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Analysis Methods for Shotgun Metagenomics

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Theoretical and Applied Aspects of Systems Biology

Part of the book series: Computational Biology ((COBO,volume 27))

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Abstract

The development of whole metagenome shotgun sequencing (WGS) has enabled the precise characterization of taxonomic diversity and functional capabilities of microbial communities in situ while obviating organism isolation and cultivation procedures. WGS created with second- and third-generation sequencing technologies will generate millions of reads and tens (or hundreds) of gigabytes of information about the organisms under investigation. Despite containing an immense amount of information, the reads are unorganized and unlabeled, leading to a significant challenge in discerning from which genome a read originated. Thus, analysis of WGS data necessitates first determining community structure and function from the raw reads before the focus can shift to making multi-sample comparisons. A typical WGS workflow consists of read assignment (taxonomic binning and classification), preprocessing techniques (normalization, dimensionality reduction), exploratory approaches (feature selection and extraction, ordination), statistical inference (regression, constrained ordination, differential abundance analysis), and machine learning. The following chapter provides an overview of these analytical approaches (including challenges and possible pitfalls that may be encountered by researchers) as well as steps toward their solutions. Relevant software packages and resources are also discussed.

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Notes

  1. 1.

    Author Contributions: SW, abstract, taxonomic binning, taxonomic classification, normalization, feature selection, feature extraction, distance-based approaches, neural network approaches, statistical inference, machine learning, drafted and ordered sub-sections, coordinated co-authors; ZZ, taxonomic classification, machine learning; GD, diversity metrics, feature selection, feature extraction; JRP, abstract, diversity metrics, distance-based approaches, diversity metrics; ERR, abstract, introduction; YL, functional annotation; JC, neural network approaches; JE, feature selection, feature extraction; SKL, taxonomic binning; GR, taxonomic classification, discussion, drafted sub-sections; all authors contributed to editing and revising.

References

  1. Handelsman J, Rondon M, Brady S, Clardy J, Goodman R. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chem Biol. 1998;5(10):R245–9.

    Article  Google Scholar 

  2. Handelsman J. Metagenomics: application of genomics to uncultured microorganisms. Microbiol Mol Biol Rev. 2004;68(4):669+.

    Article  Google Scholar 

  3. Pace N, Stahl D, Lane D, Olsen G. The analysis of natural microbial-populations by ribosomal-RNA sequences. Adv Microb Ecol. 1986;9:1–55.

    Google Scholar 

  4. Simon C, Daniel R. Metagenomic analyses: past and future trends. Appl Environ Microbiol. 2011;77(4):1153–61.

    Article  Google Scholar 

  5. Streit W, Schmitz R. Metagenomics – the key to the uncultured microbes. Curr Opin Microbiol. 2004;7(5):492–8.

    Article  Google Scholar 

  6. Tringe SG, Hugenholtz P. A renaissance for the pioneering 16S rRNA gene. Curr Opin Microbiol. 2008;11(5):442–6.

    Article  Google Scholar 

  7. Ward N. New directions and interactions in metagenomics research. FEMS Microbiol Ecol. 2006;55(3):331–8.

    Article  Google Scholar 

  8. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71(12):8228–35.

    Article  Google Scholar 

  9. Solden L, Lloyd K, Wrighton K. The bright side of microbial dark matter: lessons learned from the uncultivated majority. Curr Opin Microbiol. 2016;31:217–26.

    Article  Google Scholar 

  10. Vieites JM, Guazzaroni ME, Beloqui A, Golyshin PN, Ferrer M. Metagenomics approaches in systems microbiology. FEMS Microbiol Rev. 2009;33(1):236–55.

    Article  Google Scholar 

  11. Woese CR, Fox GE. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc Nat Acad Sci. 1977;74(11):5088–90. Available from: http://www.pnas.org/cgi/doi/10.1073/pnas.74.11.5088.

    Article  Google Scholar 

  12. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, et al. Diversity of the human intestinal microbial flora. Science. 2005;308(5728):1635–8.

    Article  Google Scholar 

  13. Edwards RA, Rodriguez-Brito B, Wegley L, Haynes M, Breitbart M, Peterson DM, et al. Using pyrosequencing to shed light on deep mine microbial ecology. BMC Genomics. 2006;7:1–13.

    Article  Google Scholar 

  14. Ley RE, Peterson Da, Gordon JI. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell. 2006;124(4):837–48. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16497592.

    Article  Google Scholar 

  15. Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Trans Med. 2009;1(6):6ra14. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2894525&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  16. Venter J, Remington K, Heidelberg J, Halpern A, Rusch D, Eisen J, et al. Environmental genome shotgun sequencing of the Sargasso Sea. Science. 2004;304(5667):66–74.

    Article  Google Scholar 

  17. Edwards RA, McNair K, Faust K, Raes J, Dutilh BE. Computational approaches to predict bacteriophage-host relationships. FEMS Microbiol Rev. 2016;40(2):258–72.

    Article  Google Scholar 

  18. Forbes JD, Knox NC, Ronholm J, Pagotto F, Reimer A. Metagenomics: the next culture-independent game changer. Front Microbiol. 2017;8:1069. Available from: http://dx.doi.org/10.3389/fmicb.2017.01069.

  19. Hurwitz BL, U’Ren JM, Youens-Clark K. Computational prospecting the great viral unknown. FEMS Microbiol Lett. 2016;363(10):1–12.

    Article  Google Scholar 

  20. Kimura N. Metagenomic approaches to understanding phylogenetic diversity in quorum sensing. Virulence. 2014;5(3):433–42.

    Article  Google Scholar 

  21. Mathieu A, Vogel TM, Simonet P. The future of skin metagenomics. Res Microbiol. 2014;165(2):69–76.

    Article  Google Scholar 

  22. Sangwan N, Xia F, Gilbert JA. Recovering complete and draft population genomes from metagenome datasets. Microbiome. 2016;4:2–11.

    Google Scholar 

  23. Schmieder R, Edwards R. Insights into antibiotic resistance through metagenomic approaches. Future Microbiol. 2012;7(1):73–89.

    Article  Google Scholar 

  24. Altschul S, Gish W, Miller W, Myers E, Lipman D. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.

    Article  Google Scholar 

  25. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6.

    Article  Google Scholar 

  26. Giardine B, Riemer C, Hardison R, Burhans R, Elnitski L, Shah P, et al. Galaxy: a platform for interactive large-scale genome analysis. Genome Res. 2005;15(10):1451–5.

    Article  Google Scholar 

  27. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17(3):377–86.

    Article  Google Scholar 

  28. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658–9.

    Article  Google Scholar 

  29. Rosen GL, Reichenberger ER, Rosenfeld AM. NBC: the Naive Bayes classification tool webserver for taxonomic classification of metagenomic reads. Bioinformatics. 2011;27(1):127–9.

    Article  Google Scholar 

  30. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75(23):7537–41.

    Article  Google Scholar 

  31. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73(16):5261–67.

    Article  Google Scholar 

  32. Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM, et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature. 2004;428(6978):37–43.

    Article  Google Scholar 

  33. Sedlar K, Kupkova K, Provaznik I. Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Computational and Structural Biotechnology Journal 2017;15:48–55. Available from: http://doi.org/10.1016/j.csbj.2016.11.005.

    Article  Google Scholar 

  34. Mende DR, Waller AS, Sunagawa S, Järvelin AI, Chan MM, Arumugam M, et al. Assessment of metagenomic assembly using simulated next generation sequencing data. PLoS One. 2012;7(2):1–11.

    Article  Google Scholar 

  35. Vázquez-Castellanos JF, García-López R, Pérez-Brocal V, Pignatelli M, Moya A. Comparison of different assembly and annotation tools on analysis of simulated viral metagenomic communities in the gut. BMC Genomics. 2014;15(1):37. Available from: http://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-15-37.

    Article  Google Scholar 

  36. Mande SS, Mohammed MH, Ghosh TS. Classification of metagenomic sequences: methods and challenges. Brief Bioinform. 2012;13(6):669–81.

    Article  Google Scholar 

  37. Imelfort M, Parks D, Woodcroft BJ, Dennis P, Hugenholtz P, Tyson GW. GroopM: an automated tool for the recovery of population genomes from related metagenomes. PeerJ. 2014;2:e603. Available from: https://peerj.com/articles/603.

    Article  Google Scholar 

  38. Ribeca P, Valiente G. Computational challenges of sequence classification in microbiomic data. Brief Bioinform. 2011;12(6):614–25.

    Article  Google Scholar 

  39. Mohammed M, Ghosh TS, Singh NK, Mande SS. SPHINX – an algorithm for taxonomic binning of metagenomic sequences. Bioinformatics. 2010;27(1):22–30.

    Article  Google Scholar 

  40. Cole ST, Brosch R, Parkhill J, Garnier T, Churcher C, Harris D, et al. Deciphering the biology of mycobacterium tuberculosis from the complete genome sequence. Nature. 1998, p. 537–544. Available from: http://dx.doi.org/10.1038/31159.

    Article  Google Scholar 

  41. Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechno. 2013;31(6):533–8.

    Article  Google Scholar 

  42. Alneberg J, Bjarnason BS, De Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11(11):1144–6.

    Article  Google Scholar 

  43. Miller IJ, Chevrette MG, Kwan JC. Interpreting microbial biosynthesis in the genomic age: biological and practical considerations. Marine Drugs. 2017, 1–24. Available from: http://dx.doi.org/10.3390/md15060165.

  44. Lykidis A, Chen CL, Tringe SG, McHardy AC, Copeland A, Kyrpides NC, et al. Multiple syntrophic interactions in a terephthalate-degrading methanogenic consortium. ISME J. 2011;5(1):122–30.

    Article  Google Scholar 

  45. Belda-Ferre P, Alcaraz LD, Cabrera-Rubio R, Romero H, Simón-Soro A, Pignatelli M, et al. The oral metagenome in health and disease. ISME J. 2012;6(1):46–56.

    Article  Google Scholar 

  46. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59–65.

    Article  Google Scholar 

  47. Sangwan N, Xia F, Gilbert JA. Recovering complete and draft population genomes from metagenome datasets. Microbiome. 2016;4(1):8. Available from: http://www.microbiomejournal.com/content/4/1/8.

  48. Mohammed MH, Ghosh TS, Reddy RM, Reddy CV, Singh NK, Mande SS. INDUS – a composition-based approach for rapid and accurate taxonomic classification of metagenomic sequences. BMC Genomics. 2011;12(Suppl 3). Available from: http://www.hubmed.org/display.cgi?uids=22369237.

    Article  Google Scholar 

  49. Schmieder R, Edwards R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One. 2011;6(3):1–11.

    Article  Google Scholar 

  50. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods. 2012;9(8):811–4. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3443552&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  51. Liu B, Gibbons T, Ghodsi M, Pop M. MetaPhyler: taxonomic profiling for metagenomic sequences. In: Proceedings – 2010 IEEE international conference on bioinformatics and biomedicine, BIBM 2010; 2010, p. 95–100.

    Google Scholar 

  52. Sunagawa S, Mende DR, Zeller G, Izquierdo-Carrasco F, Berger Sa, Kultima JR, et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods. 2013;10(12):1196–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24141494.

    Article  Google Scholar 

  53. Nayfach S, Pollard KS. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 2015;16(1):51. Available from: http://genomebiology.com/2015/16/1/51.

  54. Freitas TAK, Li PE, Scholz MB, Chain PSG. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res. 2015;43(10): e69(1–14).

    Article  Google Scholar 

  55. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17(3):377–86. Available from: http://www.hubmed.org/display.cgi?uids=17255551.

    Article  Google Scholar 

  56. Ounit R, Wanamaker S, Close TJ, Lonardi S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genomics. 2015;16(1):236. Available from: http://www.biomedcentral.com/1471-2164/16/236.

  57. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15(3):R46. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053813&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  58. Ames SK, Hysom DA, Gardner SN, Lloyd GS, Gokhale MB, Allen JE. Scalable metagenomic taxonomy classification using a reference genome database. Bioinformatics (Oxford, England). 2013;29(18):2253–60. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23828782%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3753567.

    Article  Google Scholar 

  59. Sobih A, Tomescu AI, Mäkinen V. Metaflow: metagenomic profiling based on whole-genome coverage analysis with min-cost flows. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9649; 2016. p. 111–121.

    Google Scholar 

  60. Rosen G, Garbarine E, Caseiro D, Polikar R, Sokhansanj B. Metagenome fragment classification using N-mer frequency profiles. Adv Bioinform. 2008;2008:205969. Available from: http://www.hubmed.org/display.cgi?uids=19956701.

    Article  Google Scholar 

  61. Darling AE, Jospin G, Lowe E, Matsen FA, Bik HM, Eisen JA. PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ. 2014;2:e243. Available from: https://peerj.com/articles/243.

    Article  Google Scholar 

  62. McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E, Alexander N, et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 2017;18(1):182. Available from: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1299-7.

  63. Lindgreen S, Adair KL, Gardner PP. An evaluation of the accuracy and speed of metagenome analysis tools. Sci Rep. 2016;6:1–14. Available from: http://dx.doi.org/10.1038/srep19233.

  64. Prakash T, Taylor TD. Functional assignment of metagenomic data: challenges and applications. Brief Bioinform. 2012;13(6):711–27. Prakash, Tulika Taylor, Todd D eng Research Support, Non-U.S. Gov’t Review England 2012/07/10 06:00 Brief Bioinform. 2012;13(6):711–27. https://doi.org/10.1093/bib/bbs033.Epub2012Jul6.

    Article  Google Scholar 

  65. Carr R, Borenstein E. Comparative analysis of functional metagenomic annotation and the mappability of short reads. PLoS One. 2014;9(8):e105776. Carr, Rogan Borenstein, Elhanan eng DP2 AT007802/AT/NCCIH NIH HHS/ P30 DK089507/DK/NIDDK NIH HHS/ DP2 AT007802-01/AT/NCCIH NIH HHS/Comparative Study Research Support, N.I.H., Extramural 2014/08/26 06:00 PLoS One. 2014;9(8):e105776. https://doi.org/10.1371/journal.pone.0105776. eCollection 2014.

    Article  Google Scholar 

  66. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44(D1):D733–45. O’Leary, Nuala A Wright, Mathew W Brister, J Rodney Ciufo, Stacy Haddad, Diana McVeigh, Rich Rajput, Bhanu Robbertse, Barbara Smith-White, Brian Ako-Adjei, Danso Astashyn, Alexander Badretdin, Azat Bao, Yiming Blinkova, Olga Brover, Vyacheslav Chetvernin, Vyacheslav Choi, Jinna Cox, Eric Ermolaeva, Olga Farrell, Catherine M Goldfarb, Tamara Gupta, Tripti Haft, Daniel Hatcher, Eneida Hlavina, Wratko Joardar, Vinita S Kodali, Vamsi K Li, Wenjun Maglott, Donna Masterson, Patrick McGarvey, Kelly M Murphy, Michael R O’Neill, Kathleen Pujar, Shashikant Rangwala, Sanjida H Rausch, Daniel Riddick, Lillian D Schoch, Conrad Shkeda, Andrei Storz, Susan S Sun, Hanzhen Thibaud-Nissen, Francoise Tolstoy, Igor Tully, Raymond E Vatsan, Anjana R Wallin, Craig Webb, David Wu, Wendy Landrum, Melissa J Kimchi, Avi Tatusova, Tatiana DiCuccio, Michael Kitts, Paul Murphy, Terence D Pruitt, Kim D eng Intramural NIH HHS/ Research Support, N.I.H., Intramural England 2015/11/11 06:00 Nucleic Acids Res. 2016;44(D1):D733–45. https://doi.org/10.1093/nar/gkv1189. Epub 8 Nov 2015.

    Article  Google Scholar 

  67. UniProt Consortium. Reorganizing the protein space at the universal protein resource (UniProt). Nucleic Acids Res. 2012;40:D71–5.

    Google Scholar 

  68. Gasteiger E, Jung E, Bairoch A. SWISS-PROT: connecting biomolecular knowledge via a protein database. Curr Issues Mol Biol. 2001;3(3):47–55. Gasteiger, E Jung, E Bairoch, A Eng Review England 2001/08/08 10:00 Curr Issues Mol Biol. 2001;3(3):47–55.

    Google Scholar 

  69. Alberti A, Poulain J, Engelen S, Labadie K, Romac S, Ferrera I, et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci Data. 2017;4:170093. Alberti, Adriana Poulain, Julie Engelen, Stefan Labadie, Karine Romac, Sarah Ferrera, Isabel Albini, Guillaume Aury, Jean-Marc Belser, Caroline Bertrand, Alexis Cruaud, Corinne Da Silva, Corinne Dossat, Carole Gavory, Frederick Gas, Shahinaz Guy, Julie Haquelle, Maud Jacoby, E’krame Jaillon, Olivier Lemainque, Arnaud Pelletier, Eric Samson, Gaelle Wessner, Mark Acinas, Silvia G Royo-Llonch, Marta Cornejo-Castillo, Francisco M Logares, Ramiro Fernandez-Gomez, Beatriz Bowler, Chris Cochrane, Guy Amid, Clara Hoopen, Petra Ten De Vargas, Colomban Grimsley, Nigel Desgranges, Elodie Kandels-Lewis, Stefanie Ogata, Hiroyuki Poulton, Nicole Sieracki, Michael E Stepanauskas, Ramunas Sullivan, Matthew B Brum, Jennifer R Duhaime, Melissa B Poulos, Bonnie T Hurwitz, Bonnie L Pesant, Stephane Karsenti, Eric Wincker, Patrick eng Research Support, Non-U.S. Gov’t England 2017/08/02 06:00 Sci Data. 2017;4:170093. https://doi.org/10.1038/sdata.2017.93.

  70. The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14.

    Google Scholar 

  71. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. Nat Genetics. 2000;25(1):25–9.

    Article  Google Scholar 

  72. Tatusov RL, Fedorova ND, Jackson JD, Jacobs AR, Kiryutin B, Koonin EV, et al. The COG database: an updated version includes eukaryotes. BMC Bioinform. 2003;4:41–7.

    Google Scholar 

  73. Grigoriev IV, Nordberg H, Shabalov I, Aerts A, Cantor M, Goodstein D, et al. The Genome portal of the department of energy joint Genome Institute. Nucleic Acids Res. 2012;40: D26–32.

    Article  Google Scholar 

  74. Kanehisa M, Goto S, Kawashima YSM, Furumichi M, Tanabe M. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014;42: D199–205.

    Article  Google Scholar 

  75. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.

    Article  Google Scholar 

  76. Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2010;38:D473–9.

    Article  Google Scholar 

  77. Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 2005;33:5691–702.

    Article  Google Scholar 

  78. Altschul S, Gish W, Miller W, Myers E, Lipman D. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.

    Article  Google Scholar 

  79. Markowitz VM, Ivanova NN, Szeto E, Palaniappan K, Chu K, Dalevi D, et al. IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Res. 2008;36:D534–8.

    Article  Google Scholar 

  80. Markowitz V, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, et al. IMG: the integrated microbial genomes database and comparative analysis system. Nucleic Acids Res. 2012;40:D115–22.

    Article  Google Scholar 

  81. Aziz RK, et al. The RAST server: rapid annotations using subsystems technology. BMC Genomics. 2008;9(75):1–15.

    Google Scholar 

  82. Huson DH, Auch AF, Qi J, Schuster SC. MEGAN analysis of metagenomic data. Genome Res. 2007;17(3):377–86.

    Article  Google Scholar 

  83. Abubucker S, Segata N, Goll J, Schubert AM, Izard J, Cantarel BL, et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol. 2012;8(6):1–17.

    Article  Google Scholar 

  84. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2016;44(D1):D279–85. Finn, Robert D Coggill, Penelope Eberhardt, Ruth Y Eddy, Sean R Mistry, Jaina Mitchell, Alex L Potter, Simon C Punta, Marco Qureshi, Matloob Sangrador-Vegas, Amaia Salazar, Gustavo A Tate, John Bateman, Alex eng 108433/Z/15/Z]/Wellcome Trust/United Kingdom BB/L024136/1/Biotechnology and Biological Sciences Research Council/United Kingdom Howard Hughes Medical Institute/ Research Support, Non-U.S. Gov’t England 2015/12/18 06:00 Nucleic Acids Res. 2016;44(D1):D279–85. https://doi.org/10.1093/nar/gkv1344. Epub 15 Dec 2015.

    Article  Google Scholar 

  85. Sigrist CJA, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A, et al. New and continuing developments at PROSITE. Nucleic Acids Res. 2013;41(D1):E344–7. 062BE Times Cited:260 Cited References Count:14.

    Article  Google Scholar 

  86. Mi H, Muruganujan A, Thomas PD. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 2013;41(Database issue):D377–86. Mi, Huaiyu Muruganujan, Anushya Thomas, Paul D eng GM081084/GM/NIGMS NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t England 2012/11/30 06:00 Nucleic Acids Res. 2013;41(Database issue):D377–86. https://doi.org/10.1093/nar/gks1118. Epub 27 Nov 2012.

  87. Pedruzzi I, Rivoire C, Auchincloss AH, Coudert E, Keller G, de Castro E, et al. HAMAP in 2013, new developments in the protein family classification and annotation system. Nucleic Acids Res. 2013;41(Database issue):D584–9. Pedruzzi, Ivo Rivoire, Catherine Auchincloss, Andrea H Coudert, Elisabeth Keller, Guillaume de Castro, Edouard Baratin, Delphine Cuche, Beatrice A Bougueleret, Lydie Poux, Sylvain Redaschi, Nicole Xenarios, Ioannis Bridge, Alan eng 5R01GM080646-07/GM/NIGMS NIH HHS/ 8P20GM103446-12/GM/NIGMS NIH HHS/ 5G08LM010720-03/LM/NLM NIH HHS/ 2P41 HG02273/HG/NHGRI NIH HHS/ 3R01GM080646-07S1/GM/NIGMS NIH HHS/ SP/07/007/23671/British Heart Foundation/United Kingdom 1 U41 HG006104-03/HG/NHGRI NIH HHS/ Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t Research Support, U.S. Gov’t, Non-P.H.S. England 2012/11/30 06:00 Nucleic Acids Res. 2013 Jan;41(Database issue):D584–9. https://doi.org/10.1093/nar/gks1157. Epub 27 Nov 2012.

  88. Bru C, Courcelle E, Carrere S, Beausse Y, Dalmar S, Kahn D. The ProDom database of protein domain families: more emphasis on 3D. Nucleic Acids Res. 2005;33(Database issue):D212–5. Bru, Catherine Courcelle, Emmanuel Carrere, Sebastien Beausse, Yoann Dalmar, Sandrine Kahn, Daniel eng Research Support, Non-U.S. Gov’t England 2004/12/21 09:00 Nucleic Acids Res. 2005;33(Database issue):D212–5. https://doi.org/10.1093/nar/gki034.

  89. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 2009;37(Database issue):D211–5. Hunter, Sarah Apweiler, Rolf Attwood, Teresa K Bairoch, Amos Bateman, Alex Binns, David Bork, Peer Das, Ujjwal Daugherty, Louise Duquenne, Lauranne Finn, Robert D Gough, Julian Haft, Daniel Hulo, Nicolas Kahn, Daniel Kelly, Elizabeth Laugraud, Aurelie Letunic, Ivica Lonsdale, David Lopez, Rodrigo Madera, Martin Maslen, John McAnulla, Craig McDowall, Jennifer Mistry, Jaina Mitchell, Alex Mulder, Nicola Natale, Darren Orengo, Christine Quinn, Antony F Selengut, Jeremy D Sigrist, Christian J A Thimma, Manjula Thomas, Paul D Valentin, Franck Wilson, Derek Wu, Cathy H Yeats, Corin eng BB/F010508/1/Biotechnology and Biological Sciences Research Council/United Kingdom 087656/Wellcome Trust/United Kingdom GM081084/GM/NIGMS NIH HHS/ Wellcome Trust/United Kingdom BB/F010435/1/Biotechnology and Biological Sciences Research Council/United Kingdom Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t England 2008/10/23 09:00 Nucleic Acids Res. 2009;37(Database issue):D211–5. https://doi.org/10.1093/nar/gkn785. Epub 21 Oct 2008.

    Article  Google Scholar 

  90. Nayfach S, Pollard KS. Toward accurate and quantitative comparative metagenomics. Cell. 2016;166(5):1103–16. Available from: http://dx.doi.org/10.1016/j.cell.2016.08.007.

    Article  Google Scholar 

  91. Manor O, Borenstein E. MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome. Genome Biol. 2015;16(1):53. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25885687%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4391136.

  92. McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10(4):1–11.

    Article  Google Scholar 

  93. Silverman JD, Washburne AD, Mukherjee S, David LA. A phylogenetic transform enhances analysis of compositional microbiota data. eLife. 2017;6:1–20.

    Google Scholar 

  94. Li H. Microbiome, metagenomics, and high-dimensional compositional data analysis. Ann Rev Stat Appl. 2015;2(1):73–94. Available from: http://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-010814-020351?journalCode=statistics.

    Article  Google Scholar 

  95. Kurtz ZD, Mueller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11(5):1–25.

    Article  Google Scholar 

  96. Gloor GB, Reid G. Compositional analysis: a valid approach to analyze microbiome high throughput sequencing data. Can J Microbiol. 2016;703(April):2015–0821. Available from: http://www.nrcresearchpress.com/doi/abs/10.1139/cjm-2015-0821#.VxVj4pMrJIX.

  97. Kumar MS, Slud EV, Okrah K, Hicks SC, Hannenhalli S, Corrada Bravo H. Analysis and correction of compositional bias in sparse sequencing count data. bioRxiv. 2017;1–34. Available from: http://www.biorxiv.org/content/early/2017/05/27/142851?%3Fcollection=.

  98. Tsilimigras MCB, Fodor AA. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann Epidemiol. 2016;26(5):330–5. Available from: http://dx.doi.org/10.1016/j.annepidem.2016.03.002.

    Article  Google Scholar 

  99. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. App Environ Microbiol. 2005;71(12):8228–8235.

    Article  Google Scholar 

  100. Lozupone C, Hamady M, Kelley S, Knight R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol. 2007;73(5):1576–1585.

    Article  Google Scholar 

  101. Zvelebil M, Baum J. Understanding bioinformatics. New York: Garland Science; 2008.

    Google Scholar 

  102. Cover TM, Thomas JA. Elements of information theory. New York: Wiley-Interscience; 2006.

    Google Scholar 

  103. Kira K, Rendell L. A practical approach to feature selection. In: National conference on artificial intelligence; 1992.

    Chapter  Google Scholar 

  104. Hall MA. Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the seventeenth international conference on machine learning; 2000, p. 359–366. Available from: http://www.ime.unicamp.br/~wanderson/Artigos/correlation_based_feature_selection.pdf.

  105. Brown G, Pocock A, Zhao MJ, Luján M. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection. J Mach Learn Res. 2012;13:27–66.

    Google Scholar 

  106. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc. 1996;58(1):267–88.

    Google Scholar 

  107. Bates S, Tibshirani R. Log-ratio Lasso: scalable, sparse estimation for log-ratio models. 2017;1–24. Available from: http://arxiv.org/abs/1709.01139.

  108. Ditzler G, Morrison JC, Lan Y, Rosen G. Fizzy: feature selection for metagenomics. BMC Bioinform. 2015;16(358):1–8.

    Google Scholar 

  109. Zou H, Hastie T, Tibshirani R. Sparse principal component analysis. J Comput Graph Stat. 2006;15(2):262–86.

    Article  MathSciNet  Google Scholar 

  110. Blair E, Hastie T, Paul D, Tibshirani R. Prediction by supervised principal components. J Am Stat Assoc. 2006;101(473):119–37.

    Google Scholar 

  111. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Elements. 2009;1:337–87. Available from: http://www.springerlink.com/index/10.1007/b94608.

  112. Hotelling H. Relations between two sets of variates. Biometrika. 1936;28(3):321–77.

    Article  MATH  Google Scholar 

  113. van der Maaten L, Hinton GE. Visualizing high-dimensional data using t-SNE. J Mach Learn Res. 2008;9:2579–605.

    Google Scholar 

  114. Gower JC. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 1966;53(3/4):325. Available from: http://www.jstor.org/stable/2333639?origin=crossref.

    Article  MathSciNet  MATH  Google Scholar 

  115. Hirschfeld HO. A connection between correlation and contingency. Math Proc Camb Philos Soc. 1935;31(4):520–24. Available from: http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=1737020%5Cnhttp://journals.cambridge.org/action/displayFulltext?type=1&fid=2109508&jid=&volumeId=&issueId=04&aid=1737020&bodyId=&membershipNumber=&societyETOCSession=.

    Article  MATH  Google Scholar 

  116. Kenkel NC, Orloci L. Applying metric and nonmetric multidimensional scaling to ecological studies: some new results. Ecology. 1986;67(4):919–928.

    Article  Google Scholar 

  117. Kruskal JB. Nonmetric multidimensional scaling: a numerical method. Psychometrika. 1964;29(2):115–29.

    Article  MathSciNet  MATH  Google Scholar 

  118. Legendre P, Legendre L. Numerical ecology. Amsterdam: Elsevier Science; 2008.

    Google Scholar 

  119. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. bioRxiv. 2017;142760. Available from: https://www.biorxiv.org/content/early/2017/05/28/142760.full.pdf+html.

  120. Tan J, Doing G, Lewis KA, Price CE, Chen KM, Kyle C, et al. System-wide automatic extraction of functional signatures in Pseudomonas aeruginosa with eADAGE. bioRxiv. 2016, p. 1–25.

    Google Scholar 

  121. Xie R, Wen J, Quitadamo A, Cheng J, Shi X. A deep auto-encoder model for gene expression prediction. BMC Genomics. 2017;18(S9):845. Available from: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-017-4226-0.

  122. Mikolov T, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. CrossRef Listing of Deleted DOIs. 2000;1:1–9. Available from: http://www.crossref.org/deleted_DOI.html.

  123. Ng P. dna2vec: consistent vector representations of variable-length k-mers. 2017;1–10. Available from: http://arxiv.org/abs/1701.06279.

  124. Levy O, Goldberg Y. Neural word embedding as implicit matrix factorization. Adv Neural Inf Process Syst (NIPS). 2014;2177–85. Available from: http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.

  125. Levy O, Goldberg Y, Dagan I. Improving distributional similarity with lessons learned from word embeddings. Trans Assoc Comput Linguist. 2015;3:211–25. Available from: https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/570.

  126. Landgraf AJ, Bellay J. word2vec skip-gram with negative sampling is a weighted logistic PCA. 2017;1–5. Available from: http://arxiv.org/abs/1705.09755.

  127. Mikolov T, tau Yih W, Zweig G. Linguistic regularities in continuous space word representations. In: North American Chapter of the Association for Computational Linguistics. 2015.

    Google Scholar 

  128. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. CoRR. 2013;abs/1301.3781. Available from: http://arxiv.org/abs/1301.3781.

  129. Rao C. The use and interpretation of principal component analysis in applied research; 1964. Available from: http://www.jstor.org/stable/25049339.

  130. Legendre P, Andersson MJ. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol Monogr. 1999;69(1):1–24.

    Article  Google Scholar 

  131. ter Braak CJ. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology. 1986;67(5):1167–79.

    Article  Google Scholar 

  132. Blanchet G, Legendre P, Borcard D. Forward selection of spatial explanatory variables. Ecology. 2008;89(9):2623–32.

    Article  Google Scholar 

  133. Clarke KR, Ainsworth M. A method of linking multivariate community structure to environmental variables. Marine ecology progress series. 1993;92:205–219.

    Article  Google Scholar 

  134. MacKelprang R, Waldrop MP, Deangelis KM, David MM, Chavarria KL, Blazewicz SJ, et al. Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature. 2011;480(7377):368–71.

    Article  Google Scholar 

  135. Borcard D, Gillet F, Legendre, Legendre P. Numerical ecology with R. Springer. 2011.

    Book  MATH  Google Scholar 

  136. McCune B, Grace JB. Analysis of ecological communities. Gleneden Beach: MjM Software Design; 2002.

    Google Scholar 

  137. Ramette A. Multivariate analyses in microbial ecology. Fems Microbiology Ecology 2007;62(2):142–160. Available from: http://doi.org/10.1111/j.1574-6941.2007.00375.x.

    Article  Google Scholar 

  138. Ter Braak CJF. Canonical community ordination. Part I: basic theory and linear methods. Ecoscience. 1994;1:127–40.

    Article  Google Scholar 

  139. Gelman A, Stern H. The difference between significant and not significant is not itself statistically significant. Am Stat. 2006;60(4):328–31.

    Google Scholar 

  140. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1(1):3–14. Available from: http://doi.wiley.com/10.1111/j.2041-210X.2009.00001.x.

    Article  Google Scholar 

  141. Hoff PD. A first course in Bayesian statistical methods, vol. 64; 2009. Available from: http://books.google.com/books?id=9tv0taI8l6YC%5Cnhttp://www.amazon.com/Bayesian-Statistical-Methods-Springer-Statistics/dp/0387922997.

  142. Team SD. Stan modeling language. User’s guide and reference manual. 2017; p. 1–488. Available from: http://mc-stan.org/manual.html%5Cnpapers2://publication/uuid/C0937B19-1CC1-423C-B569-3FDB66090102.

  143. Paliy O, Shankar V. Application of multivariate statistical techniques in microbial ecology. Mol Ecol. 2016;25(5):1032–57.

    Article  Google Scholar 

  144. Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4053721&tool=pmcentrez&rendertype=abstract.

    Article  Google Scholar 

  145. Love MI, Anders S, Huber W. Differential analysis of count data – the DESeq2 package, vol. 15; 2014. Available from: http://biorxiv.org/lookup/doi/10.1101/002832%5Cnhttp://dx.doi.org/10.1186/s13059-014-0550-8.

    Google Scholar 

  146. Paulson J. MetagenomeSeq: statistical analysis for sparse high-throughput sequencing. BioconductorJp. 2014;1–20. Available from: http://bioconductor.jp/packages/2.14/bioc/vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf.

  147. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60(1–18).

    Article  Google Scholar 

  148. Jonsson V, Österlund T, Nerman O, Kristiansson E. Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics. BMC Genomics. 2016;17(1):78. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4727335&tool=pmcentrez&rendertype=abstract.

  149. Mitchell TM. Machine learning. 1st ed. New York: McGraw-Hill, Inc.; 1997.

    Google Scholar 

  150. McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E, Alexander N, et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 2017;18(1):182. Available from: https://doi.org/10.1186/s13059-017-1299-7.

  151. Chatterji S, Yamazaki I, Bai Z, Eisen J. CompostBin: a DNA composition-based algorithm for binning environmental shotgun reads. ArXiv e-prints. 2007 Aug.

    Google Scholar 

  152. Rosen G, Garbarine E, Caseiro D, Polikar R, Sokhansanj B. Metagenome fragment classification using N-mer frequency profiles. Adv Bioinform. 2008;2008(205969):1–12164: e79(1–11).

    Article  Google Scholar 

  153. Rosen GL, Reichenberger ER, Rosenfeld AM. NBC: the Naive Bayes classification tool webserver for taxonomic classification of metagenomic reads. Bioinformatics. 2011;27(1):127–9. Available from: +http://dx.doi.org/10.1093/bioinformatics/btq619.

    Article  Google Scholar 

  154. Borozan I, Watt S, Ferretti V. Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification. Bioinformatics. 2015;31(9):1396–404.

    Article  Google Scholar 

  155. Wang Y, Leung H, Yiu S, FY C. MetaCluster 4.0: a novel binning algorithm for NGS reads and huge number of species. J Comput Biol. 2012;19(2):241–9.

    Article  Google Scholar 

  156. Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658–9. Available from: +http://dx.doi.org/10.1093/bioinformatics/btl158.

    Article  Google Scholar 

  157. Ghodsi M, Liu B, Pop M. DNACLUST: accurate and efficient clustering of phylogenetic marker genes. BMC Bioinformatics. 2011;12(1):271. Available from: https://doi.org/10.1186/1471-2105-12-271.

    Article  Google Scholar 

  158. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1. Available from: +http://dx.doi.org/10.1093/bioinformatics/btq461.

    Article  Google Scholar 

  159. Yoon BJ. Hidden Markov models and their applications in biological sequence analysis. Curr Genomics. 2009;10(6):402–15.

    Article  MathSciNet  Google Scholar 

  160. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010;38(20):e191. Available from: +http://dx.doi.org/10.1093/nar/gkq747.

    Article  Google Scholar 

  161. Noguchi H, Park J, Takagi T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 2006;34(19):5623–30. Available from: +http://dx.doi.org/10.1093/nar/gkl723.

    Article  Google Scholar 

  162. Chen X, Ishwaran H. Random forests for genomic data analysis. Genomics. 2012;99(6): 323–9.

    Article  Google Scholar 

  163. Capriotti E, Calabrese R, Casadio R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics. 2006;22(22):2729–34. Available from: +http://dx.doi.org/10.1093/bioinformatics/btl423.

    Article  Google Scholar 

  164. Hastie T, Tibshirani R, Wainwright M. Statistical learning with sparsity: the Lasso and generalizations. Boca Raton: CRC; 2015; p. 362.

    Google Scholar 

  165. Hughey JJ, Butte AJ. Robust meta-analysis of gene expression using the elastic net. Nucleic Acids Res. 2015;43(12):e79(1–11). Available from: http://doi.org/10.1093/nar/gkv229.

    Article  Google Scholar 

  166. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science (N Y). 2001;291(5507):1304–51. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11181995.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA

    Stephen Woloszynek, Zhengqiao Zhao, Jian Chen & Gail Rosen

  2. Department of Electrical and Computer Engineering, University of Arizona, Tuscon, AZ, USA

    Gregory Ditzler

  3. Department of Civil, Architectural, and Environmental Engineering, Drexel University, Philadelphia, PA, USA

    Jacob R. Price & Saeed Keshani Langroodi

  4. U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center, Wyndmoor, PA, USA

    Erin R. Reichenberger

  5. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yemin Lan

  6. Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA

    Joshua Earl & Garth Ehrlich

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    Floriano Paes Silva Junior

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Woloszynek, S. et al. (2018). Analysis Methods for Shotgun Metagenomics. In: Alves Barbosa da Silva, F., Carels, N., Paes Silva Junior, F. (eds) Theoretical and Applied Aspects of Systems Biology. Computational Biology, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-74974-7_5

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