KR20220086458A - Next-generation sequencing method for sharing genetic data, next-generation sequencing device and next-generation sequencing program - Google Patents

Abstract

In order to solve the above problems, a next-generation sequencing method according to an embodiment of the present invention provides a nucleotide sequence analysis method, comprising: performing diagnosis including clinical treatment and biopsy on a patient; obtaining a sample for next-generation sequencing through the clinical process; preparing a library for the next-generation sequencing; performing sequencing of the sample to obtain a read sequence of the sample included in the library; performing base calling from the sequenced read sequence; aligning the read sequences; pre-processing the aligned read sequences; performing variant calling on the read file from the preprocessed read sequence; and generating genetic data including data derived from the patient, the sample, the test equipment used for the sample experiment, the analysis equipment used for the sample analysis, and the sample.

Description

Next-generation sequencing method, next-generation sequencing device, and next-generation sequencing program for sharing genetic data

The present invention relates to a next-generation sequencing method for sharing genetic data, a next-generation sequencing device, and a next-generation sequencing program.

In recent years, there is an increasing trend in research on nucleotide sequence analysis to improve human health.

Furthermore, with the rapid development of next-generation sequencing technology, the human genome is being used in a clinical environment to realize precision medicine. The data obtained in the clinical environment can be shared with other institutions when the patient moves, or can be shared at the request of the patient.

Therefore, in order to share health information through large-scale next-generation sequencing, the need to generate clinical genome information and share the generated data through next-generation sequencing technology is also increasing.

In addition, it is necessary to share clinical genome information at a level that can reproduce the results of the institution that first acquired the clinical genome information. In addition, shared clinical genome genomic data should be interoperable.

Therefore, there is an increasing need to propose a data specification for integrating clinical data with multi-layered sequencing files and related parameters to achieve reproducibility of genomic data in clinical practice.

(Patent Document 0001) KR 10-1007926

An object of the present invention to solve the above problems is a next-generation sequencing method, a next-generation sequencing apparatus, and a next-generation sequencing method for sharing genetic data that increase the convenience of data utilization by providing a standard to the data formed during the next-generation sequencing analysis It is to provide a sequencing program.

In order to solve the above problems, a next-generation sequencing method according to an embodiment of the present invention provides a nucleotide sequence analysis method, comprising: performing diagnosis including clinical treatment and biopsy on a patient; obtaining a sample for next-generation sequencing through the clinical process; preparing a library for the next-generation sequencing; performing sequencing of the sample to obtain a read sequence of the sample included in the library; performing base calling from the sequenced read sequence; aligning the read sequences; pre-processing the aligned read sequences; performing variant calling on the read file from the preprocessed read sequence; and generating genetic data including data derived from the patient, the sample, the test equipment used for the sample experiment, the analysis equipment used for the sample analysis, and the sample.

In addition, the step of forming the genetic data includes at least one of the patient's name, the patient's unique identifier, the patient's date of birth, the patient's gender, the patient's race, the patient's diagnostic information, and the patient's treatment information It may include; forming the genetic data based on the patient information including.

In addition, the forming of the genetic data may include: forming the genetic data based on the sample information including at least one of an institution for obtaining the sample, the date the sample was sampled, and the type of the sample have.

In addition, the step of forming the genetic data includes a data quality control index of the experimental equipment used for the sample experiment, base calling information of the experimental equipment, the depth of the read sequence, and the depth of the reference allele of the read sequence. , depth of replacement allele of the read sequence, allele frequency of the read sequence, genotype of the read sequence, sequencing platform information of the read sequence, sequencer type of the read sequence, library preparation technique of the read sequence, the read It may include; forming the genetic data based on the experimental equipment information including at least one of a target capture method of a sequence, a type of the read sequence, and a length of the read sequence.

In addition, the step of forming the genetic data may include aligning the read sequence, correcting the aligned read sequence, detecting a genetic mutation from the corrected read sequence, and matching the annotation with the detected genetic mutation to the analysis equipment It may include; forming information, and forming the genetic data based on the analysis equipment information.

In addition, the step of forming the genetic data may include: forming the genetic data based on derived data including at least one of FASTQ including a gene sequence and a sequence quality score, a gene sequence alignment map, BAM, CRAM, VCF, and MAF ; may be included.

In addition, the step of sequencing the sample to obtain a read file of the sample included in the library,

The method may include extracting and selectively arranging an axon region from the read file using one of a plurality of predetermined methods.

The next-generation sequencing apparatus according to an embodiment of the present invention is an apparatus for performing next-generation sequencing, comprising:

Memory; and at least one processor configured to communicate with the memory.

The at least one processor performs diagnosis including clinical processing and biopsy on the patient, obtains a sample for next-generation sequencing through the clinical processing, prepares a library for next-generation sequencing, and , performing sequencing of the sample to obtain a read file of the sample included in the library, performing base calling from the sequenced read file, aligning the read file, and the aligned read file preprocessed,

Variant calling is performed on the read file from the preprocessed read file, and the patient, the sample, the laboratory equipment used for the sample experiment, the analysis equipment used for the sample analysis, and data derived from the sample It is possible to form genetic data comprising

In addition, the at least one processor includes at least one of the patient's name, the patient's unique identifier, the patient's date of birth, the patient's gender, the patient's race, the patient's diagnostic information, and the patient's treatment information The genetic data may be formed based on the patient information.

In addition, the at least one processor may form the genetic data based on the sample information including at least one of an institution for obtaining the sample, a date the sample was sampled, and a type of the sample.

In addition, the at least one processor may include a data quality control indicator of an experimental equipment used for the sample experiment, base calling information of the experimental equipment, a depth of the read sequence, a depth of a reference allele of the read sequence, the Depth of alternate allele of read sequence, allele frequency of said read sequence, genotype of said read sequence, sequencing platform information of said read sequence, sequencer type of said read sequence, library preparation technique of said read sequence, said read sequence The genetic data may be formed based on the experimental equipment information including at least one of a target capture method, a type of the read sequence, and a length of the read sequence.

In addition, the at least one processor aligns the read sequence, corrects the aligned read sequence, detects a genetic mutation from the corrected read sequence, and matches the annotation to the detected genetic mutation to obtain the analysis equipment information formed, and the genetic data may be formed based on the analysis equipment information.

In addition, the at least one processor is configured to include at least one of a FASTQ comprising a gene sequence and a sequence quality score, a gene sequence alignment map (SAM), a binary alignment map (BAM), a CRAM, a VCF, and a derived data comprising a MAF. It is possible to form the genetic data based on the.

Also, the at least one processor may extract and selectively arrange the axon region from the read file using one of a plurality of predetermined methods.

The program according to an embodiment of the present invention is combined with a computer, which is hardware, comprising: performing diagnosis including clinical processing and biopsy on a patient; obtaining a sample for next-generation sequencing through the clinical process; preparing a library for the next-generation sequencing; performing sequencing of the sample to obtain a read sequence of the sample included in the library; performing base calling from the sequenced read sequence; aligning the read sequences; pre-processing the aligned read sequences; performing variant calling on the read file from the preprocessed read sequence; and forming genetic data including the patient, the sample, the laboratory equipment used for the sample experiment, the analysis equipment used for the sample analysis, and data derived from the sample; have.

Other specific details of the invention are included in the detailed description and drawings.

According to the embodiments disclosed in the present invention, it is possible to increase the convenience of data utilization by providing a standard for data formed during next-generation sequencing analysis.

In addition, according to the embodiments disclosed in the present invention, there is an effect that can be used to store and share clinical genomic data in electronic health records.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram showing the main structure of a genome data model according to an embodiment of the present invention.
2 is a diagram illustrating a configuration of a workflow for next-generation nucleotide sequence preprocessing according to an embodiment of the present invention.
3 is a diagram illustrating a configuration of a workflow for parallel sequencing pre-processing according to an embodiment of the present invention.
4 is a diagram illustrating a next-generation sequencing process in clinical practice according to an embodiment of the present invention.
5 is a diagram illustrating a patient-centered health information exchange process using a parallel sequencing file according to an embodiment of the present invention.
6 is a view showing the overall operation of the next-generation nucleotide sequence analysis method according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

The word "exemplary" is used herein in the sense of "used as an illustration or illustration." Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

Also, as used herein, the term “unit” refers to a hardware element such as software, FPGA, or ASIC, and “unit” performs certain roles. However, "part" is not meant to be limited to software or hardware. A “unit” may be configured to reside on an addressable storage medium and may be configured to refresh one or more processors. Thus, by way of example, “part” refers to elements such as software elements, object-oriented software elements, class elements and task elements, and processes, functions, properties, procedures, subroutines, and programs. It includes segments of code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functionality provided within elements and “parts” may be combined into a smaller number of elements and “parts” or further separated into additional elements and “parts”.

In addition, all “units” of the present specification may be controlled by at least one processor, and at least one processor may perform operations performed by the “units” of the present disclosure.

Embodiments of the present disclosure may be described in terms of a function or a block performing a function. Blocks, which may be referred to as 'parts' or 'modules', etc. in the present disclosure include logic gates, integrated circuits, microprocessors, microcontrollers, memories, passive electronic components, active electronic components, optical components, hardwired circuits, and the like. It may be physically implemented by analog or digital circuitry, such as, and optionally driven by firmware and software.

Embodiments of the present disclosure may be implemented using at least one software program running on at least one hardware device and may perform a network management function to control an element.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

For the purposes of this International Standard, the following terms and definitions are applicable.

ISO and IEC may maintain a database of terms used for standardization at the address below.

ISO/TS 20428 is a specification for clinical sequencing generation documentation, and ISO/CDTS 23357 may refer to a data file format for clinical sequencing.

Some data elements defined in ISO/TS 20428 can be reused in ISO/CD TS 23357.

In the present invention, 'chromosomes' are prokaryotic cells or the nucleus of a prokaryotic cell, and are composed of DNA, or a structure containing DNA can deliver essential genetic information to the cell. The chromosome may include a human characteristic or some characteristics, a chromosome included in a part of a tissue or cell line, or a protein component (eg, histone).

In the present invention, 'clinical sequencing' may refer to next-generation genome analysis technology or future genome analysis technology that uses human samples for clinical practice and clinical trials.

In the present invention, 'ClinVar' may refer to an online database that is freely accessible and summarizes the relationship between human genome mutations and phenotypes together with supporting evidence.

In the present invention, 'Copy Number Variation (CNV)' may refer to an attribute type that assigns a change in the number of copies of one or more specific sequences from a reference sequence of the same type.

In the present invention, 'COSMIC (Catalogue of Somatic Mutations in Cancer)' may refer to an online database of genetic mutations found in human cancer.

In the present invention, 'dbSNP' may refer to a single nucleotide polymorphism (SNP) database provided by the US National Center for Biological Information (NCBI).

In the present invention, 'deletion' may refer to a phenomenon in which a part of a chromosome or a DNA sequence is lost during DNA replication, resulting in mutation.

In the present invention, 'deoxyribonucleic acid (DNA)' may refer to a high molecular compound encoding genetic information of a cell nucleus.

In the present invention, 'DNA sequencing' may refer to determining the order of nucleotide bases (adenine, guanine, cytonine, and thymine) constituting DNA.

In the present invention, 'exome' may refer to the sum of exon regions among the regions of the genome.

In the present invention, 'FASTQ' may refer to a text-based format that stores both a biological sequence (nucleolysis sequence) and a quality score.

In the present invention, a 'gene' may refer to a nucleic acid sequence functioning as a genetic unit and a basic command code for growth, reproduction, and maintenance of an organism.

In the present invention, 'germline' is a cell line derived from gametes, and may refer to a cell line that persists even after several generations.

In the present invention, 'insertion' may refer to the addition of one or more base pairs to a DNA sequence.

In the present invention, 'inversion' may refer to a phenomenon in which chromosome fragments are reversed from end to end and chromosomes are rearranged.

In the present invention, 'Mutation Annotation Format (MAF)' may refer to a tab-delimited text file including mutation information aggregated in a VCF (variant call format) file and generated at the project level.

Next generation sequencing Massive parallel sequencing (NGS) technology can sequence millions of small DNA fragments in parallel.

In the present invention, 'pathogenic' may refer to a characteristic that is objectively measured and evaluated as an indicator of a pharmacological response to a normal biological process, pathogenic treatment, and/or therapeutic intervention.

In the present invention, 'read' may refer to a fragmented nucleotide sequence used to reconstruct the original sequence for next-generation sequencing technology.

In the present invention, a 'read type' refers to a type of read used during sequencing, and the read type may be unidirectional or bidirectional.

Here, the single-end read type performs sequencing from one end of a single read fragment to the other. Also, in paired-end read types, sequencing is performed from one end to the other, and then sequencing is performed again at the opposite end.

In the present invention, a 'reference sequence' may refer to a reference genome sequence used for genome analysis.

In the present invention, 'ribonucleic acid (RNA)' may refer to a polynucleotide composed of a chain having a repeating backbone of phosphate and ribose units attached to a nitrogen base. The source of RNA in the human body can be specified and may include ribosomal RNA (rRNA), messenger RNA (mRNA), transfer RNA (tRNA), micro RNA (miRNA), and other non-coding RNA (ncRNA). have.

In the present invention, 'somatic cell' may refer to a body cell as opposed to a germ cell.

In the present invention, a 'specimen' or 'biospecimen' refers to a tissue, bodily fluid collected or obtained to support the evaluation, diagnosis, treatment, sedation or prevention of a disease, disorder or abnormal body condition or symptom. , may refer to samples of food or other substances.

In the present invention, a 'subject to be treated' may refer to any person who uses or is likely to use health care services.

In the present invention, 'target capture' may refer to a method of extracting only a genomic region of interest from a DNA sample prior to sequencing.

In the present invention, 'VCF (variant call format)' may refer to a format of a text file used in bioinformatics to store a gene sequence variation.

In the present invention, 'whole exome sequencing (WES)' may refer to a technique for sequencing only all protein-coding genes.

In the present invention, 'whole genome sequencing (WGS)' may refer to a technique for determining the complete DNA sequence of an organism's genome at once.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram showing the main structure of a genome data model according to an embodiment of the present invention.

The clinical genome information model defines the structure and structure of information related to the delivery of clinical genome data generated by next-generation sequencing technology.

As shown in Fig. 1, the clinical genome information model shows the relationship between major structures.

The patient ( S101 ) of FIG. 1 may refer to a person who has received or registered health care services or one or more subjects of research for other purposes such as clinical trials. In this case, information on the management subject for sequencing the sample shall be indicated in ISO 22220:2011.

According to an embodiment of the present invention, patient-related metric information may be organized as shown in [Table 1] below. The patient-related metric information may include identifier, name, date of birth, gender, race, diagnosis, diagnosis age, treatment, prior treatment, and treatment result.

According to an embodiment of the present invention, the identifier should include a unique identifier for the treatment object.

In addition, the name should include the name of the subject to be treated. In addition, the date of birth must include the date of birth of the subject to be treated in order to calculate the patient's age. The date of birth may be expressed as KS X ISO 8601.

In addition, the gender of the subject may be expressed according to KS X ISO/TS 22220:2011.

In addition, the race of the subject to be treated should be indicated to indicate their genetic origin.

Race information can be expressed based on HL7 V3 Code System Race.

In addition, race information may use a corresponding coding system if there is a national standard such as the US FDA guidance for Industy — Collection of Race and Ethnicity Data in Clinical Trials.

Diagnostic information shall indicate the relevant disease and phenotype using data from the investigation, analysis and recognition of the presence and nature of disease, condition or injury resulting from the expressed signs and symptoms, ICD codes or SNOMED-CT codes.

In addition, the age at the time of diagnosis after birth, which is the diagnosis age, should be expressed in years.

Pre-treatment is a textual description of any previous treatment received prior to the collection of the body specimen.

In addition, the treatment result may refer to a text term that describes the final result of the patient after treatment.

category metric Explanation options patient identifier - essential name - essential date of birth ISO 8601 essential gender male, female essential race HL7 v3 Code System Race essential Diagnosis ICD 10 or 11, SNOMED-CT, or other widely-adopted ontologies essential age of diagnosis essence essential prior treatment options Treatment results options

In the sample (specimen, S102) of FIG. 1 , sample information may be expressed using a patient identification type code of KS X ISO/TS 22220:2011.

In addition, the derived tissue or organ refers to an anatomical site from which a sample was obtained, and the anatomical site may be expressed as SNOMED CT or other vocabulary.

The collection date is the date the sample was obtained and is indicated according to ISO 8601.

Sample types include sample collection (e.g. biopsy, surgical excision, EDTA, heparin), processing (e.g. formalin, centrifugation), storage (e.g. paraffin block, cryolysis). Among the cryotube), the sample type for which there is relevant data (e.g., whole blood, cell, urine, fresh cell & tissue) is expressed according to the standard pre-analysis method (SPREC) of

[Table 2] below summarizes sample-related metric information in a table.

category metric Explanation options sample general details Identifier type code of ISO 22220:2011 essential derived tissue or organ SNOMED CT essential collection date ISO 8601 essential sample type SPREC V3.0 essential

Information on a sequencing technique (eg, a sequencing platform, a capture method, and related necessary data) should be provided for the experimental equipment of FIG. 1. Metric information about the experimental equipment S104 is as shown in [Table 3]. Quality control, base calling information, read depth, reference allele depth, alternative allele depth, sequencing platform information, sequencer type, library preparation technique, target capture method, read sequence of the type and length of the read sequence.

[Table 3] is a table of metric information for actual equipment.

For quality control (S103), relevant quality control (QC) indicators for sequencing and analysis may be provided.

Reports may include overall QC metrics for bioinformatics, QC metrics for overall variations of pre- and post-lead alignments, and QC metrics for specific variations based on the report creator's decision. (Example: Sequencing yield, number of total reads, (Average) read length(bp), number of reads mappedto reference genome(mapping yield, %), N (the bases were not used for base call) base (%), GC (%), Q20 (%), Q30 (%), On target coverage (%) > 1x, On target covergae (%) > 10x, on target coverage (%) > 20x, On target coverage (%) > 100x, mean deapth, and uniformity) (eg GC = 53.2 %, AT = 47,8 %, Q30 = 94,4 %, Q20 = 97,1 %)

Through the base calling information, it is possible to know information about the base detection generated by the base detection software to identify the nucleotide sequence. Other fields may be added based on the decision of the organization performing the clinical genomic analysis.

The read depth is the average number of nucleotides per base used to find the entire genome, and can be reported in a manner commonly used in the field of bioinformatics.

Reference allelic depth can be reported as used in conventional bioinformatics field.

Regarding the allele frequency, the frequency of occurrence of an alternative allele at each base position can be reported.

As for the genotype, pairs of alleles at a single location can be reported.

Sequencing platform information may be provided as text information regarding sequencing technology and data including a sequencing platform, a capture method, and an alignment algorithm.

Regarding the type of sequencers, information on specific sequencing equipment that performs sequencing should be given. (Examples: Illumina Hiseq 2500, Thermo Fisher Ion Torrent, Illumina MiSeq)

category metric value expression options laboratory equipment Quality Management message essential Base calling information message essential lead depth essence essential Reference allele depth essence options Alternative Allele Depth essence options Sequencing Platform Information message essential Sequencer type message essential How to prepare a library message Required How to capture the target message Required lead type message Required lead length message options

The data generated by the NGS technology of the analysis equipment of FIG. 1 is analyzed in several steps.

In the first analysis, millions to billions of read sequences can be initially generated by the sequencing platform.

In a second analysis, NGS reads can be aligned with a reference sequence to identify positions where sequence variations exist. The mutations found in this way can be identified that are related to the patient's clinical condition through a third analysis.

In the analysis equipment (S105), the second and third analysis of NGS can be defined as a process from “read alignment” to “mutation annotation” and related variables can be defined. For sharing clinical genomic data, the first and second ( (up to the third, if applicable) analysis pipeline should be mentioned.

The parameter setting of the pipeline should also be mentioned to confirm the reliability of the detection of mutations. NGS's data analysis pipeline can be divided into 4 main tasks: read sorting, post sort processing, mutation detection, and mutation annotation (e.g. GATK 3.5, CASAVA 1.7, Complete Genomics v2.2, Torrent Suite 5.0.2)

The derived data S106 of FIG. 1 may include FASTQ, SAM, BAM, CRAM, VCF, and MAF files. The sequencer stores the decoded sequence of NGS in FASTQ format or BAM file. The mutation detection or mutation annotation process in FASTQ or BAM consists of four steps: raw data generation, alignment, variant calling, and variant annotation. As a result of each step, the following six types of derived data files can be created. Other files and data may be added as the analysis method evolves. A detailed description of the derived data will be provided later.

2 is a diagram illustrating a configuration of a workflow for next-generation nucleotide sequence preprocessing according to an embodiment of the present invention.

Referring to FIG. 2 , the next-generation sequencing apparatus may perform read sequence alignment (S201).

In aligning the read sequence, preprocessing of aligning, sorting, and adding or replacing the read sequence may be performed.

Thereafter, the next-generation sequencing apparatus may perform a pre-processing process on the aligned read sequence (S202).

The next-generation sequencing apparatus may rearrange read sequences, perform nucleotide detection, and remove duplicated nucleotides during preprocessing.

Thereafter, the next-generation sequencing device may detect the mutation (S203).

In this process, it is possible to perform the operations of calling germline somatic mutations, copying mutation detection numbers, and detecting structural mutations.

Thereafter, the next-generation sequencing apparatus may perform mutation annotation (S204).

The next-generation sequencing device can match information including the type of mutation to the found mutation.

On the other hand, the operation described in FIG. 2 is only an embodiment of the present invention, and there is no limitation on its implementation as long as it is an operation of pre-processing the nucleotide sequence.

3 is a diagram illustrating a configuration of a workflow for parallel sequencing pre-processing according to an embodiment of the present invention.

According to an embodiment of the present invention, the read sort may consist of a read sort and a binary summary. From the read alignment algorithm to the final mutation detection process within the entire next-generation sequencing pipeline, many factors can influence mutation detection.

[Table 4] below summarizes the metric information related to the lead sorting 311 in a table.

category metric Explanation example options lead alignment process name Lead Sort Subprocess Name read alignment, sort essential tool name The name of the tool used in the lead alignment process BAM Select tool version Tool version used for lead alignment process v0.7.12 options tool options About options for tools used in the lead alignment process Number of threads: -t essential additional input Additional input information from the tool the user uses in the read alignment process, e.g. a genome with a release name GRCh38 essential Print Tool output used in the read alignment process SAM file options

[Table 5] below summarizes metric information related to the binary summary 312 in a table.

category metric Explanation example options Binary Summary tool name A set of utilities that use the SAMTOOLS tool and manipulate the sort in BAM format. SAMTOOLS essential tool version Using SAMTOOLS v1.9 v1.9 essential tool options Use -b for BAM format output. -S is required if input is SAM format. -b
-s options additional input options Print binary format BAM file options

According to an embodiment of the present invention, the BAM processing 320 may include sorting 321_, replacing or adding a read group 322 and indexing 323. [Table 6] below shows sorting 321 Relevant information is arranged in a table.

category metric Explanation example sorting tool name A set of utilities that use the SAMTOOLS tool and manipulate the sort in BAM format. SAMTOOLS tool version Using SAMTOOLS v1.9 v1.9 tool options Sort Algorithm Sort additional input Print Preprocessed BAM file BAM file

[Table 7] below is a table of information related to lead group replacement or addition (322).

category metric Explanation example Replace or add lead groups tool name I use the PICARD tool, which assigns all reads of the file to one new read group. PICARD tool version Using PICARD v1.93 v1.93 tool options RGID Lead Group ID DefaultRGLB Read Group Library Request
RGPU Lead Group Platform Unit Request
Request RGSM Lead Group Name - RGID
- RGLB
- RGPU
- RGSM additional input Print Preprocessed BAM file BAM file

[Table 8] below is a table of indexing 323 related information.

category metric Explanation example indexing tool name A set of utilities that use the SAMTOOLS tool and manipulate the sort in BAM format. SAMTOOLS tool version Using SAMTOOLS v1.9 v1.9 tool options Coordinate-sorted BAM file indexing for fast random access Index additional input Print Preprocessed BAM file BAM file

Local Realignment base quality score recalibration (330), a realigner target creator (331), an indel realigner (332), and a base recalibrator (a base recalibrator, 332) can be configured.

[Table 9] below is a table of information about the rebalancing target creator (331).

category metric Explanation example Authors for rebalancing tool name Using the GATK tool, sequencing data is used to activate all mutation calls in the genome. GATK tool version Using GATK 3.8 V3.8 tool options Input VCF file input with known indels -known additional input Reference genomic data and population-level data of known variants. Reference GenomeTGP
dbSNP Print Preprocessed BAM file BAM file

[Table 10] below is a table of information on indel readjustment (332).

category metric Explanation example Indel readjustment tool name Using the GATK tool, sequencing data is used to activate all mutation calls in the genome. GATK tool version Using GATK 3.8 V3.8 tool options Gap file output from reorderer target author target interval additional input Reference genomic data and population-level data of known variants. Reference GenomeTGP
dbSNP Print Preprocessed BAM file BAM file

[Table 11] below is a table of information related to base readjustment (332).

category metric Explanation example base readjustment tool name Using the GATK tool, sequencing data is used to activate all mutation calls in the genome. GATK tool version Using GATK 3.8 V3.8 tool options Database of known polymorphic sites
BQSR, generate quantization tables for fast quantization that subsequent tools will subsequently perform - Known sites

- BQSR additional input Reference genomic data and population-level data of known variants. Reference GenomeTGP
dbSNP Print Preprocessed BAM file BAM file

In the present invention, the mutation detection 360 may include germline mutation detection 361 and somatic mutation detection 362. [Table 12] below summarizes information on mutation detection in a table.

category metric Explanation example options Mutation detection process name Subprocess name of mutation detection Germline mutation calling essential tool name The name of the tool used to detect the mutation GATK HaplotypeCaller essential tool version Version of the tool used for mutation detection V3.8 essential tool options About options for tools used to detect mutations - T options additional input Additional input information from the tool the user uses to detect the mutation, for example, a genome with a release name. GRCh38 essential Print Tool output used for mutation detection VCF options

[Table 13] below summarizes information on mutation annotations in a table.

category metric Explanation example options mutation annotation process name Subprocess name in mutation annotation Variant annotations essential tool name The name of the tool used to annotate the mutation. VarAFT essential tool version Tool version used for mutation annotation V 2.16 essential tool options About options for tools used for mutation annotations options additional input Additional input information from the tool the user uses to annotate the variant, for example, a genome with a release name. essential Print Tool output used for mutation annotations MAF options

[Table 14] below summarizes information on target intersection (340) in a table.

category metric Explanation example target intersection tool name Cross genomic gaps in multiple files in different genomic file formats using the Bed tool Bedtools tool version Using Bedtools v2.26 v2.26 tool options - b bed file allocation - b additional input Target bed file containing information about fusion breakpoints, expression control, and target analysis target bed file Print Preprocessed BAM file BAM file

[Table 15] below is a table of information on remove duplication (350).

category metric Explanation example Remove duplicates tool name I use the PICARD tool, which assigns all reads of the file to one new read group. PICARD tool version Using PICARD v1.93 v1.93 tool options METRICS_FILE, allocates a file to write duplicate metrics to REMOVE_DUPLICATES, determines whether to remove flagged reads
CREATE_INDEX, determines whether to create a BAM index when creating a BAM file sorted by coordinates METRICS_FILE
REMOVE_DUPLICATES
CREATE_INDEX additional input Print Preprocessed BAM file BAM file

In the present invention, variant calling (360) may consist of germline mutation calling (361) and somatic mutation calling (362). [Table 16] below shows germline mutation detection ( 361) is arranged in a table.

category metric Explanation example Germline mutation detection tool name Using the GATK HaplotypeCaller tool, germline SNP and indel caller. GATK HaplotypeCaller tool version Using GATK v3.8 V3.8 tool options -T Specifies the temporary directory to use -R Allocates a reference sequence file
- Allocate BAM file containing read I
-O allocate the file the variant should be written to - T
- R
- I
- O additional input human reference genome reference genome Print VCF file, containing the individual variant call information and quality scores mentioned above for each sample. VCF file

[Table 17] below summarizes information on somatic mutation detection 362 in a table.

category metric Explanation example Somatic mutation detection tool name It uses the VarScan tool and is a somatic SNP and indel caller. VarScan tool version Using VarScan v2.3.9 V2.3.9 tool options mpileup2snp, detect SNPs in mpileup files based on user-defined parameters mpileup2snp additional input human reference genome reference genome Print VCF file, containing the individual variant call information and quality scores mentioned above for each sample. VCF file

[Table 18] below is a table of information on somatic mutation detection (362).

category metric Explanation example Somatic mutation detection tool name It uses the MuTect2 tool and is a somatic SNP and indel caller. MuTect2 tool version tool options Enable Artifact Detection to Create Normal Panels artifact_detection_Mode additional input human reference genome
Adjusting the threshold for evidence of variation from normal using COSMIC data with dbSNP reference genome

dbSNP
COSMIC Print VCF file, containing the individual variant call information and quality scores mentioned above for each sample VCF file

[Table 19] below summarizes information on copy number variant calling (363) into a table.

category metric Explanation example Copy number variation detection tool name Detect genome-wide copy number variations and alterations through high-throughput sequencing using the CNVkit tool. CNVkit tool version Using CNVkit v0.9.6 v0.9.6 tool options batch -t -f --access --output-reference --output-dir --drop-low-coverage additional input human reference genome reference genome
access-5k mappable (cnvkit optional) Print SNV Allele Count File text file (tab delimiter)

[Table 20] below summarizes information on structure variant calling (364) into a table.

category metric Explanation example Structural transformation calls tool name Using the Lumpy tool, a probabilistic framework for structural deformation discovery Lumpy tool version Using Lumpy v0.2.14 v0.2.14 tool options Lumpyexpress Provides Automated Breakpoint Detection for Standard Analysis-S Coordinate Alignment Divider BAM File
-D provide mismatched BAM file
-B coordinate alignment BAM file allocation
-r assign trim threshold
-o assign output
-m assign minimum sample weight to invocation lumpyexpress
-S
-B
-D
-r
-o
-m additional input human reference genome reference genome Print VCF file, containing the individual variant call information and quality scores mentioned above for each sample VCF file

The derived data S106 of FIG. 1 may include FASTQ, SAM, BAM, CRAM, VCF, and MAF files. The sequencer stores the decoded sequence of NGS in FASTQ format or BAM file.

The mutation detection or mutation annotation process in FASTQ or BAM consists of four steps: raw data generation, alignment, variant calling, and variant annotation.

As a result of each step, the following six types of derived data files can be created. Other files and data may be added as the analysis method evolves.

In the present invention, FASTQ is a text-based format that stores both a biological sequence or a nucleotide sequence and a corresponding quality score (QC).

Both the sequence letter and quality score (QC) are encoded as a single ASCII character for brevity.

In the present invention, a sequence alignment map (SAM) is a text-based format for storing biological sequences aligned to a reference sequence.

In the present invention, BAM is comprehensive raw data of a genome sequence, and is composed of a compressed binary representation without loss of information in SAM.

In the present invention, CRAM is a compressed columnar file format for storing biological sequences according to reference sequences.

In the present invention, VCF is a format of a text file used to store gene sequence variations in bioinformatics.

In the present invention, MAF is the format of a text file used for annotated mutations in bioinformatics.

[Table 21] below is a table of information related to derived data.

category name Explanation options derived data FASTQ options SAM options BAM options CRAM options VCF essential MAF options

Hereinafter, the characteristics of the system viewed from the user's point of view will be described through various embodiments. That is, a set of scenarios initiated by a person, the passage of time, or another system can be described. That is, the present invention relates to a service or function provided by a system, a functional device from the user's point of view that the system provides to a user, and a user's request. In response, you may modify the desired course or provide information.

The present invention can also constitute system actions of related bundles, important self-contained services, through one or more contact with the user.

These behaviors need to be defined from the user's point of view.

In the present invention, the person concerned will play a key role in pathological diagnosis and examination and others in anatomic, surgical pathology/hematopathology, and cancer data collection.

In the present invention, individual patients will be composed of ordinary people who use health care and wellbeing services.

4 is a diagram illustrating a next-generation sequencing process in clinical practice, according to an embodiment of the present invention.

As shown in FIG. 4, in clinical sequencing by next-generation sequencing analysis, integration of patients, healthcare providers, laboratories, and geneticists/medical geneticists/molecular pathologists is required. may be included. It is done at the discretion of the clinician when necessary to treat patients accurately and effectively. After the healthcare provider collects the sample, the laboratory receives, processes, and sequence the sample. After the data is analyzed and prepared for processing, it is passed on to a geneticist/medical geneticist/molecular pathologist who frequently works in the laboratory. Here, data is transformed for interpretation and organized into reports. The report is then added to the patient's Electronic Health Records (HER) so that the health care provider can view the report and develop a patient care plan.

As shown in FIG. 4 , the patient visits the hospital. The hospital analyzes the patient's condition through next-generation sequencing and determines the clinical course and treatment based on the following conditions. All medical information about the patient will be included in the EHR with the patient's consent.

Next, clinical treatment and diagnosis are performed on the visited patient (S401, S402, S403).

Here, hospital diagnosis is a process of explaining a patient's symptoms and signs as a disease or disease using the hospital's examination and equipment. In particular, a simple biopsy may be required to diagnose a malignant tumor in cancer patients. There are numerous types of biopsies. Almost all biopsies use sharp instruments to remove small amounts of tissue.

Most biopsies are needle biopsy.

The use of a needle to access the patient's suspicious tissue. However, since needle biopsy does not cover a wide range of lesions, a surgical biopsy is also required to obtain a sample for sequencing. Although this biopsy is convenient because it can be tested with a simple blood sample, the amount of CTC in the blood is too small, so an accurate detection technique cannot be widely used.

Next, a sample may be obtained by performing a clinical process (S403). Samples must be taken from the patient's organs for next-generation sequencing. Typically, this procedure is performed in a hospital as a surgical biopsy.

Biopsies are most frequently performed to identify cancer and obtain samples for next-generation sequencing. Surgical biopsies and blood tests are most commonly used to take samples of cancerous tissue and blood. This sample is sent to a wet laboratory for sequencing.

Next, the laboratory prepares a library (S404). In library preparation for next-generation sequencing, DNA fragments of a specific size are randomly collected using an enzymatic reaction that targets nucleic acids to obtain high-throughput sequencing.

Library preparation kits for next-generation sequencing ensure consistency and reproducibility for standard molecular biological reactions. Library preparation for next-generation sequencing also includes DNA fragmentation for sequencing. Libraries for next-generation sequencing are generally prepared by dividing a genomic DNA or cDNA sample into fragments and attaching special adapters to the ends of the fragments.

This process may be defined as "tagmentation".

This process combines fragmentation and ligation reactions in one step. Fragments designated as adapters are PCR-amplified and gel-purified.

Next, a sequencing step is performed in the laboratory (S405). Exome is a term used to describe any part of the genome made up of exons.

An exon, as opposed to an intron, is a DNA region that can be changed into messenger RNA by a splicing protein. Genome sequencing is a capture-based method developed to identify mutations in the coding region of genes that affect protein function. Although there exist methods for capturing the genome using PCR, hybrid capture, and molecular inversion probes exist, the most common and efficient strategy is the in-solution capture method. In-solution capture utilizes a pool or probe of oligonucleotides bound to magnetic beads, and the probe's nucleotide sequence is designed to mix up to the Exon region. After binding to genomic DNA, these probes can be pulled down and washed to selectively align exon regions.

Next, next-generation sequencing is performed (S406).

Files of sequence reads obtained from sequencing are saved in FASTQ format. This file format contains the nucleotide sequence and quality of each nucleotide. These files are subjected to quality checks to evaluate sequencing.

The sequences are then aligned to a reference sequence to determine the location of each sequence read. Afterwards, local realignment and basic quality score re-measurement are performed to minimize technical deficiencies in sequencing. Finally, duplicate reads are removed, and disparity detection is performed by considering the total disparity amount for each read depth. Next-generation sequencing result data is stored in the VCF. This file simply contains the chromosomal location and allele information of the mutations recognized during the mutation detection process. However, in order to interpret it clinically, it is necessary to annotate various information that has biological and clinical relevance. We need information about which mutations are present in which genes, how amino acids are changed by somatic mutations, how mutations are conserved, how much protein structure is altered, and which diseases are associated with mutations. All this information must be annotated to enable clinical interpretation.

Thereafter, in turn, the geneticist interprets the genetic/genomic results for clinical results (S407), and the geneticist prepares a nucleotide sequence analysis report (S408). Here, the report of next-generation sequencing is prepared including narrative finding and interpretation.

Next, the clinician who is a health care provider reviews the next-generation sequencing result/report in order to design a treatment plan for the patient (S409).

Next, the healthcare provider establishes or modifies a treatment plan in consideration of the next-generation sequencing of the healthcare provider ( S410 ).

Next, the clinician discusses the next-generation sequencing report and treatment plan with the patient (S411).

5 is a diagram illustrating a patient-centered health information exchange process using a parallel sequencing file according to an embodiment of the present invention.

Patient-centric, massively parallel sequencing-based health information exchange may require patients from both hospitals, health care providers, laboratory and geneticists, medical geneticists, and molecular pathologists all. A process from a single hospital visit to diagnosis, testing, and result acquisition may correspond to that shown in FIG. 4 . However, "patient transfer preparation", which is the process ( S512 ) in which the patient is transferred to another hospital, may differ depending on the scenario as shown in FIG. 5 .

Meanwhile, the operations described with reference to FIGS. 4 and 5 are only an embodiment of the present invention, and there is no limitation thereto.

6 is a view showing the overall operation of the next-generation nucleotide sequence analysis method according to an embodiment of the present invention.

Referring to FIG. 6 , the next-generation sequencing apparatus may collect, process, and store a sample ( S601 ).

Thereafter, the next-generation sequencing apparatus may extract DNA from the sample (S602). In addition, the next-generation sequencing apparatus may perform DNA processing and form a library based on the extracted DNA (S603).

Subsequently, the nucleotide sequence analysis apparatus may perform sequencing of the sample to obtain a read file of the sample included in the library ( S604 ). Subsequently, base calling may be performed from the sequenced read file.

The next-generation sequencing apparatus may align and map the read file (S605).

Subsequently, variant calling may be performed on the read file from the pre-processed read file ( S606 ).

In addition, the next-generation sequencing apparatus may perform annotation and filtering on the mutation (S607).

In addition, the next-generation sequencing device may determine the mutation (S608).

Thereafter, the next-generation sequencing apparatus may create a report based on the information derived based on the above-described operation (S609).

Various embodiments of the present invention may be implemented as software including one or more instructions stored in a storage medium (eg, memory) readable by a machine. For example, the processor of the device may call at least one of the one or more instructions stored from the storage medium and execute it. This makes it possible for the device to be operated to perform at least one function according to the called at least one instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, the 'non-transitory storage medium' is a tangible device and only means that it does not include a signal (eg, electromagnetic wave), and this term means that data is semi-permanently stored in the storage medium. and temporary storage. For example, the 'non-transitory storage medium' may include a buffer in which data is temporarily stored.

According to one embodiment, the method according to various embodiments disclosed herein may be provided as included in a computer program product. Computer program products may be traded between sellers and buyers as commodities. The computer program product is distributed in the form of a device-readable storage medium (eg compact disc read only memory (CD-ROM)), or via an application store (eg Play Store™) or two user devices. It can be distributed (eg downloaded or uploaded) directly or online between devices (eg smartphones). In the case of online distribution, at least a portion of the computer program product (eg, a downloadable app) is stored at least on a machine-readable storage medium, such as a memory of a manufacturer's server, a server of an application store, or a relay server. It may be temporarily stored or temporarily created. In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those of ordinary skill in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing the technical spirit or essential features thereof. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (16)

In the nucleotide sequence analysis method,
performing a diagnosis, including clinical treatment and biopsy, on the patient;
obtaining a sample for next-generation sequencing through the clinical process;
preparing a library for the next-generation sequencing;
performing sequencing of the sample to obtain a read sequence of the sample included in the library;
performing base calling from the sequenced read sequence;
aligning the read sequences;
pre-processing the aligned read sequences;
performing variant calling on the read file from the preprocessed read sequence; and
Forming genetic data including the patient, the sample, the laboratory equipment used for the sample experiment, the analysis equipment used for the sample analysis, and data derived from the sample;
Next-generation sequencing method. The method of claim 1,
The step of forming the genetic data is,
based on the patient information including at least one of the patient's name, the patient's unique identifier, the patient's date of birth, the patient's gender, the patient's race, the patient's diagnostic information, and the patient's treatment information A next-generation nucleotide sequence analysis method comprising; forming genetic data. According to claim 1,
The step of forming the genetic data is,
and forming the genetic data based on the sample information including at least one of an institution for obtaining the sample, the date the sample was sampled, and the type of the sample. According to claim 1,
The step of forming the genetic data is,
Data quality control index of the experimental equipment used for the sample experiment, base calling information of the experimental equipment, the depth of the read sequence, the depth of the reference allele of the read sequence, the replacement allele of the read sequence Depth, allele frequency of the read sequence, genotype of the read sequence, sequencing platform information of the read sequence, sequencer type of the read sequence, library preparation technique of the read sequence, target capture method of the read sequence, the read sequence A next-generation nucleotide sequence analysis method comprising a; According to claim 1,
The step of forming the genetic data is,
aligning the read sequence, correcting the aligned read sequence, detecting a genetic mutation from the corrected read sequence, and matching an annotation to the detected genetic mutation to form the analysis equipment information,
A next-generation nucleotide sequence analysis method comprising a; forming the genetic data based on the analysis equipment information. According to claim 1,
The step of forming the genetic data is,
A next-generation sequencing method comprising a; forming the gene data based on the derived data including at least one of FASTQ, gene sequence alignment map, BAM, CRAM, VCF, and MAF including gene sequence and sequence quality score . According to claim 1,
The step of sequencing the sample to obtain a read file of the sample included in the library includes:
A next-generation sequencing method comprising a; extracting and selectively arranging an exon region from the read file using one of a plurality of predetermined methods. In an apparatus for performing next-generation sequencing,
Memory; and
Including; at least one processor to communicate with the memory;
the at least one processor,
performing diagnostics, including clinical treatment and biopsies, on the patient;
Obtaining a sample for next-generation sequencing through the clinical treatment,
Prepare a library for the next-generation sequencing,
performing sequencing of the sample to obtain a read file of the sample included in the library;
Perform base calling from the sequenced read file,
sort the lead file;
pre-processing the aligned read file;
performing variant calling on the read file from the preprocessed read file;
A next-generation sequencing device for generating genetic data including the patient, the sample, the laboratory equipment used for the sample experiment, the analysis equipment used for the sample analysis, and data derived from the sample. 9. The method of claim 8,
the at least one processor,
based on the patient information including at least one of the patient's name, the patient's unique identifier, the patient's date of birth, the patient's gender, the patient's race, the patient's diagnostic information, and the patient's treatment information A next-generation sequencing device that forms genetic data. 9. The method of claim 8,
the at least one processor,
A next-generation sequencing apparatus for forming the genetic data based on the sample information including at least one of an institution for obtaining the sample, a date the sample was sampled, and a type of the sample. 9. The method of claim 8,
the at least one processor,
Data quality control index of the experimental equipment used for the sample experiment, base calling information of the experimental equipment, the depth of the read sequence, the depth of the reference allele of the read sequence, the replacement allele of the read sequence Depth, allele frequency of the read sequence, genotype of the read sequence, sequencing platform information of the read sequence, sequencer type of the read sequence, library preparation technique of the read sequence, target capture method of the read sequence, the read sequence A next-generation sequencing device for forming the genetic data based on the experimental equipment information including at least one of the type of the read sequence and the length of the read sequence. 9. The method of claim 8,
the at least one processor,
aligning the read sequence, correcting the aligned read sequence, detecting a genetic mutation from the corrected read sequence, and matching an annotation to the detected genetic mutation to form the analysis equipment information,
A next-generation nucleotide sequence analysis device that forms the genetic data based on the analysis equipment information. 9. The method of claim 8,
the at least one processor,
the genetic data based on derived data comprising at least one of a FASTQ comprising a gene sequence and a sequence quality score, a gene sequence alignment map (SAM), a binary alignment map (BAM), CRAM, VCF, MAF; Next-generation sequencing device to form. 9. The method of claim 8,
the at least one processor,
A next-generation sequencing apparatus for selectively arranging exon regions in the read file using one of a plurality of predetermined methods.
In combination with the computer, which is hardware,
performing a diagnosis, including clinical treatment and biopsy, on the patient;
obtaining a sample for next-generation sequencing through the clinical process;
preparing a library for the next-generation sequencing;
performing sequencing of the sample to obtain a read sequence of the sample included in the library;
performing base calling from the sequenced read sequence;
aligning the read sequences;
pre-processing the aligned read sequences;
performing variant calling on the read file from the preprocessed read sequence; and
The next-generation nucleotide sequence stored in the medium to execute; forming genetic data including the patient, the sample, the laboratory equipment used for the sample experiment, the analysis equipment used for the sample analysis, and data derived from the sample analysis program. 16. The method of claim 15,
The genetic data is
the patient information including at least one of the patient's name, the patient's unique identifier, the patient's date of birth, the patient's gender, the patient's race, the patient's diagnostic information, and the patient's treatment information,
and the sample information including at least one of an institution acquiring the sample, a date the sample was sampled, and a type of the sample,
Data quality control index of the experimental equipment used for the sample experiment, base calling information of the experimental equipment, the depth of the read sequence, the depth of the reference allele of the read sequence, the replacement allele of the read sequence Depth, allele frequency of the read sequence, genotype of the read sequence, sequencing platform information of the read sequence, sequencer type of the read sequence, library preparation technique of the read sequence, target capture method of the read sequence, the read sequence and the experimental equipment information including at least one of the type of the read sequence and the length of the read sequence,
Aligning the read sequence, correcting the aligned read sequence, detecting a genetic mutation from the corrected read sequence, and matching the annotation to the detected genetic mutation to include the analysis equipment information,
Next-generation nucleotide sequence comprising at least one of FASTQ including gene sequence and sequence quality score, the gene sequence alignment map (SAM), binary alignment map (BAM), CRAM, VCF, and MAF analysis program.

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