information input to the task, and the sequence information includes an identifier of a read and a sequence of the read,
The output value is a nucleotide sequence analysis device that is a result of processing data by task. The method of claim 7,
The arithmetic unit generates the hash value for each of the plurality of tasks by using the sequence information and a data processing command constituting the task. The method of claim 7,
The storage device is a nucleotide sequence analysis device that further stores a hash value and an output value for each of the tasks. The method of claim 7,
The arithmetic unit is a nucleotide sequence analysis device for performing analysis on the sample using a file that is at least one of file formats including FASTA, FASTQ, SAM, BAM, and VCF. The method of claim 7,
The pre-processing task group is
contains tasks in the analysis in which the particular task is used, or
A sequencing apparatus comprising tasks in the analysis and tasks in a separate analysis not related to the specific task. The method of claim 7,
The specific task and the target task are tasks in which a processing command and sequence information used for each task are the same.

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