Best practices for analysing microbiomes

[1]  Theodore Alexandrov,et al.  3D molecular cartography using LC–MS facilitated by Optimus and 'ili software , 2017, Nature Protocols.

[2]  Zhibin Ning,et al.  MetaLab: an automated pipeline for metaproteomic data analysis , 2017, Microbiome.

[3]  Charis M. Thompson.  Collect reliable data on embryo selection , 2017, Nature.

[4]  Curtis Huttenhower,et al.  A Bayesian method for detecting pairwise associations in compositional data , 2017, PLoS Comput. Biol..

[5]  Rick L. Stevens,et al.  A communal catalogue reveals Earth’s multiscale microbial diversity , 2017, Nature.

[6]  Jun Wang,et al.  Quantitative microbiome profiling links gut community variation to microbial load , 2017, Nature.

[7]  D. Or,et al.  Resolving Species Level Changes in a Representative Soil Bacterial Community Using Microfluidic Quantitative PCR , 2017, Front. Microbiol..

[8]  Luis Pedro Coelho,et al.  Towards standards for human fecal sample processing in metagenomic studies , 2017, Nature Biotechnology.

[9]  C. Huttenhower,et al.  Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium , 2017, Nature Biotechnology.

[10]  N. Segata,et al.  Shotgun metagenomics, from sampling to analysis , 2017, Nature Biotechnology.

[11]  Erica M. Hartmann,et al.  Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems , 2017, Microbiome.

[12]  Paul J. McMurdie,et al.  Exact sequence variants should replace operational taxonomic units in marker-gene data analysis , 2017, The ISME Journal.

[13]  Stefano Lonardi,et al.  Comprehensive benchmarking and ensemble approaches for metagenomic classifiers , 2017, Genome Biology.

[14]  Natalia N. Ivanova,et al.  1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life , 2017, Nature Biotechnology.

[15]  Philip D. Blood,et al.  Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software , 2017, Nature Methods.

[16]  Trey Ideker,et al.  Systems biology guided by XCMS Online metabolomics , 2017, Nature Methods.

[17]  B. Bohannan,et al.  The role of adaptive immunity as an ecological filter on the gut microbiota in zebrafish , 2017, The ISME Journal.

[18]  Jose A Navas-Molina,et al.  Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns , 2017, mSystems.

[19]  Jose A Navas-Molina,et al.  Correcting for Microbial Blooms in Fecal Samples during Room-Temperature Shipping , 2017, mSystems.

[20]  Jesse R. Zaneveld,et al.  Normalization and microbial differential abundance strategies depend upon data characteristics , 2017, Microbiome.

[21]  Joon-Yong Lee,et al.  ATLAS (Automatic Tool for Local Assembly Structures) - a comprehensive infrastructure for assembly, annotation, and genomic binning of metagenomic and metatranscriptomic data , 2017, PeerJ Prepr..

[22]  Rurun Wang,et al.  Opinion: Hijacking exogenous signals to generate new secondary metabolites during symbiotic interactions , 2017 .

[23]  Brian C. Thomas,et al.  The Source and Evolutionary History of a Microbial Contaminant Identified Through Soil Metagenomic Analysis , 2017, mBio.

[24]  Lawrence A. David,et al.  Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets , 2017, PeerJ.

[25]  Lisa M Bramer,et al.  Dynamics of the human gut microbiome in Inflammatory Bowel Disease , 2017, Nature Microbiology.

[26]  John Vollmers,et al.  Comparing and Evaluating Metagenome Assembly Tools from a Microbiologist’s Perspective - Not Only Size Matters! , 2017, PloS one.

[27]  Jose A Navas-Molina,et al.  Balance Trees Reveal Microbial Niche Differentiation , 2017, mSystems.

[28]  Dominique Lavenier,et al.  Critical Assessment of Metagenome Interpretation – a benchmark of computational metagenomics software , 2017, bioRxiv.

[29]  Young-Mo Kim,et al.  Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome , 2016, Nature Microbiology.

[30]  A. Heintz‐Buschart,et al.  IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses , 2016, Genome Biology.

[31]  Larry Smarr,et al.  Using machine learning to identify major shifts in human gut microbiome protein family abundance in disease , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[32]  Se Jin Song,et al.  Tiny microbes, enormous impacts: what matters in gut microbiome studies? , 2016, Genome Biology.

[33]  S. Salzberg,et al.  Centrifuge: rapid and sensitive classification of metagenomic sequences , 2016, bioRxiv.

[34]  Lawrence A. David,et al.  A phylogenetic transform enhances analysis of compositional microbiota data , 2016, bioRxiv.

[35]  Kristian Fog Nielsen,et al.  Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking , 2016, Nature Biotechnology.

[36]  Sophie J. Weiss,et al.  Correlation detection strategies in microbial data sets vary widely in sensitivity and precision , 2016, The ISME Journal.

[37]  Daniel H. Huson,et al.  MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data , 2016, PLoS Comput. Biol..

[38]  Shyamal D Peddada,et al.  Immunization with a heat-killed preparation of the environmental bacterium Mycobacterium vaccae promotes stress resilience in mice , 2016, Proceedings of the National Academy of Sciences.

[39]  David S. Wishart,et al.  PHASTER: a better, faster version of the PHAST phage search tool , 2016, Nucleic Acids Res..

[40]  Amnon Amir,et al.  Preservation Methods Differ in Fecal Microbiome Stability, Affecting Suitability for Field Studies , 2016, mSystems.

[41]  Paul J. McMurdie,et al.  DADA2: High resolution sample inference from Illumina amplicon data , 2016, Nature Methods.

[42]  Duy Tin Truong,et al.  Strain-level microbial epidemiology and population genomics from shotgun metagenomics , 2016, Nature Methods.

[43]  Geoffrey D. Hannigan,et al.  Skin Microbiome Surveys Are Strongly Influenced by Experimental Design. , 2016, The Journal of investigative dermatology.

[44]  Yves Moreau,et al.  Candidate gene prioritization with Endeavour , 2016, Nucleic Acids Res..

[45]  J. Raes,et al.  Population-level analysis of gut microbiome variation , 2016, Science.

[46]  John H. Chase,et al.  Geography and Location Are the Primary Drivers of Office Microbiome Composition , 2016, mSystems.

[47]  Brian C. Thomas,et al.  A new view of the tree of life , 2016, Nature Microbiology.

[48]  N. Fierer,et al.  Relic DNA is abundant in soil and obscures estimates of soil microbial diversity , 2016, Nature Microbiology.

[49]  J. Gilbert,et al.  Recovering complete and draft population genomes from metagenome datasets , 2016, Microbiome.

[50]  A. Margolles,et al.  Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health , 2016, Front. Microbiol..

[51]  Blake A. Simmons,et al.  MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets , 2016, Bioinform..

[52]  P. Bork,et al.  Gut Microbiota Linked to Sexual Preference and HIV Infection , 2016, EBioMedicine.

[53]  Matthew J. Gebert,et al.  Microbial community assembly and metabolic function during mammalian corpse decomposition , 2016, Science.

[54]  Eran Elinav,et al.  Use of Metatranscriptomics in Microbiome Research , 2016, Bioinformatics and biology insights.

[55]  Robert D. Finn,et al.  The Pfam protein families database: towards a more sustainable future , 2015, Nucleic Acids Res..

[56]  Barbara A. Bailey,et al.  Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome , 2015, The ISME Journal.

[57]  Wen J. Li,et al.  Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation , 2015, Nucleic Acids Res..

[58]  F. Freimoser,et al.  Tritagonist as a new term for uncharacterised microorganisms in environmental systems , 2015, The ISME Journal.

[59]  Haixu Tang,et al.  Utilizing de Bruijn graph of metagenome assembly for metatranscriptome analysis , 2015, Bioinform..

[60]  Christophe Croux,et al.  Robust sparse canonical correlation analysis , 2015, BMC Systems Biology.

[61]  Tim D Spector,et al.  Proton pump inhibitors alter the composition of the gut microbiota , 2015, Gut.

[62]  Jun Wang,et al.  Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota , 2015, Nature.

[63]  R. Knight,et al.  Context and the human microbiome , 2015, Microbiome.

[64]  E. Balskus.  Colibactin: understanding an elusive gut bacterial genotoxin. , 2015, Natural product reports.

[65]  Jens Roat Kultima,et al.  Disentangling the effects of type 2 diabetes and metformin on the human gut microbiota , 2016 .

[66]  Tom O. Delmont,et al.  Anvi’o: an advanced analysis and visualization platform for ‘omics data , 2015, PeerJ.

[67]  James E Johnson,et al.  Metaproteomic analysis using the Galaxy framework , 2015, Proteomics.

[68]  T. R. Licht,et al.  A catalog of the mouse gut metagenome , 2015, Nature Biotechnology.

[69]  Duy Tin Truong,et al.  MetaPhlAn2 for enhanced metagenomic taxonomic profiling , 2015, Nature Methods.

[70]  Hongyu Zhao,et al.  CCLasso: correlation inference for compositional data through Lasso , 2015, Bioinform..

[71]  C. Schadt,et al.  PanFP: pangenome-based functional profiles for microbial communities , 2015, BMC Research Notes.

[72]  Amnon Amir,et al.  Prediction of Early Childhood Caries via Spatial-Temporal Variations of Oral Microbiota. , 2015, Cell host & microbe.

[73]  Eran Segal,et al.  Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples , 2015, Science.

[74]  Hongzhe Li,et al.  Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA , 2015, Bioinform..

[75]  Connor T. Skennerton,et al.  CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes , 2015, Genome research.

[76]  Karen P. Scott,et al.  16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice , 2015, Microbiome.

[77]  S. Canizales-Quinteros,et al.  Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: towards a systems-level understanding of human microbiome , 2015, Computational and structural biotechnology journal.

[78]  Rob Knight,et al.  Analysis of composition of microbiomes: a novel method for studying microbial composition , 2015, Microbial ecology in health and disease.

[79]  Luis Pedro Coelho,et al.  Structure and function of the global ocean microbiome , 2015, Science.

[80]  Mark P. Waldrop,et al.  Multi-omics of permafrost, active layer and thermokarst bog soil microbiomes , 2015, Nature.

[81]  Peter Meinicke,et al.  Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data , 2015, Bioinform..

[82]  N. Isaac,et al.  Measuring β‐diversity with species abundance data , 2015, The Journal of animal ecology.

[83]  Ó. O’Sullivan,et al.  The Effects of Freezing on Faecal Microbiota as Determined Using MiSeq Sequencing and Culture-Based Investigations , 2015, PloS one.

[84]  Bart C. Weimer,et al.  Metabolome searcher: a high throughput tool for metabolite identification and metabolic pathway mapping directly from mass spectrometry and using genome restriction , 2015, BMC Bioinformatics.

[85]  Piotr Gawron,et al.  VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data , 2015, Microbiome.

[86]  Peter B. McGarvey,et al.  UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches , 2014, Bioinform..

[87]  Kunihiko Sadakane,et al.  MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph , 2014, Bioinform..

[88]  Christian L. Müller,et al.  Sparse and Compositionally Robust Inference of Microbial Ecological Networks , 2014, PLoS Comput. Biol..

[89]  K. Strissel,et al.  Modulation of gut microbiota during probiotic-mediated attenuation of metabolic syndrome in high fat diet-fed mice , 2014, The ISME Journal.

[90]  Peter Meinicke,et al.  Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data , 2015, Bioinform..

[91]  Molly K. Gibson,et al.  Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology , 2014, The ISME Journal.

[92]  Mihai Pop,et al.  TIPP: taxonomic identification and phylogenetic profiling , 2014, Bioinform..

[93]  Paul Turner,et al.  Reagent and laboratory contamination can critically impact sequence-based microbiome analyses , 2014, BMC Biology.

[94]  Anders F. Andersson,et al.  Binning metagenomic contigs by coverage and composition , 2014, Nature Methods.

[95]  Susannah G. Tringe,et al.  FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus , 2014, Nucleic acids research.

[96]  A. Butte,et al.  The Integrative Human Microbiome Project: Dynamic Analysis of Microbiome-Host Omics Profiles during Periods of Human Health and Disease , 2014, Cell host & microbe.

[97]  Rob Knight,et al.  Predictive modeling of gingivitis severity and susceptibility via oral microbiota , 2014, The ISME Journal.

[98]  Rob Knight,et al.  Longitudinal analysis of microbial interaction between humans and the indoor environment , 2014, Science.

[99]  Jürg Bähler,et al.  Proportionality: A Valid Alternative to Correlation for Relative Data , 2014, bioRxiv.

[100]  H. Lehrach,et al.  Influence of RNA extraction methods and library selection schemes on RNA-seq data , 2014, BMC Genomics.

[101]  T. Urich,et al.  Metatranscriptomic Analysis of Arctic Peat Soil Microbiota , 2014, Applied and Environmental Microbiology.

[102]  Qunyuan Zhang,et al.  Persistent Gut Microbiota Immaturity in Malnourished Bangladeshi Children , 2014, Nature.

[103]  C. Huttenhower,et al.  Relating the metatranscriptome and metagenome of the human gut , 2014, Proceedings of the National Academy of Sciences.

[104]  S. Tringe,et al.  Tackling soil diversity with the assembly of large, complex metagenomes , 2014, Proceedings of the National Academy of Sciences.

[105]  Bo Li,et al.  Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection , 2014, Nature Communications.

[106]  Robert Schmieder,et al.  Breath gas metabolites and bacterial metagenomes from cystic fibrosis airways indicate active pH neutral 2,3-butanedione fermentation , 2014, The ISME Journal.

[107]  Susan P. Holmes,et al.  Waste Not , Want Not : Why Rarefying Microbiome Data is Inadmissible . October 1 , 2013 , 2013 .

[108]  Jens Roat Kultima,et al.  Disentangling the effects of type 2 diabetes and metformin on the human gut microbiota , 2015, Nature.

[109]  Derrick E. Wood,et al.  Kraken: ultrafast metagenomic sequence classification using exact alignments , 2014, Genome Biology.

[110]  Rob Knight,et al.  EMPeror: a tool for visualizing high-throughput microbial community data , 2013, GigaScience.

[111]  Marti J. Anderson,et al.  PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: What null hypothesis are you testing? , 2013 .

[112]  Sharon L. Grim,et al.  Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data , 2013, Methods in ecology and evolution.

[113]  R. Knight,et al.  Meta-analyses of studies of the human microbiota , 2013, Genome research.

[114]  J. Clemente,et al.  Gut Microbiota from Twins Discordant for Obesity Modulate Metabolism in Mice , 2013, Science.

[115]  Jesse R. Zaneveld,et al.  Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences , 2013, Nature Biotechnology.

[116]  Paul M. Ruegger,et al.  Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships , 2013, Microbiome.

[117]  A. Kostic,et al.  Exploring host-microbiota interactions in animal models and humans. , 2013, Genes & development.

[118]  P. Turnbaugh,et al.  Xenobiotics Shape the Physiology and Gene Expression of the Active Human Gut Microbiome , 2013, Cell.

[119]  Paul Wilmes,et al.  A biomolecular isolation framework for eco-systems biology , 2012, The ISME Journal.

[120]  Daniel D. Sommer,et al.  MetAMOS: a modular and open source metagenomic assembly and analysis pipeline , 2013, Genome Biology.

[121]  C. Consolandi,et al.  An efficient rRNA removal method for RNA sequencing in GC-rich bacteria , 2013, Microbial Informatics and Experimentation.

[122]  David J. Edwards,et al.  Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data , 2012, PloS one.

[123]  Brandi L. Cantarel,et al.  Integrated Metagenomics/Metaproteomics Reveals Human Host-Microbiota Signatures of Crohn's Disease , 2012, PloS one.

[124]  Shujiro Okuda,et al.  Virtual metagenome reconstruction from 16S rRNA gene sequences , 2012, Nature Communications.

[125]  Jonathan Friedman,et al.  Inferring Correlation Networks from Genomic Survey Data , 2012, PLoS Comput. Biol..

[126]  Lynn K. Carmichael,et al.  Evaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research , 2012, PloS one.

[127]  Bernard Henrissat,et al.  Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome , 2012, PLoS Comput. Biol..

[128]  Katherine H. Huang,et al.  Structure, Function and Diversity of the Healthy Human Microbiome , 2012, Nature.

[129]  Sergey I. Nikolenko,et al.  SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing , 2012, J. Comput. Biol..

[130]  Jean Thioulouse,et al.  Multivariate analyses in soil microbial ecology: a new paradigm , 2012, Environmental and Ecological Statistics.

[131]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[132]  Katherine H. Huang,et al.  Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes , 2012, Genome Biology.

[133]  T. Fujii,et al.  Extraction of Bacterial RNA from Soil: Challenges and Solutions , 2012, Microbes and environments.

[134]  Rob Knight,et al.  Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences , 2012, The ISME Journal.

[135]  Peter Williams,et al.  IMG: the integrated microbial genomes database and comparative analysis system , 2011, Nucleic Acids Res..

[136]  Eric P. Nawrocki,et al.  An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea , 2011, The ISME Journal.

[137]  Jesse R. Zaneveld,et al.  Human-associated microbial signatures: examining their predictive value. , 2011, Cell host & microbe.

[138]  F. Bushman,et al.  Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes , 2011, Science.

[139]  T. Glenn.  Field guide to next‐generation DNA sequencers , 2011, Molecular ecology resources.

[140]  Rob Knight,et al.  Bayesian community-wide culture-independent microbial source tracking , 2011, Nature Methods.

[141]  Kai Blin,et al.  antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences , 2011, Nucleic Acids Res..

[142]  Emily S. Charlson,et al.  Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications , 2011, Nature Biotechnology.

[143]  R. Knight,et al.  Supervised classification of human microbiota. , 2011, FEMS microbiology reviews.

[144]  Christian L. Lauber,et al.  PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers , 2011, Bioinform..

[145]  D. Relman,et al.  Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation , 2010, Proceedings of the National Academy of Sciences.

[146]  R. Knight,et al.  Microbial community resemblance methods differ in their ability to detect biologically relevant patterns , 2010, Nature Methods.

[147]  William A. Walters,et al.  QIIME allows analysis of high-throughput community sequencing data , 2010, Nature Methods.

[148]  W. Huber,et al.  which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets , 2011 .

[149]  P. Bork,et al.  A human gut microbial gene catalogue established by metagenomic sequencing , 2010, Nature.

[150]  Rob Knight,et al.  Ribosomal RNA diversity predicts genome diversity in gut bacteria and their relatives , 2010, Nucleic acids research.

[151]  I. Wilson,et al.  Site and Strain-Specific Variation in Gut Microbiota Profiles and Metabolism in Experimental Mice , 2010, PloS one.

[152]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[153]  V. Kunin,et al.  Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. , 2009, Environmental microbiology.

[154]  R. Knight,et al.  Fast UniFrac: Facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data , 2009, The ISME Journal.

[155]  R. Knight,et al.  Bacterial Community Variation in Human Body Habitats Across Space and Time , 2009, Science.

[156]  Martin Hartmann,et al.  Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities , 2009, Applied and Environmental Microbiology.

[157]  Rob Knight,et al.  The 'rare biosphere': a reality check , 2009, Nature Methods.

[158]  Ignacio González,et al.  integrOmics: an R package to unravel relationships between two omics datasets , 2009, Bioinform..

[159]  W. R. Wikoff,et al.  Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites , 2009, Proceedings of the National Academy of Sciences.

[160]  Daniela M Witten,et al.  Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data , 2009, Statistical applications in genetics and molecular biology.

[161]  R. Knight,et al.  The influence of sex, handedness, and washing on the diversity of hand surface bacteria , 2008, Proceedings of the National Academy of Sciences.

[162]  J. Lindon,et al.  Systems biology: Metabonomics , 2008, Nature.

[163]  F. Bushman,et al.  Short pyrosequencing reads suffice for accurate microbial community analysis , 2007, Nucleic acids research.

[164]  J. Tiedje,et al.  Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy , 2007, Applied and Environmental Microbiology.

[165]  R. Cattell,et al.  The Procrustes Program: Producing direct rotation to test a hypothesized factor structure. , 2007 .

[166]  Anne-Laure Boulesteix,et al.  Partial least squares: a versatile tool for the analysis of high-dimensional genomic data , 2006, Briefings Bioinform..

[167]  F. Bäckhed,et al.  Obesity alters gut microbial ecology. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[168]  P. Dixon.  VEGAN, a package of R functions for community ecology , 2003 .

[169]  J. Doré,et al.  Differences in rDNA libraries of faecal bacteria derived from 10- and 25-cycle PCRs. , 2002, International journal of systematic and evolutionary microbiology.

[170]  S. Dolédec,et al.  Co‐inertia analysis: an alternative method for studying species–environment relationships , 1994 .

[171]  John Aitchison,et al.  The Statistical Analysis of Compositional Data , 1986 .

[172]  N. Mantel.  The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.