A clinician's guide to microbiome analysis (original) (raw)
Manichanh, C., Borruel, N., Casellas, F. & Guarner, F. The gut microbiota in IBD. Nat. Rev. Gastroenterol. Hepatol.9, 599–608 (2012). ArticleCASPubMed Google Scholar
Salonen, A., de Vos, W. M. & Palva, A. Gastrointestinal microbiota in irritable bowel syndrome: present state and perspectives. Microbiology156, 3205–3215 (2010). ArticleCASPubMed Google Scholar
Tang, W. H. et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med.368, 1575–1584 (2013). ArticleCASPubMedPubMed Central Google Scholar
Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature535, 376–381 (2016). ArticleCASPubMed Google Scholar
Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science336, 1255–1262 (2012). ArticleCASPubMedPubMed Central Google Scholar
Dethlefsen, L., Huse, S., Sogin, M. L. & Relman, D. A. The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol.6, e280 (2008). ArticleCASPubMedPubMed Central Google Scholar
Petrosino, J. F., Highlander, S., Luna, R. A., Gibbs, R. A. & Versalovic, J. Metagenomic pyrosequencing and microbial identification. Clin. Chem.55, 856–866 (2009). ArticleCASPubMedPubMed Central Google Scholar
Acinas, S. G., Marcelino, L. A., Klepac-Ceraj, V. & Polz, M. F. Divergence and redundancy of 16S rRNA sequences in genomes with multiple rrn operons. J. Bacteriol.186, 2629–2635 (2004). ArticleCASPubMedPubMed Central Google Scholar
Neefs, J. M., Van de Peer, Y., De Rijk, P., Chapelle, S. & De Wachter, R. Compilation of small ribosomal subunit RNA structures. Nucleic Acids Res.21, 3025–3049 (1993). ArticleCASPubMedPubMed Central Google Scholar
Claesson, M. J. et al. Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucleic Acids Res.38, e200 (2010). ArticleCASPubMedPubMed Central Google Scholar
Clooney, A. G. et al. Comparing apples and oranges?: Next generation sequencing and its impact on microbiome analysis. PLoS ONE11, e0148028 (2016). ArticleCASPubMedPubMed Central Google Scholar
Lavelle, A. et al. Spatial variation of the colonic microbiota in patients with ulcerative colitis and control volunteers. Gut64, 1553–1561 (2015). ArticleCASPubMedPubMed Central Google Scholar
Huse, S. M. et al. Comparison of brush and biopsy sampling methods of the ileal pouch for assessment of mucosa-associated microbiota of human subjects. Microbiome2, 5 (2014). ArticlePubMedPubMed Central Google Scholar
Chiodini, R. J. et al. Microbial population differentials between mucosal and submucosal intestinal tissues in advanced crohn's disease of the ileum. PLoS ONE10, e0134382 (2015). ArticleCASPubMedPubMed Central Google Scholar
Watt, E. et al. Extending colonic mucosal microbiome analysis-assessment of colonic lavage as a proxy for endoscopic colonic biopsies. Microbiome4, 61 (2016). ArticlePubMedPubMed Central Google Scholar
Shobar, R. M. et al. The effects of bowel preparation on microbiota-related metrics differ in health and in inflammatory bowel disease and for the mucosal and luminal microbiota compartments. Clin. Transl Gastroenterol.7, e143 (2016). ArticleCASPubMedPubMed Central Google Scholar
Gorzelak, M. A. et al. Methods for improving human gut microbiome data by reducing variability through sample processing and storage of stool. PLoS ONE10, e0134802 (2015). ArticleCASPubMedPubMed Central Google Scholar
Bahl, M. I., Bergstrom, A. & Licht, T. R. Freezing fecal samples prior to DNA extraction affects the Firmicutes to Bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol. Lett.329, 193–197 (2012). ArticleCASPubMed Google Scholar
Vogtmann, E. et al. Comparison of collection methods for fecal samples in microbiome studies. Am. J. Epidemiol.185, 115–123 (2017). ArticlePubMedPubMed Central Google Scholar
Hill, C. J. et al. Effect of room temperature transport vials on DNA quality and phylogenetic composition of faecal microbiota of elderly adults and infants. Microbiome4, 19 (2016). ArticlePubMedPubMed Central Google Scholar
Anderson, E. L. et al. A robust ambient temperature collection and stabilization strategy: enabling worldwide functional studies of the human microbiome. Sci. Rep.6, 31731 (2016). ArticleCASPubMedPubMed Central Google Scholar
Flores, R. et al. Collection media and delayed freezing effects on microbial composition of human stool. Microbiome3, 33 (2015). ArticlePubMedPubMed Central Google Scholar
Choo, J. M., Leong, L. E. & Rogers, G. B. Sample storage conditions significantly influence faecal microbiome profiles. Scientif. Rep.5, 16350 (2015). ArticleCAS Google Scholar
Sherker, A. R., Cherepanov, V., Alvandi, Z., Ramos, R. & Feld, J. J. Optimal preservation of liver biopsy samples for downstream translational applications. Hepatol. Int.7, 758–766 (2013). ArticlePubMed Google Scholar
Persson, S., de Boer, R. F., Kooistra-Smid, A. M. & Olsen, K. E. Five commercial DNA extraction systems tested and compared on a stool sample collection. Diagnost. Microbiol. Infecti. Dis.69, 240–244 (2011). ArticleCAS Google Scholar
Yuan, S., Cohen, D. B., Ravel, J., Abdo, Z. & Forney, L. J. Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS ONE7, e33865 (2012). ArticleCASPubMedPubMed Central Google Scholar
Li, F., Hullar, M. A. & Lampe, J. W. Optimization of terminal restriction fragment polymorphism (TRFLP) analysis of human gut microbiota. J. Microbiol. Methods68, 303–311 (2007). ArticleCASPubMed Google Scholar
Ariefdjohan, M. W., Savaiano, D. A. & Nakatsu, C. H. Comparison of DNA extraction kits for PCR-DGGE analysis of human intestinal microbial communities from fecal specimens. Nutr. J.9, 23 (2010). ArticleCASPubMedPubMed Central Google Scholar
Becker, L., Steglich, M., Fuchs, S., Werner, G. & Nubel, U. Comparison of six commercial kits to extract bacterial chromosome and plasmid DNA for MiSeq sequencing. Scientif. Rep.6, 28063 (2016). ArticleCAS Google Scholar
Mirsepasi, H. et al. Microbial diversity in fecal samples depends on DNA extraction method: easyMag DNA extraction compared to QIAamp DNA stool mini kit extraction. BMC Res. Notes7, 50 (2014). ArticleCASPubMedPubMed Central Google Scholar
Wesolowska-Andersen, A. et al. Choice of bacterial DNA extraction method from fecal material influences community structure as evaluated by metagenomic analysis. Microbiome2, 19 (2014). ArticlePubMedPubMed Central Google Scholar
Hart, M. L., Meyer, A., Johnson, P. J. & Ericsson, A. C. Comparative evaluation of DNA extraction methods from feces of multiple host species for downstream next-generation sequencing. PLoS ONE10, e0143334 (2015). ArticleCASPubMedPubMed Central Google Scholar
Kennedy, N. A. et al. The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing. PLoS ONE9, e88982 (2014). ArticleCASPubMedPubMed Central Google Scholar
Lauder, A. P. et al. Comparison of placenta samples with contamination controls does not provide evidence for a distinct placenta microbiota. Microbiome4, 29 (2016). ArticlePubMedPubMed Central Google Scholar
Perez-Munoz, M. E., Arrieta, M. C., Ramer-Tait, A. E. & Walter, J. A critical assessment of the “sterile womb” and “in utero colonization” hypotheses: implications for research on the pioneer infant microbiome. Microbiome5, 48 (2017). ArticlePubMedPubMed Central Google Scholar
Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nature reviews. Genetics17, 333–351 (2016). CASPubMed Google Scholar
Treangen, T. J. et al. MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome Biol.14, R2 (2013). ArticlePubMedPubMed Central Google Scholar
Namiki, T., Hachiya, T., Tanaka, H. & Sakakibara, Y. MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads. Nucleic Acids Res.40, e155 (2012). ArticleCASPubMedPubMed Central Google Scholar
Peng, Y., Leung, H. C., Yiu, S. M. & Chin, F. Y. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics28, 1420–1428 (2012). ArticleCASPubMed Google Scholar
Afiahayati, Sato, K. & Sakakibara, Y. MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Res.: Int. J. Rapid Publ. Rep. Genes Genomes22, 69–77 (2015). ArticleCAS Google Scholar
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 Bioinformat.5, 163 (2004). ArticleCAS Google Scholar
Patil, K. R., Roune, L. & McHardy, A. C. The PhyloPythiaS web server for taxonomic assignment of metagenome sequences. PLoS ONE7, e38581 (2012). ArticleCASPubMedPubMed Central Google Scholar
Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol.15, R46 (2014). ArticlePubMedPubMed Central Google Scholar
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nature Methods12, 902–903 (2015). ArticleCASPubMed Google Scholar
Brady, A. & Salzberg, S. L. Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models. Nature Methods6, 673–676 (2009). ArticleCASPubMedPubMed Central Google Scholar
Wang, Y., Leung, H., Yiu, S. & Chin, F. MetaCluster-TA: taxonomic annotation for metagenomic data based on assembly-assisted binning. BMC Genom.15 (Suppl. 1), S12 (2014). Article Google Scholar
Wu, M. & Scott, A. J. Phylogenomic analysis of bacterial and archaeal sequences with AMPHORA2. Bioinformatics28, 1033–1034 (2012). ArticleCASPubMed Google Scholar
Lin, H. H. & Liao, Y. C. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Scientif. Rep.6, 24175 (2016). ArticleCAS Google Scholar
Peabody, M. A., Van Rossum, T., Lo, R. & Brinkman, F. S. Evaluation of shotgun metagenomics sequence classification methods using in silico and in vitro simulated communities. BMC Bioinformat.16, 363 (2015). ArticleCAS Google Scholar
Ounit, R., Wanamaker, S., Close, T. J. & Lonardi, S. CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers. BMC Genom.16, 236 (2015). ArticleCAS Google Scholar
Lindgreen, S., Adair, K. L. & Gardner, P. P. An evaluation of the accuracy and speed of metagenome analysis tools. Scientif. Rep.6, 19233 (2016). ArticleCAS Google Scholar
Hyatt, D., LoCascio, P. F., Hauser, L. J. & Uberbacher, E. C. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics28, 2223–2230 (2012). ArticleCASPubMed Google Scholar
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol.215, 403–410 (1990). ArticleCASPubMed Google Scholar
Huerta-Cepas, J. et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res.44, D286–D293 (2016). ArticleCASPubMed Google Scholar
Hunter, S. et al. InterPro: the integrative protein signature database. Nucleic Acids Res.37, D211–D215 (2009). ArticleCASPubMed Google Scholar
Meyer, F. et al. The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformat.9, 386 (2008). ArticleCAS Google Scholar
Seshadri, R., Kravitz, S. A., Smarr, L., Gilna, P. & Frazier, M. CAMERA: a community resource for metagenomics. PLoS Biol.5, e75 (2007). ArticleCASPubMedPubMed Central Google Scholar
Hunter, S. et al. EBI metagenomics—a new resource for the analysis and archiving of metagenomic data. Nucleic Acids Res.42, D600–606 (2014). ArticleCASPubMed Google Scholar
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol.75, 7537–7541 (2009). ArticleCASPubMedPubMed Central Google Scholar
Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods10, 996–998 (2013). ArticleCASPubMed Google Scholar
Plummer, E., Twin, J., Bulach, D. M., Garland, S. M. & Tabrizi, S. N. A comparison of three bioinformatics pipelines for the analysis of preterm gut microbiota using 16S rRNA gene sequencing data. J. Proteomics Bioinform, 8, 283–291 (2015). Article Google Scholar
Westcott, S. L. & Schloss, P. D. de novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units. PeerJ3, e1487 (2015). ArticleCASPubMedPubMed Central Google Scholar
Jervis-Bardy, J. et al. Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome3, 19 (2015). ArticlePubMedPubMed Central Google Scholar
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics27, 2194–2200 (2011). ArticleCASPubMedPubMed Central Google Scholar
Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res.21, 494–504 (2011). ArticleCASPubMedPubMed Central Google Scholar
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol.73, 5261–5267 (2007). ArticleCASPubMedPubMed Central Google Scholar
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res.41, D590–D596 (2013). ArticleCASPubMed Google Scholar
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol.72, 5069–5072 (2006). ArticleCASPubMedPubMed Central Google Scholar
Koljalg, U. et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. New Phytol.166, 1063–1068 (2005). ArticleCASPubMed Google Scholar
Allard, G., Ryan, F. J., Jeffery, I. B. & Claesson, M. J. SPINGO: a rapid species-classifier for microbial amplicon sequences. BMC Bioinformat.16, 324 (2015). ArticleCAS Google Scholar
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics26, 2460–2461 (2010). ArticleCASPubMed Google Scholar
Chen, W., Zhang, C. K., Cheng, Y., Zhang, S. & Zhao, H. A comparison of methods for clustering 16S rRNA sequences into OTUs. PLoS ONE8, e70837 (2013). ArticleCASPubMedPubMed Central Google Scholar
Huson, D. H. et al. MEGAN Community Edition — Interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Computat. Biol.12, e1004957 (2016). ArticleCAS Google Scholar
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE8, e61217 (2013). ArticleCASPubMedPubMed Central Google Scholar
Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnol.31, 814–821 (2013). ArticleCAS Google Scholar
Asshauer, K. P., Wemheuer, B., Daniel, R. & Meinicke, P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics31, 2882–2884 (2015). ArticleCASPubMedPubMed Central Google Scholar
van Nood, E. et al. Duodenal infusion of donor feces for recurrent Clostridium difficile. N. Engl. J. Med.368, 407–415 (2013). ArticleCASPubMed Google Scholar
Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature500, 541–546 (2013). ArticleCASPubMed Google Scholar
Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature488, 178–184 (2012). ArticleCASPubMed Google Scholar
US Food and Drug Administration. Early Clinical Trials With Live Biotherapeutic Products: Chemistry, Manufacturing, and Control Information; Guidance for Industry (FDA, 2016).
Goldberg, B., Sichtig, H., Geyer, C., Ledeboer, N. & Weinstock, G. M. Making the leap from research laboratory to clinic: challenges and opportunities for next-generation sequencing in infectious disease diagnostics. mBio6, e01888–e01815 (2015). ArticleCASPubMedPubMed Central Google Scholar
Wilson, M. R. et al. Acute west nile virus meningoencephalitis diagnosed via metagenomic deep sequencing of cerebrospinal fluid in a renal transplant patient. Am. J. Transplant.http://dx.doi.org/10.1111/ajt.14058 (2016).
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell163, 1079–1094 (2015). ArticleCASPubMed Google Scholar
Bauer, E., Laczny, C. C., Magnusdottir, S., Wilmes, P. & Thiele, I. Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires. Microbiome3, 55 (2015). ArticlePubMedPubMed Central Google Scholar
Heinken, A. & Thiele, I. Systems biology of host-microbe metabolomics. Wiley Interdiscip. Rev. Syst. Biol. Med.7, 195–219 (2015). ArticlePubMedPubMed Central Google Scholar
Zhou, Q., Su, X., Jing, G. & Ning, K. Meta-QC-Chain: comprehensive and fast quality control method for metagenomic data. Genom. Proteom. Bioinformat.12, 52–56 (2014). Article Google Scholar
Peng, Y., Leung, H. C., Yiu, S. M. & Chin, F. Y. Meta-IDBA: a de novo assembler for metagenomic data. Bioinformatics27, i94–i101 (2011). ArticleCASPubMedPubMed Central Google Scholar
Boisvert, S., Raymond, F., Godzaridis, E., Laviolette, F. & Corbeil, J. Ray Meta: scalable de novo metagenome assembly and profiling. Genome Biol.13, R122 (2012). ArticleCASPubMedPubMed Central Google Scholar
Li, D., Liu, C. M., Luo, R., Sadakane, K. & Lam, T. W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics31, 1674–1676 (2015). ArticleCASPubMed Google Scholar
Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ3, e1165 (2015). ArticleCASPubMedPubMed Central Google Scholar
Haider, B. et al. Omega: an overlap-graph de novo assembler for metagenomics. Bioinformatics30, 2717–2722 (2014). ArticleCASPubMed Google Scholar
Reddy, R. M., Mohammed, M. H. & Mande, S. S. MetaCAA: a clustering-aided methodology for efficient assembly of metagenomic datasets. Genomics103, 161–168 (2014). ArticleCASPubMed Google Scholar
Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. metaSPAdes: a new versatile de novo metagenomics assembler. arXiv 1604.03071 (2016).
Ye, Y. & Tang, H. An ORFome assembly approach to metagenomics sequences analysis. J. Bioinformat. Computat. Biol.7, 455–471 (2009). ArticleCAS Google Scholar
Yu, F., Sun, Y., Liu, L. & Farmerie, W. GSTaxClassifier: a genomic signature based taxonomic classifier for metagenomic data analysis. Bioinformation4, 46–49 (2010). Article Google Scholar
Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nature Methods10, 1196–1199 (2013). ArticleCASPubMed Google Scholar
Diaz, N. N., Krause, L., Goesmann, A., Niehaus, K. & Nattkemper, T. W. TACOA: taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach. BMC Bioinformat.10, 56 (2009). ArticleCAS Google Scholar
Rosen, G. L., Reichenberger, E. R. & Rosenfeld, A. M. NBC: the Naive Bayes Classification tool webserver for taxonomic classification of metagenomic reads. Bioinformatics27, 127–129 (2011). ArticleCASPubMed Google Scholar
Stark, M., Berger, S. A., Stamatakis, A. & von Mering, C. MLTreeMap—accurate Maximum Likelihood placement of environmental DNA sequences into taxonomic and functional reference phylogenies. BMC Genom.11, 461 (2010). ArticleCAS Google Scholar
Freitas, T. A., Li, P. E., Scholz, M. B. & Chain, P. S. Accurate read-based metagenome characterization using a hierarchical suite of unique signatures. Nucleic Acids Res.43, e69 (2015). ArticleCASPubMedPubMed Central Google Scholar
Ames, S. K. et al. Scalable metagenomic taxonomy classification using a reference genome database. Bioinformatics29, 2253–2260 (2013). ArticleCASPubMedPubMed Central Google Scholar
Droge, J., Gregor, I. & McHardy, A. C. Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods. Bioinformatics31, 817–824 (2015). ArticleCASPubMed Google Scholar
MacDonald, N. J., Parks, D. H. & Beiko, R. G. Rapid identification of high-confidence taxonomic assignments for metagenomic data. Nucleic Acids Res.40, e111 (2012). ArticleCASPubMedPubMed Central Google Scholar
Monzoorul Haque, M., Ghosh, T. S., Komanduri, D. & Mande, S. S. SOrt-ITEMS: sequence orthology based approach for improved taxonomic estimation of metagenomic sequences. Bioinformatics25, 1722–1730 (2009). ArticleCASPubMed Google Scholar
Mohammed, M. H., Ghosh, T. S., Singh, N. K. & Mande, S. S. SPHINX—an algorithm for taxonomic binning of metagenomic sequences. Bioinformatics27, 22–30 (2011). ArticleCASPubMed Google Scholar
Nalbantoglu, O. U., Way, S. F., Hinrichs, S. H. & Sayood, K. RAIphy: phylogenetic classification of metagenomics samples using iterative refinement of relative abundance index profiles. BMC Bioinformat.12, 41 (2011). Article Google Scholar
Chan, C. K., Hsu, A. L., Halgamuge, S. K. & Tang, S. L. Binning sequences using very sparse labels within a metagenome. BMC Bioinformat.9, 215 (2008). ArticleCAS Google Scholar
Schreiber, F., Gumrich, P., Daniel, R. & Meinicke, P. Treephyler: fast taxonomic profiling of metagenomes. Bioinformatics26, 960–961 (2010). ArticleCASPubMed Google Scholar
Weber, M. et al. Practical application of self-organizing maps to interrelate biodiversity and functional data in NGS-based metagenomics. ISME J.5, 918–928 (2011). ArticleCASPubMed Google Scholar
Pati, A., Heath, L. S., Kyrpides, N. C. & Ivanova, N. ClaMS: a classifier for metagenomic sequences. Standards Genom. Sci.5, 248–253 (2011). Article Google Scholar
Sharma, A. K., Gupta, A., Kumar, S., Dhakan, D. B. & Sharma, V. K. Woods: a fast and accurate functional annotator and classifier of genomic and metagenomic sequences. Genomics106, 1–6 (2015). ArticleCASPubMed Google Scholar
Ghosh, T. S., Monzoorul Haque, M. & Mande, S. S. DiScRIBinATE: a rapid method for accurate taxonomic classification of metagenomic sequences. BMC Bioinformat.11 (Suppl. 7), S14 (2010). Article Google Scholar
Liu, J. et al. Composition-based classification of short metagenomic sequences elucidates the landscapes of taxonomic and functional enrichment of microorganisms. Nucleic Acids Res.41, e3 (2013). ArticleCASPubMed Google Scholar
Mohammed, M. H. et al. INDUS - a composition-based approach for rapid and accurate taxonomic classification of metagenomic sequences. BMC Genom.12 (Suppl. 3), S4 (2011). Article Google Scholar
Sharma, V. K., Kumar, N., Prakash, T. & Taylor, T. D. Fast and accurate taxonomic assignments of metagenomic sequences using MetaBin. PLoS ONE7, e34030 (2012). ArticleCASPubMedPubMed Central Google Scholar
Liu, B., Gibbons, T., Ghodsi, M., Treangen, T. & Pop, M. Accurate and fast estimation of taxonomic profiles from metagenomic shotgun sequences. BMC Genom.12 (Suppl. 2), S4 (2011). ArticleCAS Google Scholar
Rasheed, Z. & Rangwala, H. Metagenomic taxonomic classification using extreme learning machines. J. Bioinformat. Computat. Biol.10, 1250015 (2012). Article Google Scholar
Ander, C., Schulz-Trieglaff, O. B., Stoye, J. & Cox, A. J. metaBEETL: high-throughput analysis of heterogeneous microbial populations from shotgun DNA sequences. BMC Bioinformat.14 (Suppl. 5), S2 (2013). Article Google Scholar
Porter, M. S. & Beiko, R. G. SPANNER: taxonomic assignment of sequences using pyramid matching of similarity profiles. Bioinformatics29, 1858–1864 (2013). ArticleCASPubMedPubMed Central Google Scholar
Piro, V. C., Lindner, M. S. & Renard, B. Y. DUDes: a top-down taxonomic profiler for metagenomics. Bioinformatics32, 2272–2280 (2016). ArticleCASPubMed Google Scholar
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nature Commun.7, 11257 (2016). ArticleCAS Google Scholar
Wu, Y. W., Tang, Y. H., Tringe, S. G., Simmons, B. A. & Singer, S. W. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. Microbiome2, 26 (2014). ArticleCASPubMedPubMed Central Google Scholar
Petrenko, P., Lobb, B., Kurtz, D. A., Neufeld, J. D. & Doxey, A. C. MetAnnotate: function-specific taxonomic profiling and comparison of metagenomes. BMC Biol.13, 92 (2015). ArticleCASPubMedPubMed Central Google Scholar
Luo, C., Rodriguez, R. L. & Konstantinidis, K. T. MyTaxa: an advanced taxonomic classifier for genomic and metagenomic sequences. Nucleic Acids Res.42, e73 (2014). ArticleCASPubMedPubMed Central Google Scholar
Jiang, H., An, L., Lin, S. M., Feng, G. & Qiu, Y. A statistical framework for accurate taxonomic assignment of metagenomic sequencing reads. PLoS ONE7, e46450 (2012). ArticleCASPubMedPubMed Central Google Scholar
Klingenberg, H., Asshauer, K. P., Lingner, T. & Meinicke, P. Protein signature-based estimation of metagenomic abundances including all domains of life and viruses. Bioinformatics29, 973–980 (2013). ArticleCASPubMedPubMed Central Google Scholar
Reddy, R. M., Mohammed, M. H. & Mande, S. S. TWARIT: an extremely rapid and efficient approach for phylogenetic classification of metagenomic sequences. Gene505, 259–265 (2012). ArticleCASPubMed Google Scholar
Hou, T. et al. Classification of metagenomics data at lower taxonomic level using a robust supervised classifier. Evol. Bioinformat. Online11, 3–10 S20523 (2015). CAS Google Scholar
Kristiansson, E., Hugenholtz, P. & Dalevi, D. ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes. Bioinformatics25, 2737–2738 (2009). ArticleCASPubMed Google Scholar
Li, W. Analysis and comparison of very large metagenomes with fast clustering and functional annotation. BMC Bioinformat.10, 359 (2009). ArticleCAS Google Scholar
Kelley, D. R., Liu, B., Delcher, A. L., Pop, M. & Salzberg, S. L. Gene prediction with Glimmer for metagenomic sequences augmented by classification and clustering. Nucleic Acids Res.40, e9 (2012). ArticleCASPubMed Google Scholar
Hoff, K. J., Lingner, T., Meinicke, P. & Tech, M. Orphelia: predicting genes in metagenomic sequencing reads. Nucleic Acids Res.37, W101–W105 (2009). ArticleCASPubMedPubMed Central Google Scholar
Liu, Y., Guo, J., Hu, G. & Zhu, H. Gene prediction in metagenomic fragments based on the SVM algorithm. BMC Bioinformat.14 (Suppl. 5), S12 (2013). Article Google Scholar
van der Veen, B. E., Harris, H. M., O'Toole, P. W. & Claesson, M. J. Metaphor: finding bi-directional best hit homology relationships in (meta)genomic datasets. Genomics104, 459–463 (2014). ArticleCASPubMed Google Scholar
Liu, B. & Pop, M. MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets. BMC Proc.5 (Suppl. 2), S9 (2011). ArticlePubMedPubMed Central Google Scholar
Overbeek, R. et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res.42, D206–D214 (2014). ArticleCASPubMed Google Scholar
Powell, S. et al. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res.42, D231–D239 (2014). ArticleCASPubMed Google Scholar
Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Computat. Biol.8, e1002358 (2012). ArticleCAS Google Scholar
Markowitz, V. M. et al. IMG/M 4 version of the integrated metagenome comparative analysis system. Nucleic Acids Res.42, D568–573 (2014). ArticleCASPubMed Google Scholar
Wu, S., Zhu, Z., Fu, L., Niu, B. & Li, W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genom.12, 444 (2011). Article Google Scholar
Goll, J. et al. METAREP: JCVI metagenomics reports—an open source tool for high-performance comparative metagenomics. Bioinformatics26, 2631–2632 (2010). ArticleCASPubMedPubMed Central Google Scholar
Su, X., Pan, W., Song, B., Xu, J. & Ning, K. Parallel-META 2.0: enhanced metagenomic data analysis with functional annotation, high performance computing and advanced visualization. PloS one9, e89323 (2014). ArticleCASPubMedPubMed Central Google Scholar
Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res.44, W3–W10 (2016). ArticleCASPubMedPubMed Central Google Scholar
Fosso, B. et al. BioMaS: a modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS. BMC Bioinformat.16, 203 (2015). Article Google Scholar
Angly, F. et al. PHACCS, an online tool for estimating the structure and diversity of uncultured viral communities using metagenomic information. BMC Bioinformat.6, 41 (2005). ArticleCAS Google Scholar
Arumugam, M., Harrington, E. D., Foerstner, K. U., Raes, J. & Bork, P. SmashCommunity: a metagenomic annotation and analysis tool. Bioinformatics26, 2977–2978 (2010). ArticleCASPubMed Google Scholar
Schloss, P. D. & Handelsman, J. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl. Environ. Microbiol.71, 1501–1506 (2005). ArticleCASPubMedPubMed Central Google Scholar
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics22, 1658–1659 (2006). ArticleCASPubMed Google Scholar
Hao, X., Jiang, R. & Chen, T. Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering. Bioinformatics27, 611–618 (2011). ArticleCASPubMedPubMed Central Google Scholar
Cai, Y. & Sun, Y. ESPRIT-Tree: hierarchical clustering analysis of millions of 16S rRNA pyrosequences in quasilinear computational time. Nucleic Acids Res.39, e95 (2011). ArticleCASPubMedPubMed Central Google Scholar
Ghodsi, M., Liu, B. & Pop, M. DNACLUST: accurate and efficient clustering of phylogenetic marker genes. BMC Bioinformat.12, 271 (2011). Article Google Scholar
Russell, D. J., Way, S. F., Benson, A. K. & Sayood, K. A grammar-based distance metric enables fast and accurate clustering of large sets of 16S sequences. BMC Bioinformat.11, 601 (2010). Article Google Scholar
Wang, X., Yao, J., Sun, Y. & Mai, V. M-Pick, a modularity-based method for OTU picking of 16S rRNA sequences. BMC Bioinformat.14, 43 (2013). Article Google Scholar
Mahe, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ3, e1420 (2015). ArticlePubMedPubMed Central Google Scholar
Franzen, O. et al. Improved OTU-picking using long-read 16S rRNA gene amplicon sequencing and generic hierarchical clustering. Microbiome3, 43 (2015). ArticlePubMedPubMed Central Google Scholar
Wei, Z. G. & Zhang, S. W. MtHc: a motif-based hierarchical method for clustering massive 16S rRNA sequences into OTUs. Mol. bioSystems11, 1907–1913 (2015). ArticleCAS Google Scholar
Mysara, M., Saeys, Y., Leys, N., Raes, J. & Monsieurs, P. CATCh, an ensemble classifier for chimera detection in 16S rRNA sequencing studies. Appl. Environ. Microbiol.81, 1573–1584 (2015). ArticlePubMedPubMed Central Google Scholar
Soergel, D. A., Dey, N., Knight, R. & Brenner, S. E. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J.6, 1440–1444 (2012). ArticleCASPubMedPubMed Central Google Scholar
Chaudhary, N., Sharma, A. K., Agarwal, P., Gupta, A. & Sharma, V. K. 16S classifier: a tool for fast and accurate taxonomic classification of 16S rRNA hypervariable regions in metagenomic datasets. PLoS ONE10, e0116106 (2015). ArticleCASPubMedPubMed Central Google Scholar
Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res.43, D593–D598 (2015). ArticleCASPubMed Google Scholar
Jaziri, F. et al. PhylOPDb: a 16S rRNA oligonucleotide probe database for prokaryotic identification. Database (Oxford)http://dx.doi.org/10.1093/database/bau036 (2014).
Ritari, J., Salojarvi, J., Lahti, L. & de Vos, W. M. Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database. BMC Genom.16, 1056 (2015). ArticleCAS Google Scholar
Lozupone, C., Hamady, M. & Knight, R. UniFrac—an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformat.7, 371 (2006). ArticleCAS Google Scholar
Gilmore, R. D., Cieplak, W., Policastro, P. F. & Hackstadt, T. The 120 kilodalton outer membrane (rOmpB) of Rickettsia rickettsii is encoded by an unusually long open reading frame: evidence for protein processing from a large precursor. Mol. Microbiol.5, 2361–2370 (1991). ArticleCASPubMed Google Scholar
Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nature Methods10, 1200–1202 (2013). ArticleCASPubMedPubMed Central Google Scholar
Angly, F. E. et al. CopyRighter: a rapid tool for improving the accuracy of microbial community profiles through lineage-specific gene copy number correction. Microbiome2, 11 (2014). ArticlePubMedPubMed Central Google Scholar
Beck, D., Settles, M. & Foster, J. A. OTUbase: an R infrastructure package for operational taxonomic unit data. Bioinformatics27, 1700–1701 (2011). ArticleCASPubMedPubMed Central Google Scholar
Seguritan, V. & Rohwer, F. FastGroup: a program to dereplicate libraries of 16S rDNA sequences. BMC Bioinformat.2, 9 (2001). ArticleCAS Google Scholar
Kumar, S. et al. CLOTU: an online pipeline for processing and clustering of 454 amplicon reads into OTUs followed by taxonomic annotation. BMC Bioinformat.12, 182 (2011). Article Google Scholar
Nebel, M. E. et al. JAGUC—a software package for environmental diversity analyses. J. Bioinformat. Computat. Biol.9, 749–773 (2011). Article Google Scholar
Albanese, D., Fontana, P., De Filippo, C., Cavalieri, D. & Donati, C. MICCA: a complete and accurate software for taxonomic profiling of metagenomic data. Scientif. Rep.5, 9743 (2015). ArticleCAS Google Scholar
Weisman, D., Yasuda, M. & Bowen, J. L. FunFrame: functional gene ecological analysis pipeline. Bioinformatics29, 1212–1214 (2013). ArticleCASPubMed Google Scholar