CN104408284A - Integration algorithm of sequencing data analysis workflow of cancer somatic mutation gene - Google Patents
Integration algorithm of sequencing data analysis workflow of cancer somatic mutation gene Download PDFInfo
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- CN104408284A CN104408284A CN201410571652.0A CN201410571652A CN104408284A CN 104408284 A CN104408284 A CN 104408284A CN 201410571652 A CN201410571652 A CN 201410571652A CN 104408284 A CN104408284 A CN 104408284A
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- 206010028980 Neoplasm Diseases 0.000 title claims abstract description 26
- 238000012163 sequencing technique Methods 0.000 title claims abstract description 25
- 201000011510 cancer Diseases 0.000 title claims abstract description 24
- 206010069754 Acquired gene mutation Diseases 0.000 title claims abstract description 21
- 230000037439 somatic mutation Effects 0.000 title claims abstract description 21
- 230000010354 integration Effects 0.000 title claims abstract description 19
- 108090000623 proteins and genes Proteins 0.000 title claims abstract description 17
- 238000007405 data analysis Methods 0.000 title abstract description 10
- 238000012797 qualification Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 2
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- 241001223864 Sphyraena barracuda Species 0.000 description 2
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- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000013399 early diagnosis Methods 0.000 description 1
- 238000012165 high-throughput sequencing Methods 0.000 description 1
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Abstract
The invention relates to an integration algorithm of sequencing data analysis workflow of a cancer somatic mutation gene. The integration algorithm comprises the following steps that (1) comparison of sequencing data utilizes a cushaw algorithm; (2) authentication of SNP (Single Nucleotide Polymorphism) utilizes a samtools algorithm; and (3) authentication of cancer somatic mutation utilizes a VarScan algorithm.
Description
Technical field
The present invention relates to biomedical data analysis field, in particular to a kind of cancer somatic mutation gene sequencing data analysis workflow integration algorithm.
Background technology
Gene is physical basis of heredity.The all life phenomenons such as the birth and old age, sickness and death of biosome are all relevant with gene.Gene sequencing understands a kind of approach of life, along with the development of the second generation and third generation high throughput sequencing technologies, sequencing result often TB rank even larger sequence data.Reasonable analysis understands these extensive and high-dimensional data to be become and to obtain after data a larger difficult point, is the committed step of current biological research, has huge realistic meaning.
The storage of magnanimity high-flux sequence data, process and analysis are all greatly challenged current department of computer science and to be unified computation schema.Existing systems face operand is inadequate, and manual intervention fiduciary level is lower, and cloud framework is the problem such as lower and privacy of user worry to bottom hardware control.
The comprehensive coordinate that the challenge of existing large data order-checking information to data analysis tool needs storage, management, transmission, scheduling and computational analysis to optimize, need the close fit in many ways such as biological field, computer realm, data statistic analysis, especially in the integration of analysis tool, it is low to there is degree of integration in existing data analysis software, poor to the Data Matching of separate sources, accuracy and repeatability are not high, the various problems such as inefficiency.
In lesion detection and early diagnosis, cancer somatic mutation is that order-checking detects institute's issues that need special attention, this requirement can be tried one's best and efficiently and accurately be analyzed order-checking raw data, but existing algorithm often only pays close attention to the single link analyzed with sequencing data, and the range of choice of analysis software too numerous and diverse in each step, also give and show that diagnostic result adds obstacle by order-checking raw data
Such as, in sequencing data comparison link, conventional algorithm comprises bwa, bowtie, cushaw, barracuda, arithmetic speed is different, adaptive underlying hardware is also different, and wherein cushaw is high performance computation card specialized designs, can reach the object that parallel computation is carried out accelerating, and bwa, bowtie, although barracuda does not possess parallel computation function, bottom data and computing hardware that can be adaptive be then comparatively wide in range.
And SNP qualification (main realize by sequencing result comparison to genome and identify the function of related mutation) link, popular software mainly comprises: samtools, GATK, Qcall etc., what these softwares had lays particular stress on accuracy, and what have lays particular stress on efficiency.
In cancer somatic mutation (mainly through more same patient normal and cancer tissue, and identify the somatic mutation occurred in cancer thus) in qualification process, main software comprises: VarScan, GATK UnifiedGenotyper, VarScan scheduling algorithm, have to bias toward accuracy in detection high, some universality data compatibilities, some input and output standards are relatively simple.
Therefore, need a kind of cancer somatic mutation gene sequencing data analysis workflow integration algorithm, to a certain extent above-mentioned algorithm optimization is integrated, detect the object of cancer somatic mutation gene to reach accurate efficiently.
Summary of the invention
The object of this invention is to provide a kind of cancer somatic mutation gene sequencing data analysis workflow integration algorithm.
Described cancer somatic mutation gene sequencing is data from the sequenator of the Hiseq series of illumina company, or PGM and the Proton series sequenator of ThermoFisher company, sequencing data rank is Mb ~ Gb size, foundation length 10 ~ 1000bp, data layout is FastQ or SFF form.
Described cancer somatic mutation gene sequencing analyzes datamation stream exemplary flow as accompanying drawing 1,
Described cancer somatic mutation gene sequencing is analyzed datamation stream integration algorithm and is comprised the steps:
(1) by sequencing data comparison to reference to genome sequence, use cushaw algorithm, after using scientific calculation stream handle to accelerate, comparing speed is increased to the 10-100 of other softwares doubly;
(2) SNP qualification, use samtools algorithm, compatible several data form, accuracy is high, and location is fast;
(3) cancer somatic mutation qualification, use VarScan algorithm, compatible data kind is many, and accuracy is high, and input and output meet Open Standard;
Through above-mentioned workflow integration method, can and cancer tissue sequencing result normal by more same patient fast, thus identify the somatic mutation occurred in cancer.
Described workflow integration algorithm is GPU algorithm, utilizes the high-speed floating point of GPU to calculate and parallel characteristics, significantly can improve the arithmetic speed of algorithm, reach hardware-accelerated effect.
In described workflow integration algorithm, design have matched the software using GPU to accelerate further, improves workflow efficiency by hardware-accelerated.
The invention has the advantages that: the complete job stream that this Algorithms Integration sequencing data is analyzed, data analysis user can be removed from from row filter and optimize the step of specific program/procedure set, algorithm after simultaneously optimizing and combining greatly improves on operation efficiency, can feed back sequencing data analysis result more rapidly.
Accompanying drawing explanation
Fig. 1. cancer somatic mutation gene sequencing analyzes datamation stream schematic flow diagram.
Embodiment
Below in conjunction with specific embodiment, the invention will be further described, but should not limit the scope of the invention with this.
Raw data derives from Illumina Hiseq 2000, and data layout is FastQ, and reading long is 100bp.
By workflow analysis,
(1) comparison result that checks order exports: wherein normal structure data count is 233988 records, article 222290, (95.3%) comparison is in genome, cancer tissue data count is 200549, and 188516 (94%) comparisons are on genome
(2) samtools is used to identify SNP;
(3) by comparing the SNP difference site of normal combination and tumor tissues, the distinctive site 12 of tumor tissues is accredited as.
This workflow integral operation time is 95s.
Claims (3)
1. cancer somatic mutation gene sequencing analyzes datamation stream integration algorithm, it is characterized in that, comprises the steps:
(1) by sequencing data comparison to reference to genome sequence, use bwa algorithm, after using scientific calculation stream handle to accelerate, comparing speed is increased to the 10-100 of other softwares doubly;
(2) SNP qualification, use samtools algorithm, compatible several data form, accuracy is high, and location is fast;
(3) cancer somatic mutation qualification, use VarScan algorithm, compatible data kind is many, and accuracy is high, and input and output meet Open Standard.
2. cancer somatic mutation gene sequencing analyzes datamation stream integration algorithm according to claim 1, and it is characterized in that, described workflow integration algorithm is GPU algorithm, utilizes the calculating of the high-speed floating point of GPU and parallel characteristics to reach hardware-accelerated effect.
3. cancer somatic mutation gene sequencing analyzes datamation stream integration algorithm according to claim 1, it is characterized in that, in described workflow integration algorithm, comprises the software using GPU to accelerate further.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391965A (en) * | 2017-08-15 | 2017-11-24 | 上海派森诺生物科技股份有限公司 | A kind of lung cancer somatic mutation determination method based on high throughput sequencing technologies |
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| WO1998053300A2 (en) * | 1997-05-23 | 1998-11-26 | Lynx Therapeutics, Inc. | System and apparaus for sequential processing of analytes |
| TW201101081A (en) * | 2009-06-30 | 2011-01-01 | Univ Ishou | Analysis comparison system for SNP mutation information sequence |
| CN104059966A (en) * | 2014-05-20 | 2014-09-24 | 吴松 | STAG2 gene mutant sequence and detection method thereof as well as use of STAG2 gene mutation in detecting bladder cancer |
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- 2014-10-24 CN CN201410571652.0A patent/CN104408284A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1998053300A2 (en) * | 1997-05-23 | 1998-11-26 | Lynx Therapeutics, Inc. | System and apparaus for sequential processing of analytes |
| TW201101081A (en) * | 2009-06-30 | 2011-01-01 | Univ Ishou | Analysis comparison system for SNP mutation information sequence |
| CN104059966A (en) * | 2014-05-20 | 2014-09-24 | 吴松 | STAG2 gene mutant sequence and detection method thereof as well as use of STAG2 gene mutation in detecting bladder cancer |
Non-Patent Citations (1)
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| 陈辰: "基于二代测序技术的结直肠癌中Wnt通路相关基因的indel检测与细胞功能研究"", 《中国博士学位论文全文数据库》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391965A (en) * | 2017-08-15 | 2017-11-24 | 上海派森诺生物科技股份有限公司 | A kind of lung cancer somatic mutation determination method based on high throughput sequencing technologies |
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