高通量DNA测序数据的生物信息学方法

doi:10.11959/j.issn.2096-0271.2016021

摘要/Abstract

摘要：

高通量测序技术产生的DNA序列数据长度较短，而且数据量非常巨大。分析了高通量测序环境下大数据的挑战和机遇，总结并讨论了数据压缩、宏基因组数据序列拼接、宏基因组数据序列分析方面的算法和工具等研究成果。最后，展望了高通量测序下DNA短读序列数据研究的发展趋势。

关键词: 高通量DNA测序, 生物信息学, 短读序列数据压缩, 短读序列数据拼接, 短读序列数据分析

Abstract:

DNA sequence data generated by high-throughput sequencing technology is short in length, and the amount of data is enormous. The challenges and opportunities of the big data in high-throughput sequencing environment were analyzed. The data compression, the assembly of metagenomic sequence data, and algorithms and tools of metagenomic sequence data analysis also were summarized and discussed. Finally, the future of the study on short read DNA sequence data in high-throughput sequencing environment was discussed.

Key words: high-throughput DNA sequencing, bioinformatics, short read sequence data compression, short read sequence data splicing, short read sequence data analysis

中图分类号:

TP399

詹晓娟,姚登举,朱怀球. 高通量DNA测序数据的生物信息学方法[J]. 大数据, 2016, 2(2): 76-87.

xiaojuan Zhan,dengju Yao,huaiqiu Zhu. Bioinformatics methods for high-throughput DNA sequencing data[J]. Big Data Research, 2016, 2(2): 76-87.

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参考文献 56

[1]	SCHUSTER S C . Next-generation sequencing transforms today’s biology[J]. Nature Methods, 2008,5(1): 16-18.
[2]	SANGER F , NICKLEN S , COULSON A R . . DNA sequencing with chain-terminating inhibitors[J]. Proceeding of the National Academy of Sciences, 1977,B7(12): 5463-5467.
[3]	SHENDURE J , JI H . Next-generation DNA sequencing[J]. Nature Biotechnology, 2008,26(10): 1135-1145.
[4]	HIGGINS G . Human Genomes and Big Data Challenges[R]. Mason: AssureRx Health Inc, 2013.
[5]	WARD R M , SCHMIEDER R , HIGHNAM G , et al. Big data challenges and opportunities in highthrough-put sequencing[J]. Systems Biomedicine, 2013,1(1): 29-34.
[6]	DUNHAM I , BIRNEY E , LAJOIE B R , et al. An integrated encyclopedia of DNA elements in the human genome[J]. Nature, 2012,489(7414): 57-74.
[7]	COLLINS F S , BARKER A D . Mapping the cancer genome[J]. Scientific American, 2007,296(3): 50-57.
[8]	HAYDEN E C . International genome project launched[J]. Nature, 2008,451(7177): 378-389.
[9]	GEVERS D , KNIGHT R , PETROSINO J F , et al. The human microbiome project:a community resource for the healthy human microbiome[J]. PLoS Biology, 2012,10(8):e1001377.
[10]	HAUSSLER D , O’BRIEN S J , RYDER O A , et al. Genome 10K: a proposal to obtain whole-genome sequence for 10 000 vertebrate species[J]. The Journal of Heredity, 2008,100(6): 659-674.
[11]	O’ROAK B J , VIVES L , GIRIRAJAN S , et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations[J]. Nature, 2012,485(7397): 246-250.
[12]	EHRLICH S D . MetaHIT: the European union project on metagenomics of the human intestinal tract[M]// Metagenomics of the Human Body. New York: Springer, 2011: 307-316.
[13]	LEGRAIN P , AEBERSOLD R , ARCHAKOV A , et al. The human proteome project: current state and future direction[J]. Molecular & Cellular Proteomics, 2011,10(7):M111. 009993.
[14]	GILBERT J A , MEYER F , ANTONOPOULOS D , et al. Meeting report: the terabase metagenomics workshop and the vision of an earth microbiome project[J]. Standards in Genomic Sciences, 2010,3(3): 243.
[15]	ROBINSON G E , HACKETT K J , PURCELL M M , et al. Creating a buzz about insect genomes[J]. Science, 2011,331(6023): 1386.
[16]	JOLY Y , DOVE E S , KNOPPERS B M , et al. Data sharing in the post-genomic world: the experience of the international cancer genome consortium (ICGC) data access compliance office (DACO)[J]. PLoS Comput Biol, 2012,8(7):e1002549.
[17]	WU X D , ZHU X Q . Data mining with big data[J]. IEEE Transactions on Knowledge and Data Engineering, 2014,26(1): 97-108.
[18]	CHRISTLEY S , LU Y , LI C , et al. Human genomes as email attachments[J]. Bioinformatics, 2009,25(2): 274-275.
[19]	BRADON M C , WALLACE D C , BALDI P , et al. Data structures and compression algorithms for genomic sequence data[J]. Bioinformatics, 2009,25(14): 1731-1738.
[20]	KOZANITIS C , SAUNDERS C , KRUGLYAK S , et al. Compressing genomic sequence fragments using SlimGene[J]. Journal of Computational Biology, 2011,18(3): 401-413.
[21]	WANG C , ZHANG D . A novel compression tool for efficient storage of genome resequencing data[J]. Nucleic Acids Research, 2011,39(7): e45.
[22]	FRITZ M H Y , LEINONEN R , COCHRANE G , et al. Efficient storage of high throughput DNA sequencing data using reference-based compression[J]. Genome Research, 2011,21(5): 734-740.
[23]	MILLER J R , KOREN S , SUTTON G , et al. Assembly algorithms for next-generation sequencing data[J]. Genomics, 2010,95(6): 315-327.
[24]	BONFIELD J K , MAHONEY M V . Compression of FASTQ and SAM format sequencing data[J]. Plos One, 2013,8(3): 1453-1456.
[25]	COX A J , BAUER M J , JAKOBI T , et al. Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform[J]. Bioinformatics, 2012,28(11): 1415-1419.
[26]	HACH F , NUMANAGI? I , ALKAN C , et al. SCALCE: boosting sequence compression algorithms using locally consistent encoding[J]. Bioinformatics, 2012,28(23): 3051-3057.
[27]	SELVA J J , CHEN X . SRComp: short read sequence compression using burstsort and Elias omega coding[J]. PloS One, 2013,8(12): e81414.
[28]	PATRO R , KINGSFORD C . Data-dependent bucketing improves reference-free compression of sequencing reads[J]. Bioinformatics, 2015:btv248.
[29]	JONES D C , RUZZO W L , PENG X , et al. Compression of next-generation sequencing reads aided by highly efficient de novo assembly[J]. Nucleic Acids Research, 2012,40(22): e171.
[30]	METZKER M L . Applications of next-generation sequencing technologies the next generation[J]. Nature Reviews Genetics, 2010,11(1): 31-46.
[31]	WOOLEY C , GODZIK A , FRIEDBERG I . . A primer on metagenomics[J]. PLoS Comput Biol, 2010,6(2):e1000667.
[32]	POP M , PHILLIPPY A , DELCHER A L , et al. Comparative genome assembly[J]. Briefings in Bioinformatics, 2004,5(3): 237-248.
[33]	KECECIOGLU J , JU J . Separating repeats in DNA sequence assembly[C]// The 5th Annual International Conference on Computational Biology, April 22-25,2001, Montreal, Canada. [S.l.:s.n.], 2001: 176-183.
[34]	PRIDE D T , MEINERSMANN R J , WASSENAAR T M , et al. Evolutionary implications of microbial genome tetranucleotide frequency biases[J]. Genome Research, 2003,13(2): 145-158.
[35]	WU Y W , YE Y . A novel abundance-based algorithm for binning metagenomic sequences using l-tuples[J]. Journal of Computational Biology, 2011,18(3): 523-534.
[36]	PRAKASH T , TAYLOR T D . Functional assignment of metagenomic data:challenges and applications[J]. Briefings in Bioinformatics, 2012,13(6): 711-727.
[37]	QIN J , LI R , RAES J , et al. A human gut microbial gene catalogue established by metagenomic sequencing[J]. Nature, 2010,464(7285): 59-65.
[38]	QIN J , LI Y , CAI Z , et al. A metagenome-wide association study of gut microbiota in type 2 diabetes[J]. Nature, 2012,490(7418): 55-60.
[39]	BORODOVSKY M , MCININCH J . GENMARK: parallel gene recognition for both DNA strands[J]. Computers &Chemistry, 1993,17(2): 123-133.
[40]	LUKASHIN A , BORODOVSKY M . GeneMark.hmm: new solutions for gene finding[J]. Nucleic Acids Research, 1998,26(4): 1107-1115.
[41]	BESEMER J , LOMSADZE A , BORODOVSKY M , et al. GeneMarks: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions[J]. Nucleic Acids Research, 2011,29(12): 2607-2618.
[42]	SALZBERG S L , DELCHER A L , KASIF S , et al. Microbial gene identification using interpolated Markov models[J]. Nucleic Acids Research, 1998,26(2): 544-548.
[43]	DELCHER A L , BRATKE K A , POWERS E C , et al. Identifying bacterial genes and endosymbiont DNA with Glimmer[J]. Bioinformatics, 2007,23(6): 673-679.
[44]	FRIGAARD N U , MARTIMEZ A , MINCER T J , et al. Proteorhodopsin lateral gene transfer between marine planktonic bacteria and archaea[J]. Nature, 2006,439(7078): 847-850.
[45]	OUYANG Z , ZHU H , WANG J , et al. Multivariate entropy distance method for prokaryotic gene identification[J]. Journal of Bioinformatics and Computational Biology, 2004,2(2): 353-373.
[46]	ZHU H Q , HU G Q , YANG Y F , et al. MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes[J]. BMC Bioinformatics, 2007,8(1): 97.
[47]	NOGUCHI H , TANIGUCHI T , ITOH T , et al. MetaGeneAnnotator: detecting species-specific patterns of ribosomal binding site for precise gene prediction in anonymous prokaryotic and phage genomes[J]. DNA Research, 2008,15(6): 387-396.
[48]	HOFF K J , LINGNER T , MEINICKE P , et al. Orphelia: predicting genes in metagenomic sequencing reads[J]. Nucleic Acids Research, 2009,37(suppl 2): W101-W105.
[49]	ZHU W , LOMSADZE A , BORODOVSKY M , et al. Ab initio gene identification in metagenomic sequences[J]. Nucleic Acids Research, 2010,38(12):e132.
[50]	RHO M , TANG H , YE Y , et al. FragGeneScan:predicting genes in short and error-prone reads[J]. Nucleic Acids Research, 2010,38(20):e191.
[51]	KELLEY D R , LIU B , DELCHER A L , et al. Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering[J]. Nucleic Acids Research, 2012,40(1):e9.
[52]	HYATT D , LOCASCIO P F , HAUSER L J , et al. Gene and translation initiation site prediction in metagenomic sequences[J]. Bioinformatics, 2012,28(17): 2223-2230.
[53]	WANG Y , LEUNG H C M , YIU S M , et al. MetaCluster 5.0: a two-round binning approach for metagenomic data for low-abundance species in a noisy sample[J]. Bioinformatics, 2012,28(18): i356-i362.
[54]	LIU Y , GUO J , HU G , et al. Gene prediction in metagenomic fragments based on the SVM algorithm[J]. BMC Bioinformatics, 2013,14(suppl 5): S12.
[55]	DESANTIS T Z , HUGENHOLTZ P , LARSEN N , et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB[J]. Applied and Environmental Microbiology, 2006,72(7): 5069-5072.
[56]	PRUESSE E , QUAST C , KNITTEL K , et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB[J]. Nucleic Acids Research, 2007,35(21): 7188-7196.

项目名称	成员	年度
DNA元件百科全书（ENCODE）计划^[6]	400个科学家分成32组	2003
癌症和肿瘤基因图谱（TCGA）计划^[7]	超过150个研究人员，12个基金会	2005
千人基因组计划^[8]	1 000个基因组项目财团	2007
人类微生物组计划（HMP）^[9]	美国、日本、中国等10多个国家参与	2007
万种脊椎动物基因组联盟（G10K）^[10]	研究10 000种脊椎动物序列群落的科学家	2009
自闭症测序联盟（ASC）^[11]	超过20个独立的研究小组	2010
人类肠道宏基因组计划（MetaHIT）^[12]	来自中国、美国、丹麦、法国、日本、西班牙、英国、芬兰8个国家的学术界和工业界的13家研究机构	2010
人类蛋白质组计划（HPP）^[13]	中国、美国、德国、英国、加拿大、日本、韩国、澳大利亚、法国、俄国等19个国家（地区）	2010
地球微生物组计划（EMP）^[14]	国际交叉学科联盟	2011
5 000种昆虫和其他节肢动物基因组计划^[15]	国际交叉学科联盟	2011
国际癌症基因组联盟（ICGC）^[16]	联合各国科学家对癌症基因组开展研究	2012
10万病原微生物基因组计划	美国加州大学、安捷伦科技公司、美国食品药品管理局、美国疾病预防控制中心、美国国立卫生研究院、北京师范大学、中国科学院微生物研究所、北京诺赛基因组研究中心	2014
第五届癌症系统生物学国际研讨会	来自中国和美国的癌症生物学、癌症临床、癌症组学数据分析的专家学者	2015

组学	工具和方法
基因组学	next generation sequencing
表观基因组学	ChIP-seq；
	Histon ChIP-seq；
	bisulfide sequencing
转录组学	RNA-seq；
	RNA-PET；
	CAGE
蛋白质组学	mass spectrometry；
	MALDI-imaging；
	CyTOF
代谢物组学	mass spectrometry；
	NMR；
	liquid & gas；
	chromatography

算法	特征和策略
MetaGene,MetaGeneAnnotator^[47]	双密码子的回归模型，基因长度等多种特征打分的动态规划模型
Orphelia^[48]	双密码子，TIS信号等多种特征的人工神经网络
MetaGeneMark^[49]	寡核苷酸回归模型，基因长度等多种特征的动态规划模型
FragGeneScan^[50]	基于密码子的隐马尔科夫模型
Glimmer-MG^[51]	基于Phymn分类后在各类上实施Glimmer预测方法
MetaProdigal^[52]	基于六连码回归模型和TIS信号的动态规划模型
AbundanceBin^[35]	基于k字词的方法，将序列的k字词向量进行比较，将相近的划归为一组
MetaCluster^[53]	基于k字词的方法，对序列物种进行划分
MetaGUN^[54]	序列按基因预测模型和翻译起始信号打分参数的选择分类