Analysis，Comparison and Classification of Metagenomic Samples

doi:10.13560/j.cnki.biotech.bull.1985.2016.05.001

Abstract

Abstract: Metagenomics attempts to understand the diversity of the environmental microbial community and the interaction between microorganisms and environment by analyzing the sequence data of metagenomic samples. Microbiology has been revolutionized by metagenomics，which makes it feasible to research the microbial communities in complex biological systems without cultivating the microbes. The high-throughput metagenomic study is promoted by the rapid development of next-generation sequencing technology and bioinformatics. As a mass of high-quality metagenomic sequencing data are produced，also are accessible to the scientific community，the role of metagenomics has been recognized by various scientific areas. On the other sides，huge metagenomic data for individuals with different health status，or for different habitats of the human body makes the comparison and classification of metagenomic samples more important，leading the comparative metagenomics to become an important branch of metagenomics. This review mainly introduces the related researches and algorithms in the analysis，comparison and classification of metagenomic sequencing data.

Key words: metagenome, sample analysis and comparison, sample classification, classification feature

CHENG Fu-dong,DING Xiao,LI Sheng,SUN Xiao. Analysis，Comparison and Classification of Metagenomic Samples[J]. Biotechnology Bulletin, 2016, 32(5): 1-10.

References

[1]Huson DH, Mitra S, Ruscheweyh HJ, et al. Integrative analysis of environmental sequences using MEGAN4[J]. Genome Research, 2011, 21（9）：1552-1560.
[2]Haiser HJ, Gootenberg DB, Chatman K, et al. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta[J]. Science, 2013, 341（6143）：295-298.
[3]Koeth RA, Wang Z, Levison BS, et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis[J]. Nat Med, 2013, 19（5）：576-585.
[4]Ramakrishna BS. Role of the gut microbiota in human nutrition and metabolism[J]. J Gastroenterol Hepatol, 2013, 28（Suppl）4：9-17.
[5]Qin JJ, Li YR, Cai ZM, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes[J]. Nature, 2012, 490（7418）：55-60.
[6]Group NHW, Peterson J, Garges S, et al. The NIH human microbiome project[J]. Genome Res, 2009, 19（12）：2317-2323.
[7]Yang CY, Mills D, Mathee K, et al. An ecoinformatics tool for microbial community studies：supervised classification of Amplicon Length Heterogeneity（ALH）profiles of 16S rRNA[J]. Journal of Microbiological Methods, 2006, 65（1）：49-62.
[8]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.
[9]Ridaura V, Belkaid Y. Gut microbiota：the link to your second brain[J]. Cell, 2015, 161（2）：193-194.
[10]Korem T, Zeevi D, Suez J, et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples[J]. Science, 2015, 349（6252）：1101-1106.
[11]Wang Z, Roberts AB, Buffa JA, et al. Non-lethal inhibition of gut microbial trimethylamine production for the treatment of atherosclerosis[J]. Cell, 2015, 163（7）：1585-1595.
[12]Quast C, Pruesse E, Yilmaz P, et al. The SILVA ribosomal RNA gene database project：improved data processing and web-based tools[J]. Nucleic Acids Res, 2013, 41（Database issue）：D590-D596.
[13]Schloss PD, Westcott SL, Ryabin T, et al. Introducing mothur：open-source, platform-independent, community-supported software for describing and comparing microbial communities[J]. Appl Environ Microbiol, 2009, 75（23）：7537-7541.
[14]Caporaso JG, Kuczynski J, Stombaugh J, et al. QIIME allows analysis of high-throughput community sequencing data[J]. Nat Methods, 2010, 7（5）：335-336.
[15]Cole JR, Wang Q, Cardenas E, et al. The Ribosomal Database Project：improved alignments and new tools for rRNA analysis[J]. Nucleic Acids Res, 2009, 37（Database issue）：D141-D145.
[16]Brooks JP, Edwards DJ, Harwich MD, et al. The truth about metagenomics：quantifying and counteracting bias in 16S rRNA studies[J]. Bmc Microbiology, 2015, 15：66.
[17]Sohn MB, An LL, Pookhao N, et al. Accurate genome relative abundance estimation for closely related species in a metagenomic sample[J]. Bmc Bioinformatics, 2014, 15：242.
[18]Xia LC, Cram JA, Chen T, et al. Accurate genome relative abundance estimation based on shotgun metagenomic reads[J]. PLoS One, 2011, 6（12）：e27992.
[19]Yuan C, Lei J, Cole J, et al. Reconstructing 16S rRNA genes in metagenomic data[J]. Bioinformatics, 2015, 31（12）：i35-43.
[20]Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform[J]. Bioinformatics, 2009, 25（14）：1754-1760.
[21]Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND[J]. Nature Methods, 2015, 12（1）：59-60.
[22]Huson DH, Richter DC, Mitra S, et al. Methods for comparative metagenomics[J]. BMC Bioinformatics, 2009, 10（Suppl）1：S12.
[23]Lee J, Lee HT, Hong WY, et al. FCMM：A comparative metagenomic approach for functional characterization of multiple metagenome samples[J]. J Microbiol Methods, 2015, 115：121-128.
[24]Wintermans B, Brandt B, Vandenbroucke-Grauls C, et al. TreeSeq, a fast and intuitive tool for analysis of whole genome and metagenomic sequence data[J]. PLoS One, 2015, 10（5）：e0123851.
[25]Liu J, Wang H, Yang H, et al. Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms[J]. Nucleic Acids Res, 2013, 41（1）：e3.
[26]Albertsen M, Hugenholtz P, Skarshewski A, et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes[J]. Nature Biotechnology, 2013, 31（6）：533-538.
[27]Kislyuk A, Bhatnagar S, Dushoff J, et al. Unsupervised statistical clustering of environmental shotgun sequences[J]. Bmc Bioinformatics, 2009, 10：316.
[28]Wu YW, Ye YZ. A novel abundance-based algorithm for binning metagenomic sequences using l-tuples[J]. Journal of Computational Biology, 2011, 18（3）：523-534.
[29]Wang Y, Leung HCM, Yiu SM, 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.
[30]Wang Y, Leung HCM, Yiu SM, et al. MetaCluster-TA：taxonomic annotation for metagenomic data based on assembly-assisted binning[J]. Bmc Genomics, 2014（ Suppl1）1：S12.
[31]Ding X, Cao CC, Sun X. Intrinsic correlation of oligonucleotides：a novel genomic signature for metagenome analysis[J]. J Theor Biol, 2014, 353：9-18.
[32]Rodrigue S, Malmstrom RR, Berlin AM, et al. Whole genome amplification and de novo assembly of single bacterial cells[J]. PLoS One, 2009, 4（9）：e6864.
[33]Kodzius R, Gojobori T. Single-cell technologies in environmental omics[J]. Gene, 2016, 576（2 Pt 1）：701-707.
[34]Kashtan N, Roggensack SE, Rodrigue S, et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus[J]. Science, 2014, 344（6182）：416-420.
[35]Bergquist PL, Hardiman EM, Ferrari BC, et al. Applications of flow cytometry in environmental microbiology and biotechnology[J]. Extremophiles, 2009, 13（3）：389-401.
[36]Lasken RS. Genomic sequencing of uncultured microorganisms from single cells[J]. Nat Rev Microbiol, 2012, 10（9）：631-640.
[37]Paerl HW, Xu H, Hall NS, et al. Controlling cyanobacterial blooms in hypertrophic Lake Taihu, China：will nitrogen reductions cause replacement of non-N2 fixing by N2 fixing taxa?[J]. PLoS One, 2014, 9（11）：e113123.
[38]Knights D, Kuczynski J, Charlson ES, et al. Bayesian community-wide culture-independent microbial source tracking[J]. Nat Methods, 2011, 8（9）：761-763.
[39]Glaab E, Garibaldi JM, Krasnogor N. Learning pathway-based decision rules to classify micro array cancer samples[J]. German Conference on Conformations, 2010：123-134.
[40]Asyali MH, Colak D, Demirkaya O, et al. Gene expression profile classification：a review[J]. Current Bioinformatics, 2006, 1（1）：55-73.
[41]Yi G, Thon MR, Sze SH. Supervised protein family classification and new family construction[J]. Journal of Computational Biology, 2012, 19（8）：957-967.
[42]Willner D, Thurber RV, Rohwer F. Metagenomic signatures of 86 microbial and viral metagenomes[J]. Environ Microbiol, 2009, 11（7）：1752-1766.
[43]Pookhao N, Sohn MB, Li Q, et al. A two-stage statistical procedure for feature selection and comparison in functional analysis of metagenomes[J]. Bioinformatics, 2015, 31（2）：158-165.
[44]Shafiei M, Dunn KA, Chipman H, et al. BiomeNet：a bayesian model for inference of metabolic divergence among microbial communities[J]. Plos Computational Biology, 2014, 10（11）：e1003918.
[45]Nielsen HB, Almeida M, Juncker AS, et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes[J]. Nature Biotechnology, 2014, 32（8）：822-828.
[46]Yu JF, Xiao K, Jiang DK, et al. An integrative method for identifying the over-annotated protein-coding genes in microbial genomes[J]. DNA Res, 2011, 18（6）：435-449.
[47]Yu JF, Sun X. Reannotation of protein-coding genes based on an improved graphical representation of DNA sequence[J]. J Comput Chem, 2010, 31（11）：2126-2135.
[48]Rocha EPC, Danchin A. Base composition bias might result from competition for metabolic resources[J]. Trends in Genetics, 2002, 18（6）：291-294.
[49]Raes J, Foerstner KU, Bork P. Get the most out of your metagenome：computational analysis of environmental sequence data[J]. Current Opinion in Microbiology, 2007, 10（5）：490-498.
[50]Pride DT, Meinersmann RJ, Wassenaar TM, et al. Evolutionary implications of microbial genome tetranucleotide frequency biases[J]. Genome Res, 2003, 13（2）：145-158.
[51] Chatterji S, Yamazaki I, Bai Z, et al. CompostBin：a DNA compo-sition-based algorithm for binning environmental shotgun reads[M]//Vingron M, Wong L, editor, RECOMB, LNIB 4955, 2008：17-28.
[52] Ding X, Cheng F, Cao C, et al. DectICO：an alignment-free super-vised metagenomic classification method based on feature extract-ion and dynamic selection[J]. BMC Bioinformatics, 2015, 16：323.
[53]Pinello L, Lo Bosco G, Yuan GC. Applications of alignment-free methods in epigenomics[J]. Brief Bioinform, 2014, 15（3）：419-430.
[54]Ghosh TS, Mohammed MH, Rajasingh H, et al. HabiSign：a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences[J]. BMC Bioinformatics, 2011, 12 Suppl 13：S9.
[55]Liu Z, Hsiao W, Cantarel BL, et al. Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data[J]. Bioinformatics, 2011, 27（23）：3242-3249.
[56]Cui H, Zhang X. Alignment-free supervised classification of metagenomes by recursive SVM[J]. BMC Genomics, 2013, 14：641.
[57]Tanaseichuk O, Borneman J, Jiang T. Phylogeny-based classification of microbial communities[J]. Bioinformatics, 2014, 30（4）：449-456.
[58]Hinks TSC, Handley S, Keller B, et al. Analysis of the lung microbiome in human asthma using whole genome shot-gun metagenomics[J]. Thorax, 2013, 68：A14.