本文系统地讲述了在多元基因组的研究过程中，我们进行数据处理时使用到的工具和方法、以及这些方法因含有人为因素而造成的不准确之处。

多元基因组学方法是一种将基因测序或功能性试验应用到在实验室独立培养的、复杂多样的微生物中的研究方法。尽管传统的纯培育方法对物种的表征依然有着不可或缺的重要性，但其传统的排外特性阻碍了我们对微生物世界的进一步探究——微生物应该在一个群体中生存并相互作用。 在这篇文章中，作者致力于介绍用于多元基因组数据分析的新计算方法和统计工具，并阐述了在分析过程中应用不当而可能导致错误结论的人为因素。

大规模的多元基因组数据，和该学科带来的全新的复杂性，以及研究人员提出的崭新问题，使得计算方法分析在该领域产生了比传统的基因工程更为重要的影响。在该学科的研究中，有如下计算和统计工具可以使用。
（1）基因组装和预测
DNA序列集齐后，数据分析的第一步是重构整个微生物的基因组。目前已设计出Metagene和Orphelia用来预测较短的毗连DNA序列；Krause和他的同事也研究出一种基因发现器用于毗连DNA序列；另辟蹊径的ORFome组装方法同时也应运而生。
（2）定性并定量地表征微生物种多样性的工具
人们已经开发出一些直接从原始数据推断物种信息的计算工具，它们对基因组的组装完全没有要求。一些基于相似性和系统发展的工具对已知的基因或蛋白质数据库进行多元基因组的相似性搜索；其他一些工具也已开发出来去应对相关问题，它们大多使用了DNA分解技术，但是基于DNA分解的方法只对较长的毗连DNA序列起作用；更多致力于解决超短毗连DNA序列的工具同时正在出现，而高精度的计算工具仍然有着很大的市场。
（3）功能预测
基于多元基因组数据的功能预测比基因的研究更为困难，相关算法和软件也更少一些。尽管我们已经亲眼目睹基于非同源的方法成功地解决了预测多元基因组序列的问题，但是许多现存的非同源性功能预测方法并不能应用到多元基因组序列的功能预测上。
（4）对比多元基因组学
对比多元基因组学对研究生活在不同环境中的微生物群体的功能和进化有其重要性。UniFrac一直以来经常用于比较包含不同宗族的群体，而MEGAN是另外一种基于所包含宗族进行多元基因组的视觉和统计比较工具。与此同时，微生物群体也可基于非宗族的其他信息进行比较。
（5）多元基因组学的统计工具
考虑到多元基因组研究中的采样和测序都可看成随机过程，我们需要统计工具来分析数据，PHACCS就是一种使用多元基因组信息估计未经培育的病毒群体的结构和多样性的在线工具。在基于不完全的观察或表征微生物群体对它们进行比较的过程中也需要统计技术，相关的用于群体成员和结构比较的统计工具已经开发出来。Metastat是一种用于检测两个种族不同特征的统计工具。与此同时，我们需注意到，不同的统计技术各有其用途和局限性。

多元基因组学促使我们研究微生物是如何适应它们的生存环境并与它们的宿主共同发展的，这些有基础意义和实际意义，各研究人员为此建立了自己的交互模型。

多元基因组序列的数据挖掘因规模庞大、序列复杂而变得具有挑战性，这也意味着在解决相应问题时人们更容易犯错误。以下列出了在多元基因组研究中发现的部分对结论产生较大影响的人为因素：
（1）16s rRNA 嵌合体容易导致对群体物种多样性的估计失误；
（2）人为的复制可能会给基因和大量分类单元的估计带来系统上的人为性；
（3）由于不同基因家族的长度，基于多元基因组数据中毗连DNA计数的基因家族的频率可能是不可靠的；
（4）注意人为通路的影响，MinPath这个计算工具可以很好地减少人为通路。

尽管最近二十年来DNA序列的工作成果成指数级增长，大量的多元基因组数据在很多领域依旧挑战着众多的研究人员：
（1）我们需要更多有效的计算工具处理大量的多元基因组数据；
（2）不同数据集的一体化需要更多的知识；
（3）我们依旧需要想象力。

¹Center for Research on BioSystems, Calit2, University of Califormia San Diego, La Jolla, CA 92093, U.S.A.
²School of Informatics and Computing, Indiana University, Bloomington, Indiana, 47408, U.S.A.

Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. By enabling an analysis of populations including many (so-far) unculturable and often unknown microbes, metagenomics is revolutionizing the field of microbiology, and has excited researchers in many disciplines that could benefit from the study of environmental microbes, including those in ecology, environmental sciences, and biomedicine. Specific computational and statistical tools have been developed for metagenomic data analysis and comparison. New studies, however, have revealed various kinds of artifacts present in metagenomics data caused by limitations in the experimental protocols and/or inadequate data analysis procedures, which often lead to incorrect conclusions about a microbial community. Here, we review some of the artifacts, such as overestimation of species diversity and incorrect estimation of gene family frequencies, and discuss emerging computational approaches to address them. We also review potential challenges that metagenomics may encounter with the extensive application of next-generation sequencing (NGS) techniques.

This work is supported by NIH under Grant No. 1R01HG004908-01, NSF of USA under Grant No. DBI-0845685 (YY), and also the Gordon and Betty Moore Foundation for the Community Cyberinfrastructure for Marine Microbial Ecological Research and Analysis (CAMERA) Project (JW).

[1] Handelsman J, Rondon M R, Brady S F, Clardy J, Goodman R M. Molecular biological access to the chemistry of unknown soil microbes: A new frontier for natural products. Chemistry & Biology, 1998, 5(10): R245-R249.
[2] Mardis E. Anticipating the 1,000 dollar genome. Genome Biol., 2006, 7(7): 112.
[3] Tyson G, Chapman J, Hugenholtz P, Allen E, Ram R, Richardson P, Solovyev V, Rubin E, Rokhsar D, Banfield J. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature, 2004, 428(6978): 37-43.
[4] Venter J, Remington K, Heidelberg J et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science, 2004, 304(5667): 66-74.
[5] Dinsdale E A, Pantos O, Smriga S, Edwards R A et al. Microbial ecology of four coral atolls in the Northern Line Islands. PLoS ONE, 2008, 3(2): e1584.
[6] Lorenz P, Eck J. Metagenomics and industrial applications. Nat. Rev. Microbiol., 2005, 3(6): 510-516.
[7] Turnbaugh P J, Hamady M, Yatsunenko T et al. A core gut microbiome in obese and lean twins. Nature, 2009, 457(7228): 480-484.
[8] Turnbaugh P J, Ley R E, Hamady M, Fraser-Liggett C M, Knight R, Gordon J I. The human microbiome project. Nature, 2007, 449(7164): 804-810.
[9] Hamady M, Walker J J, Harris J K, Gold N J, Knight R. Error-correcting barcoded primers for pyrosequencing hundreds of samples in multiplex. Nat. Methods, 2008, 5(3): 235-237.
[10] Li L, McCorkle S, Monchy S, Taghavi S, van der Lelie D. Bioprospecting metagenomes: Glycosyl hydrolases for converting biomass. Biotechnol. Biofuels, 2009, 2: 10.
[11] Brulc J, Antonopoulos D, Miller M et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc. Natl. Acad. Sci. USA, 2009, 106(6): 1948-1953.
[12] Jones B, Begley M, Hill C, Gahan C, Marchesi J. Functional and comparative metagenomic analysis of bile salt hydrolase activity in the human gut microbiome. Proc. Natl. Acad. Sci. USA, 2008, 105(36): 13580-13585.
[13] Mori T, Mizuta S, Suenaga H, Miyazaki K. Metagenomic screening for bleomycin resistance genes. Appl. Environ. Microbiol., 2008, 74(21): 6803-6805.
[14] Steele H, Jaeger K, Daniel R, Streit W. Advances in recovery of novel biocatalysts from metagenomes. J Mol. Microbiol. Biotechnol., 2009, 16(1/2): 25-37.
[15] Handelsman J, Tiedje J M, Alvarez-Cohen L et al. The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet. The National Academies Press, 2007.
[16] Tringe S, von Mering C, Kobayashi A et al. Comparative metagenomics of microbial communities. Science, 2005, 308(5721): 554-557.
[17] Turnbaugh P J, Ley R E, Mahowald M A, Magrini V, Mardis E R, Gordon J I. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature, 2006, 444(7122): 1027-1131.
[18] Hooper S D, Raes J, Foerstner K U, Harrington E D, Dalevi D, Bork P. A molecular study of microbe transfer between distant environments. PLoS ONE, 2008, 3(7): e2607.
[19] Raes J, Foerstner K U, Bork P. Get the most out of your metagenome: Computational analysis of environmental sequence data. Curr. Opin. Microbiol., 2007, 10(5): 490-498.
[20] Kunin V, Copeland A, Lapidus A, Mavromatis K, Hugenholtz P. A bioinformatician’s guide to metagenomics. Microbiol. Mol. Biol. Rev., 2008, 72(4): 557-578, Table of Contents.
[21] Hamady M, Knight R. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Res., 2009, 19(7): 1141-1152.
[22] Galperin M. Metagenomics: From acid mine to shining sea. Environ. Microbiol., 2004, 6(6): 543-545.
[23] Hernandez D, Francois P, Farinelli L, Osteras M, Schrenzel J. De novo bacterial genome sequencing: Millions of very short reads assembled on a desktop computer. Genome Res., 2008, 18(5): 802-809.
[24] Butler J, MacCallum I, Kleber M, Shlyakhter I A, Belmonte M K, Lander E S, Nusbaum C, Jaffe D B. ALLPATHS: De novo assembly of whole-genome shotgun microreads. Genome Res., 2008, 18(5): 810-820.
[25] Chaisson M J, Pevzner P A. Short read fragment assembly of bacterial genomes. Genome Res., 2008, 18(2): 324-330.
[26] PopM. Genome assembly reborn: Recent computational challenges. Brief Bioinform., 2009, 10(4): 354-366.
[27] Noguchi H, Park J, Takagi T. MetaGene: Prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res., 2006, 34(19): 5623-5630.
[28] Hoff K J, Tech M, Lingner T, Daniel R, Morgenstern B, Meinicke P. Gene prediction in metagenomic fragments: A large scale machine learning approach. BMC Bioinformatics, 2008, 9: 217.
[29] Hoff K J, Lingner T, Meinicke P, Tech M. Orphelia: Predicting genes in metagenomic sequencing reads. Nucleic Acids Res., 2009, 37(Web Server Issue): W101-W105.
[30] Krause L, Diaz N N, Bartels D, Edwards R A, Puhler A, Rohwer F, Meyer F, Stoye J. Finding novel genes in bacterial communities isolated from the environment. Bioinformatics, 2006, 22(14): e281-e289.
[31] Ye Y, Tang H. An orfome assembly approach to metagenomics sequences analysis. J. Bioinform. Comput. Biol., 2009, 7(3): 455-471.
[32] Cardenas E, Tiedje J. New tools for discovering and characterizing microbial diversity. Curr. Opin. Biotechnol., 2008, 19(6): 544-549.
[33] Huson D H, Auch A F, Qi J, Schuster S C. MEGAN analysis of metagenomic data. Genome Res., 2007, 17(3): 377-386.
[34] Chakravorty S, Helb D, Burday M, Connell N, Alland D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods, 2007, 69(2): 330-339.
[35] Monier A, Claverie J M, Ogata H. Taxonomic distribution of large DNA viruses in the sea. Genome Biol., 2008, 9(7): R106.
[36] Ciccarelli F D, Doerks T, von Mering C, Creevey C J, Snel B, Bork P. Toward automatic reconstruction of a highly resolved tree of life. Science, 2006, 311(5765): 1283-1287.
[37] von Mering C, Hugenholtz P, Raes J, Tringe S G, Doerks T, Jensen L J, Ward N, Bork P. Quantitative phylogenetic assessment of microbial communities in diverse environments. Science, 2007, 315(5815): 1126-1130.
[38] Wu M, Eisen J A. A simple, fast, and accurate method of phylogenomic inference. Genome Biol., 2008, 9(10): R151.
[39] Krause L, Diaz N N, Goesmann A, Kelley S, Nattkemper T W, Rohwer F, Edwards R A, Stoye J. Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Res., 2008, 36(7): 2230-2239.
[40] Finn R D, Mistry J, Schuster-Bockler B et al. Pfam: Clans, Web tools and services. Nucleic Acids Res., 2006, 34(Database Issue): D247-D251.
[41] Bentley S D, Parkhill J. Comparative genomic structure of prokaryotes. Annu. Rev. Genet., 2004, 38: 771-792.
[42] Teeling H, Waldmann J, Lombardot T, Bauer M, Glockner F O. TETRA: A Web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences. BMC Bioinformatics, 2004, 5: 163.
[43] Woyke T, Teeling H, Ivanova N N et al. Symbiosis insights through metagenomic analysis of a microbial consortium. Nature, 2006, 443(7114): 950-955.
[44] Chatterji S, Yamazaki I, Bai Z, Eisen J. CompostBin: A DNA composition-based algorithm for binning environmental shotgun reads. In Proc. the 12th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2008), Singapore, March 30-April 2, 2008, pp.17- 28.
[45] Zhou F, Olman V, Xu Y. Barcodes for genomes and applications. BMC Bioinformatics, 2008, 9: 546.
[46] Brady A, Salzberg S L. Phymm and PhymmBL: Metagenomic phylogenetic classification with interpolated Markov models. Nat. Methods, 2009, 6(9): 673-676.
[47] Gilbert J A, Field D, Huang Y, Edwards R, Li W, Gilna P, Joint I. Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities. PLoS One, 2008, 3(8): e3042.
[48] Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 2000, 28(1): 27-30.
[49] Overbeek R, Begley T, Butler R M et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res., 2005, 33(7): 5691-5702.
[50] Dinsdale E A, Edwards R A, Hall D et al. Functional metagenomic profiling of nine biomes. Nature, 2008, 452(7187): 629- 632.
[51] Meyer F, Paarmann D, D’Souza M et al. The metagenomics RAST server — A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics, 2008, 9: 386.
[52] Yooseph S, Sutton G, Rusch D B et al. The Sorcerer II Global Ocean Sampling expedition: Expanding the universe of protein families. PLoS Biol., 2007, 5(3): e16.
[53] Li W, Wooley J C, Godzik A. Probing metagenomics by rapid cluster analysis of very large datasets. PLoS One, 2008, 3(10): e3375.
[54] Li W, Jaroszewski L, Godzik A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics, 2001, 17(3): 282-283.
[55] Marcotte E M. Computational genetics: Finding protein function by nonhomology methods. Curr. Opin. Struct. Biol., 2000, 10(3): 359-365.
[56] Tringe S G, von Mering C, Kobayashi A et al. Comparative metagenomics of microbial communities. Science, 2005, 308(5721): 554-557.
[57] Foerstner K U, von Mering C, Hooper S D, Bork P. Environments shape the nucleotide composition of genomes. EMBO Rep., 2005, 6(12): 1208-1213.
[58] Raes J, Korbel J O, Lercher M J, von Mering C, Bork P. Prediction of effective genome size in metagenomic samples. Genome Biol., 2007, 8(1): R10.
[59] Gianoulis T A, Raes J, Patel P V et al. Quantifying environmental adaptation of metabolic pathways in metagenomics. Proc. Natl. Acad. Sci. USA, 2009, 106(5): 1374-1379.
[60] Lozupone C, Knight R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol., 2005, 71(12): 8228-8235.
[61] Huson D H, Richter D C, Mitra S, Auch A F, Schuster S C. Methods for comparative metagenomics. BMC Bioinformatics, 2009, 10(Suppl 1): S12.
[62] Mitra S, Klar B, Huson D H. Visual and statistical comparison of metagenomes. Bioinformatics, 2009, 25(15): 1849-1855.
[63] Schloss P D, Handelsman J. A statistical toolbox for metagenomics: Assessing functional diversity in microbial communities. BMC Bioinformatics, 2008, 9: 34.
[64] Wommack K E, Bhavsar J, Ravel J. Metagenomics: Read length matters. Appl. Environ. Microbiol., 2008, 74(5): 1453-1463.
[65] Hughes J B, Hellmann J J, Ricketts T H, Bohannan B J. Counting the uncountable: Statistical approaches to estimating microbial diversity. Appl. Environ. Microbiol., 2001, 67(10): 4399-4406.
[66] Breitbart M, Salamon P, Andresen B, Mahaffy J M, Segall A M, Mead D, Azam F, Rohwer F. Genomic analysis of uncultured marine viral communities. Proc. Natl. Acad. Sci. USA, 2002, 99(22): 14250-14255.
[67] Schloss P D, Handelsman J. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl. Environ. Microbiol., 2005, 71(3): 1501-1506.
[68] Angly F, Rodriguez-Brito B, Bangor D, McNairnie P, Breitbart M, Salamon P, Felts B, Nulton J, Mahaffy J, Rohwer F. PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information. BMC Bioinformatics, 2005, 6: 41.
[69] Schloss P D. Evaluating different approaches that test whether microbial communities have the same structure. ISME J, 2008, 2(3): 265-275.
[70] Schloss P D, Handelsman J. Introducing SONS, a tool for operational taxonomic unit-based comparisons of microbial community memberships and structures. Appl. Environ. Microbiol., 2006, 72(10): 6773-6779.
[71] White J, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol., 2009, 5(4): e1000352.
[72] Zaneveld J, Turnbaugh P J, Lozupone C, Ley R E, Hamady M, Gordon J I, Knight R. Host-bacterial coevolution and the search for new drug targets. Curr. Opin. Chem. Biol., 2008, 12(1): 109-114.
[73] Ley R E, Hamady M, Lozupone C, Turnbaugh P J, Ramey R R, Bircher J S, Schlegel M L, Tucker T A, Schrenzel M D, Knight R, Gordon J I. Evolution of mammals and their gut microbes. Science, 2008, 320(5883): 1647-1651.
[74] Shannon P, Markiel A, Ozier O, Baliga N S, Wang J T, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13(11): 2498-2504.
[75] Rusch D B, Halpern A L, Sutton G et al. The Sorcerer II Global Ocean Sampling expedition: Northwest Atlantic through eastern tropical Pacific. PLoS Biol., 2007, 5(3): e77.
[76] Ashelford K E, Chuzhanova N A, Fry J C, Jones A J, Weightman A J. At least 1 in 20 16S rRNA sequence records currently held in public repositories is estimated to contain substantial anomalies. Appl. Environ. Microbiol., 2005, 71(12): 7724- 7736.
[77] Williams R, Peisajovich S, Miller O, Magdassi S, Tawfik D, Griffiths A. Amplification of complex gene libraries by emulsion PCR. Nat. Methods, 2006, 3(7): 545-550.
[78] Huber T, Faulkner G, Hugenholz P. Bellerophon: A program to detect chimeric sequences in multiple sequence alignments. Bioinformatics, 2004, 20(14): 2317-2319.
[79] Ashelford K E, Chuzhanova N A, Fry J C, Jones A J, Weightman A J. New screening software shows that most recent large 16S rRNA gene clone libraries contain chimeras. Appl. Environ. Microbiol., 2006, 72(9): 5734-5741.
[80] Gomez-Alvarez V, Teal T, Schmidt T. Systematic artifacts in metagenomes from complex microbial communities. ISME J, 2009, 3(11): 1314–1317.
[81] Sharon I, Pati A, Markowitz V M, Pintter R Y. A statistical framework for the functional analysis of metagenomes. In Proc. RECOMB 2009, Tucson, USA, May 18–21, 2009, pp.496-511.
[82] Lander E S, Waterman M S. Genomic mapping by fingerprinting random clones: A mathematical analysis. Genomics, 1988, 2: 231-239.
[83] Ye Y, Doak T G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol., 2009, 5(8): e1000465.
[84] Okuda S, Yamada T, Hamajima M, Itoh M, Katayama T, Bork P, Goto S, Kanehisa M. KEGG Atlas mapping for global analysis of metabolic pathways. Nucleic Acids Res., 2008, 36(Web Server Issue): W423-W426.
[85] Rosin F M, Watanabe N, Lam E. Moonlighting vacuolar protease: Multiple jobs for a busy protein. Trends Plant Sci., 2005, 10(11): 516-518.
[86] Seshadri R, Kravitz S A, Smarr L, Gilna P, Frazier M. CAMERA: A community resource for metagenomics. PLoS Biol., 2007, 5(3): e75.
[87] Price M N, Dehal P S, Arkin A P. FastBLAST: Homology relationships for millions of proteins. PLoS One, 2008, 3(10): e3589.
[88] Sun Y, Cai Y, Liu L, Yu F, Farrell M L, McKendree W, FarmerieW. ESPRIT: Estimating species richness using large collections of 16S rRNA pyrosequences. Nucleic Acids Res., 2009, 37(10): e76.
[89] Shi Y, Tyson G W, DeLong E F. Metatranscriptomics reveals unique microbial small RNAs in the ocean’s water column. Nature, 2009, 459(7244): 266-269.
[90] Verberkmoes N C, Russell A L, Shah M, Godzik A, Rosenquist M, Halfvarson J, Lefsrud M G, Apajalahti J, Tysk C, Hettich R L, Jansson J K. Shotgun metaproteomics of the human distal gut microbiota. ISME J, 2009, 3(2): 179-189.
[91] Frias-Lopez J, Shi Y, Tyson G W, Coleman M L, Schuster S C, Chisholm S W, Delong E F. Microbial community gene expression in ocean surface waters. Proc. Natl. Acad. Sci. USA, 2008, 105(10): 3805-3810.