Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

. 2021 ; 12 () : 635781. [epub] 20210222

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid33692771

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Bioinformatics Core Luxembourg Centre for Systems Biomedicine University of Luxembourg Esch sur Alzette Luxembourg

Bioinformatics Research Laboratory Department of Biological Sciences University of Cyprus Nicosia Cyprus

Bioinformatics Research Unit Riga Stradins University Riga Latvia

Biotechnical Faculty University of Ljubljana Ljubljana Slovenia

Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición Instituto de Salud Carlos 3 Madrid Spain

Centro de Matemática e Aplicações FCT UNL Caparica Portugal

CINTESIS NOVA Medical School NMS Universidade Nova de Lisboa Lisbon Portugal

Computational Biology Group Precision Nutrition and Cancer Research Program IMDEA Food Institute Madrid Spain

Computational Oncology Sage Bionetworks Seattle WA United States

Department for Biomedical Sciences Institute for Biomedical Technologies National Research Council Bari Italy

Department of Biology University of Fribourg Fribourg Switzerland

Department of Clinical Science University of Bergen Bergen Norway

Department of Computer Engineering Abdullah Gul University Kayseri Turkey

Department of Computer Science and Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

Department of Computer Science University of Bari Aldo Moro Bari Italy

Department of Computer Technologies Karadeniz Technical University Trabzon Turkey

Department of Computing University of Turku Turku Finland

Department of Electrical and Electronics Engineering Karadeniz Technical University Trabzon Turkey

Department of Epidemiology Erasmus Medical Center Rotterdam Netherlands

Department of Genetics and Bioengineering International University of Sarajevo Sarajevo Bosnia and Herzegovina

Department of Infection and Immunity Luxembourg Institute of Health Esch sur Alzette Luxembourg

Department of Infectious Diseases and Immunology Faculty of Veterinary Medicine Utrecht University Utrecht Netherlands

Department of Mathematical Analysis and Applications of Mathematics Palacký University Olomouc Czechia

Department of Microbiology and Clinical Microbiology Faculty of Medicine Erciyes University Kayseri Turkey

Department of Microbiology University of Innsbruck Innsbruck Austria

Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester Manchester United Kingdom

European Molecular Biology Laboratory Structural and Computational Biology Unit Heidelberg Germany

Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana Slovenia

Faculty of Information Tehnology and Bionics Pázmány University Budapest Hungary

Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruñ Poland

Human Genetics and Disease Mechanisms Latvian Biomedical Research and Study Centre Riga Latvia

Institute of Computational Biomedicine Heidelberg University Faculty of Medicine and Heidelberg University Hospital Heidelberg Germany

Institute of Molecular and Cell Biology University of Tartu Tartu Estonia

Instituto de Investigación Biomédica de Málaga Unidad de Gestión Clìnica de Endocrinologìa y Nutrición Hospital Clìnico Universitario Virgen de la Victoria Universidad de Málaga Málaga Spain

Jozef Stefan Institute Ljubljana Slovenia

Laboratory of Genetics Department of Biotechnology School of Applied Biology and Biotechnology Agricultural University of Athens Athens Greece

Latvian Biomedical Research and Study Centre Riga Latvia

Metagenomics Laboratory Genome and Stem Cell Center Erciyes University Kayseri Turkey

Molecular Nutrition and Proteomics Lab Faculty of the Food Science and Technology Institute of Life Sciences University of Agricultural Sciences and Veterinary Medicine of Cluj Napoca Cluj Napoca Romania

Navarrabiomed Complejo Hospitalario de Navarra Pamplona Spain

NOVA Laboratory for Computer Science and Informatics FCT UNL Caparica Portugal

Odense Research Center for Anaphylaxis Department of Dermatology and Allergy Center Odense University Hospital University of Southern Denmark Odense Denmark

Sarajevo Medical School University Sarajevo School of Science and Technology Sarajevo Bosnia and Herzegovina

School of Microbiology and APC Microbiome Ireland University College Cork Cork Ireland

Swiss Institute of Bioinformatics Lausanne Switzerland

Zobrazit více v PubMed

Ai L., Tian H., Chen Z., Chen H., Xu J., Fang J.-Y. (2017). Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer. Oncotarget 8 9546–9556. 10.18632/oncotarget.14488 PubMed DOI PMC

Aitchison J. (1986). THE statistical Analysis of Compositional Data. New York, NY: Chapman and Hall.

Alneberg J., Bjarnason B. S., de Bruijn I., Schirmer M., Quick J., Ijaz U. Z., et al. (2014). Binning metagenomic contigs by coverage and composition. Nat. Methods 11 1144–1146. 10.1038/nmeth.3103 PubMed DOI

Arango-Argoty G., Garner E., Pruden A., Heath L. S., Vikesland P., Zhang L. (2018). DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6:23. 10.1186/s40168-018-0401-z PubMed DOI PMC

Arbel J., Mengersen K., Rousseau J. (2016). Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity. Ann. Appl. Stat. 10 1496–1516. 10.1214/16-AOAS944 DOI

Armour C. R., Nayfach S., Pollard K. S., Sharpton T. J. (2019). A metagenomic meta-analysis reveals functional signatures of health and disease in the human gut microbiome. mSystems 4:e00332-18. 10.1128/mSystems.00332-18 PubMed DOI PMC

Aryal S., Alimadadi A., Manandhar I., Joe B., Cheng X. (2020). Machine learning strategy for gut microbiome-based diagnostic screening of cardiovascular disease. Hypertens. Dallas Tex 1979 1555–1562. 10.1161/HYPERTENSIONAHA.120.15885 PubMed DOI PMC

Asgari E., Garakani K., McHardy A. C., Mofrad M. R. K. (2018). MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples. Bioinform. Oxf. Engl. 34 i32–i42. 10.1093/bioinformatics/bty296 PubMed DOI PMC

Barratt M. J., Lebrilla C., Shapiro H.-Y., Gordon J. I. (2017). The gut microbiota, food science, and human nutrition: a timely marriage. Cell Host Microbe 22 134–141. 10.1016/j.chom.2017.07.006 PubMed DOI PMC

Becht E., McInnes L., Healy J., Dutertre C.-A., Kwok I. W. H., Ng L. G., et al. (2019). Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37 38–44. 10.1038/nbt.4314 PubMed DOI

Berg G., Rybakova D., Fischer D., Cernava T., Vergès M.-C. C., Charles T., et al. (2020). Microbiome definition re-visited: old concepts and new challenges. Microbiome 8:103. 10.1186/s40168-020-00875-0 PubMed DOI PMC

Björk J. R., Hui F. K. C., O’Hara R. B., Montoya J. M. (2018). Uncovering the drivers of host-associated microbiota with joint species distribution modelling. Mol. Ecol. 27 2714–2724. 10.1111/mec.14718 PubMed DOI PMC

Bolyen E., Rideout J. R., Dillon M. R., Bokulich N. A., Abnet C. C., Al-Ghalith G. A., et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37 852–857. 10.1038/s41587-019-0209-9 PubMed DOI PMC

Buffie C. G., Pamer E. G. (2013). Microbiota-mediated colonization resistance against intestinal pathogens. Nat. Rev. Immunol. 13 790–801. 10.1038/nri3535 PubMed DOI PMC

Buza T. M., Tonui T., Stomeo F., Tiambo C., Katani R., Schilling M., et al. (2019). iMAP: an integrated bioinformatics and visualization pipeline for microbiome data analysis. BMC Bioinformatics 20:374. 10.1186/s12859-019-2965-4 PubMed DOI PMC

Callahan B. J., McMurdie P. J., Rosen M. J., Han A. W., Johnson A. J. A., Holmes S. P. (2016). DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13 581–583. 10.1038/nmeth.3869 PubMed DOI PMC

Chong J., Liu P., Zhou G., Xia J. (2020). Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15 799–821. 10.1038/s41596-019-0264-1 PubMed DOI

Costea P. I., Hildebrand F., Arumugam M., Bäckhed F., Blaser M. J., Bushman F. D., et al. (2018). Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3 8–16. 10.1038/s41564-017-0072-8 PubMed DOI PMC

Cullen C. M., Aneja K. K., Beyhan S., Cho C. E., Woloszynek S., Convertino M., et al. (2020). Emerging priorities for microbiome research. Front. Microbiol. 11:136. 10.3389/fmicb.2020.00136 PubMed DOI PMC

Davis N. M., Proctor D. M., Holmes S. P., Relman D. A., Callahan B. J. (2018). Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226. 10.1186/s40168-018-0605-2 PubMed DOI PMC

Díez López C., Vidaki A., Ralf A., Montiel González D., Radjabzadeh D., Kraaij R., et al. (2019). Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials. Forensic Sci. Int. Genet. 41 72–82. 10.1016/j.fsigen.2019.03.015 PubMed DOI

Eetemadi A., Rai N., Pereira B. M. P., Kim M., Schmitz H., Tagkopoulos I. (2020). The computational diet: a review of computational methods across diet, microbiome, and health. Front. Microbiol. 11:393. 10.3389/fmicb.2020.00393 PubMed DOI PMC

Eren A. M., Esen ÖC., Quince C., Vineis J. H., Morrison H. G., Sogin M. L., et al. (2015). Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3:e1319. 10.7717/peerj.1319 PubMed DOI PMC

Falony G., Joossens M., Vieira-Silva S., Wang J., Darzi Y., Faust K., et al. (2016). Population-level analysis of gut microbiome variation. Science 352 560–564. 10.1126/science.aad3503 PubMed DOI

Fernandes A. D., Reid J. N., Macklaim J. M., McMurrough T. A., Edgell D. R., Gloor G. B. (2014). Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2:15. 10.1186/2049-2618-2-15 PubMed DOI PMC

Gagnière J., Raisch J., Veziant J., Barnich N., Bonnet R., Buc E., et al. (2016). Gut microbiota imbalance and colorectal cancer. World J. Gastroenterol. 22 501–518. 10.3748/wjg.v22.i2.501 PubMed DOI PMC

Gloor G. B., Macklaim J. M., Pawlowsky-Glahn V., Egozcue J. J. (2017). Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8:2224. 10.3389/fmicb.2017.02224 PubMed DOI PMC

Gómez-López G., Dopazo J., Cigudosa J. C., Valencia A., Al-Shahrour F. (2019). Precision medicine needs pioneering clinical bioinformaticians. Brief. Bioinform. 20 752–766. 10.1093/bib/bbx144 PubMed DOI

Hillmann B., Al-Ghalith G. A., Shields-Cutler R. R., Zhu Q., Gohl D. M., Beckman K. B., et al. (2018). Evaluating the information content of shallow shotgun metagenomics. mSystems 3 e69–e18. 10.1128/mSystems.00069-18 PubMed DOI PMC

Holmes I., Harris K., Quince C. (2012). Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7:e30126. 10.1371/journal.pone.0030126 PubMed DOI PMC

Huang R., Soneson C., Ernst F. G. M., Rue-Albrecht K. C., Yu G., Hicks S. C., et al. (2020). TreeSummarizedExperiment: a S4 class for data with hierarchical structure. F1000Research 9:1246. 10.12688/f1000research.26669.1 PubMed DOI PMC

Hughes D. A., Bacigalupe R., Wang J., Rühlemann M. C., Tito R. Y., Falony G., et al. (2020). Genome-wide associations of human gut microbiome variation and implications for causal inference analyses. Nat. Microbiol. 5 1079–1087. 10.1038/s41564-020-0743-8 PubMed DOI PMC

Juhász J., Kertész-Farkas A., Szabó D., Pongor S. (2014). Emergence of collective territorial defense in bacterial communities: horizontal gene transfer can stabilize microbiomes. PLoS One 9:e0095511. 10.1371/journal.pone.0095511 PubMed DOI PMC

Kim S., Covington A., Pamer E. G. (2017). The intestinal microbiota: antibiotics, colonization resistance, and enteric pathogens. Immunol. Rev. 279 90–105. 10.1111/imr.12563 PubMed DOI PMC

Knight R., Vrbanac A., Taylor B. C., Aksenov A., Callewaert C., Debelius J., et al. (2018). Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16 410–422. 10.1038/s41579-018-0029-9 PubMed DOI

Knights D., Kuczynski J., Charlson E. S., Zaneveld J., Mozer M. C., Collman R. G., et al. (2011). Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 8:761. 10.1038/nmeth.1650 PubMed DOI PMC

Kobak D., Berens P. (2019). The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10:5416. 10.1038/s41467-019-13056-x PubMed DOI PMC

Lahti L., Salojärvi J., Salonen A., Scheffer M., de Vos W. M. (2014). Tipping elements in the human intestinal ecosystem. Nat. Commun. 5:4344. 10.1038/ncomms5344 PubMed DOI PMC

LaPierre N., Ju C. J.-T., Zhou G., Wang W. (2019). MetaPheno: a critical evaluation of deep learning and machine learning in metagenome-based disease prediction. Methods San Diego Calif. 166 74–82. 10.1016/j.ymeth.2019.03.003 PubMed DOI PMC

Lederberg J., McCray A. T. (2001). ‘Ome sweet ‘omics– a genealogical treasury of words. Scientist 15:8. 10.1089/clinomi.03.09.05 DOI

Legendre P., Legendre L. (2012). Numerical Ecology. Amsterdam: Elsevier.

Liao T., Wei Y., Luo M., Zhao G.-P., Zhou H. (2019). tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies. Genome Biol. 20:293. 10.1186/s13059-019-1871-4 PubMed DOI PMC

Lin C., Culver J., Weston B., Underhill E., Gorky J., Dhurjati P. (2018). GutLogo: agent-based modeling framework to investigate spatial and temporal dynamics in the gut microbiome. PLoS One 13:e0207072. 10.1371/journal.pone.0207072 PubMed DOI PMC

Lin H., Peddada S. D. (2020). Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11:3514. 10.1038/s41467-020-17041-7 PubMed DOI PMC

Liu Y., Meric G., Havulinna A. S., Teo S. M., Ruuskanen M., Sanders J., et al. (2020). Early prediction of liver disease using conventional risk factors and gut microbiome-augmented gradient boosting. medRxiv [Preprint]. 10.1101/2020.06.24.20138933 PubMed DOI PMC

Love M. I., Huber W., Anders S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:550. 10.1186/s13059-014-0550-8 PubMed DOI PMC

Lozupone C. A., Stombaugh J., Gonzalez A., Ackermann G., Wendel D., Vázquez-Baeza Y., et al. (2013). Meta-analyses of studies of the human microbiota. Genome Res. 23 1704–1714. 10.1101/gr.151803.112 PubMed DOI PMC

Lynch S. V., Ng S. C., Shanahan F., Tilg H. (2019). Translating the gut microbiome: ready for the clinic? Nat. Rev. Gastroenterol. Hepatol. 16 656–661. 10.1038/s41575-019-0204-0 PubMed DOI

Malla M. A., Dubey A., Kumar A., Yadav S., Hashem A., Abd_Allah E. F. (2019). Exploring the human microbiome: the potential future role of next-generation sequencing in disease diagnosis and treatment. Front. Immunol. 9:2968. 10.3389/fimmu.2018.02868 PubMed DOI PMC

Marcos-Zambrano L. J., Karaduzovic-Hadziabdic K., Przymus P., Trajkovik V., Aasmets O., Berland M., et al. (2021). Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment. Front. Microbiol. 10.3389/fmicb.2021.634511 PubMed DOI PMC

McGhee J. J., Rawson N., Bailey B. A., Fernandez-Guerra A., Sisk-Hackworth L., Kelley S. T. (2020). Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics. PeerJ 8:e8783. 10.7717/peerj.8783 PubMed DOI PMC

McIver L. J., Abu-Ali G., Franzosa E. A., Schwager R., Morgan X. C., Waldron L., et al. (2018). bioBakery: a meta’omic analysis environment. Bioinformatics 34 1235–1237. 10.1093/bioinformatics/btx754 PubMed DOI PMC

McMurdie P. J., Holmes S. (2013). phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8:e61217. 10.1371/journal.pone.0061217 PubMed DOI PMC

Mehta R. S., Abu-Ali G. S., Drew D. A., Lloyd-Price J., Subramanian A., Lochhead P., et al. (2018). Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. 3 347–355. 10.1038/s41564-017-0096-0 PubMed DOI PMC

Meyer F., Paarmann D., D’Souza M., Olson R., Glass E., Kubal M., et al. (2008). The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9:386. 10.1186/1471-2105-9-386 PubMed DOI PMC

Mitchell A. L., Almeida A., Beracochea M., Boland M., Burgin J., Cochrane G., et al. (2020). MGnify: the microbiome analysis resource in 2020. Nucleic Acids Res. 48 D570–D578. 10.1093/nar/gkz1035 PubMed DOI PMC

Murovec B., Deutsch L., Stres B. (2020). Computational framework for high-quality production and large-scale evolutionary analysis of metagenome assembled genomes. Mol. Biol. Evol. 37 593–598. 10.1093/molbev/msz237 PubMed DOI PMC

Namkung J. (2020). Machine learning methods for microbiome studies. J. Microbiol. 58 206–216. 10.1007/s12275-020-0066-8 PubMed DOI

Nayfach S., Shi Z. J., Seshadri R., Pollard K. S., Kyrpides N. C. (2019). New insights from uncultivated genomes of the global human gut microbiome. Nature 568 505–510. 10.1038/s41586-019-1058-x PubMed DOI PMC

Oh M., Zhang L. (2020). DeepMicro: deep representation learning for disease prediction based on microbiome data. Sci. Rep. 10:6026. 10.1038/s41598-020-63159-5 PubMed DOI PMC

Olson R. S., La Cava W., Orzechowski P., Urbanowicz R. J., Moore J. H. (2017). PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData Min. 10:36. 10.1186/s13040-017-0154-4 PubMed DOI PMC

Org E., Parks B. W., Joo J. W. J., Emert B., Schwartzman W., Kang E. Y., et al. (2015). Genetic and environmental control of host-gut microbiota interactions. Genome Res. 25 1558–1569. 10.1101/gr.194118.115 PubMed DOI PMC

Pasolli E., Truong D. T., Malik F., Waldron L., Segata N. (2016). Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights. PLoS Comput. Biol. 12:e1004977. 10.1371/journal.pcbi.1004977 PubMed DOI PMC

Pearl J. (2009). Causal inference in statistics: an overview. Stat. Surv. 3 96–146. 10.1214/09-SS057 DOI

Poussin C., Sierro N., Boué S., Battey J., Scotti E., Belcastro V., et al. (2018). Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov. Today 23 1644–1657. 10.1016/j.drudis.2018.06.005 PubMed DOI

Qin J., Li R., Raes J., Arumugam M., Burgdorf K. S., Manichanh C., et al. (2010). A human gut microbial gene catalog established by metagenomic sequencing. Nature 464 59–65. 10.1038/nature08821 PubMed DOI PMC

Qin Y., Meric G., Long T., Watrous J., Burgess S., Havulinna A., et al. (2020). Genome-wide association and Mendelian randomization analysis prioritizes bioactive metabolites with putative causal effects on common diseases. medRxiv [Preprint]. 10.1101/2020.08.01.20166413 DOI

Quince C., Walker A. W., Simpson J. T., Loman N. J., Segata N. (2017). Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35 833–844. 10.1038/nbt.3935 PubMed DOI

Rahman M. A., Rangwala H. (2020). IDMIL: an alignment-free interpretable deep multiple instance learning (MIL) for predicting disease from whole-metagenomic data. Bioinformatics 36 i39–i47. 10.1093/bioinformatics/btaa477 PubMed DOI PMC

Rahman S. F., Olm M. R., Morowitz M. J., Banfield J. F. (2018). Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome. mSystems 3:e00123-17. 10.1128/mSystems.00123-17 PubMed DOI PMC

Reiman D., Metwally A. A., Dai Y. (2018). PopPhy-CNN: a phylogenetic tree embedded architecture for convolution neural networks for metagenomic data. bioRxiv [Preprint]. 10.1101/257931 PubMed DOI

Roslund M. I., Puhakka R., Gr nroos N., Nurminen N., Oikarinen N., Gazal A. M. (2020). Biodiversity intervention enhances immune regulation and health-associated commensal microbiota among daycare children. Sci. Adv. 6:eaba2578. 10.1126/sciadv.aba2578 PubMed DOI PMC

Rule A., Birmingham A., Zuniga C., Altintas I., Huang S.-C., Knight R., et al. (2019). Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. PLoS Comput. Biol. 15:e1007007. 10.1371/journal.pcbi.1007007 PubMed DOI PMC

Saez-Rodriguez J., Costello J. C., Friend S. H., Kellen M. R., Mangravite L., Meyer P., et al. (2016). Crowdsourcing biomedical research: leveraging communities as innovation engines. Nat. Rev. Genet. 17 470–486. 10.1038/nrg.2016.69 PubMed DOI PMC

Salosensaari A., Laitinen V., Havulinna A. S., Meric G., Cheng S., Perola M., et al. (2020). Taxonomic signatures of long-term mortality risk in human gut microbiota. medRxiv [Preprint]. 10.1101/2019.12.30.19015842 PubMed DOI PMC

Sampson T. R., Debelius J. W., Thron T., Janssen S., Shastri G. G., Ilhan Z. E., et al. (2016). Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson’s disease. Cell 167 1469.e12–1480.e12. 10.1016/j.cell.2016.11.018 PubMed DOI PMC

Sankaran K., Holmes S. (2014). structSSI: simultaneous and selective inference for grouped or hierarchically structured data. J. Stat. Softw. 59 1–21. 10.18637/jss.v059.i13 PubMed DOI PMC

Sankaran K., Holmes S. P. (2019). Latent variable modeling for the microbiome. Biostat. Oxf. Engl. 20 599–614. 10.1093/biostatistics/kxy018 PubMed DOI PMC

Sanna S., van Zuydam N. R., Mahajan A., Kurilshikov A., Vich Vila A., Võsa U., et al. (2019). Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat. Genet. 51 600–605. 10.1038/s41588-019-0350-x PubMed DOI PMC

Schmidt T. S. B., Raes J., Bork P. (2018). The human gut microbiome: from association to modulation. Cell 172 1198–1215. 10.1016/j.cell.2018.02.044 PubMed DOI

Schmitt S., Tsai P., Bell J., Fromont J., Ilan M., Lindquist N., et al. (2012). Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 6 564–576. 10.1038/ismej.2011.116 PubMed DOI PMC

Schloss P. D., Westcott S. L., Ryabin T., Hall J. R., Hartmann M., Hollister E. B., et al. (2009). Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75 7537–7541. 10.1128/AEM.01541-09 PubMed DOI PMC

Segata N., Izard J., Waldron L., Gevers D., Miropolsky L., Garrett W. S., et al. (2011). Metagenomic biomarker discovery and explanation. Genome Biol. 12:R60. 10.1186/gb-2011-12-6-r60 PubMed DOI PMC

Shenhav L., Thompson M., Joseph T. A., Briscoe L., Furman O., Bogumil D., et al. (2019). FEAST: fast expectation-maximization for microbial source tracking. Nat. Methods 16 627–632. 10.1038/s41592-019-0431-x PubMed DOI PMC

Shetty S. A., Lahti L. (2019). Microbiome data science. J. Biosci. 44:115. PubMed

Singh R. K., Chang H.-W., Yan D., Lee K. M., Ucmak D., Wong K., et al. (2017). Influence of diet on the gut microbiome and implications for human health. J. Transl. Med. 15:73. 10.1186/s12967-017-1175-y PubMed DOI PMC

Sze M. A., Schloss P. D. (2018). Leveraging existing 16S rRNA gene surveys to identify reproducible biomarkers in individuals with colorectal tumors. mBio 9:e00630-18. 10.1128/mBio.00630-18 PubMed DOI PMC

Tamames J., Cobo-Simón M., Puente-Sánchez F. (2019). Assessing the performance of different approaches for functional and taxonomic annotation of metagenomes. BMC Genomics 20:960. 10.1186/s12864-019-6289-6 PubMed DOI PMC

Tamburini S., Shen N., Wu H. C., Clemente J. C. (2016). The microbiome in early life: implications for health outcomes. Nat. Med. 22 713–722. 10.1038/nm.4142 PubMed DOI

ten Hoopen P., Finn R. D., Bongo L. A., Corre E., Fosso B., Meyer F., et al. (2017). The metagenomic data life-cycle: standards and best practices. GigaScience 6:gix047. 10.1093/gigascience/gix047 PubMed DOI PMC

Topçuoğlu B. D., Lesniak N. A., Ruffin M. T., Wiens J., Schloss P. D. (2020). A framework for effective application of machine learning to microbiome-based classification problems. mBio 11:e00434-20. 10.1128/mBio.00434-20 PubMed DOI PMC

Treangen T. J., Koren S., Sommer D. D., Liu B., Astrovskaya I., Ondov B., et al. (2013). MetAMOS: a modular and open source metagenomic assembly and analysis pipeline. Genome Biol. 14:R2. 10.1186/gb-2013-14-1-r2 PubMed DOI PMC

Turnbaugh P. J., Ley R. E., Hamady M., Fraser-Liggett C. M., Knight R., Gordon J. I. (2007). The human microbiome project. Nature 449 804–810. 10.1038/nature06244 PubMed DOI PMC

Walhout M., Vidal M., Dekker J. (2013). Handbook of Systems Biology. Amsterdam: Elsevier.

Wang Y., Kasper L. H. (2014). The role of microbiome in central nervous system disorders. Brain. Behav. Immun. 38 1–12. 10.1016/j.bbi.2013.12.015 PubMed DOI PMC

Washburne A. D., Silverman J. D., Leff J. W., Bennett D. J., Darcy J. L., Mukherjee S., et al. (2017). Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets. PeerJ 5:e2969. 10.7717/peerj.2969 PubMed DOI PMC

Washburne A. D., Silverman J. D., Morton J. T., Becker D. J., Crowley D., Mukherjee S., et al. (2019). Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data. Ecol. Monogr. 89:e01353. 10.1002/ecm.1353 DOI

Weiss S., Xu Z. Z., Peddada S., Amir A., Bittinger K., Gonzalez A., et al. (2017). Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:27. 10.1186/s40168-017-0237-y PubMed DOI PMC

Zeevi D., Korem T., Godneva A., Bar N., Kurilshikov A., Lotan-Pompan M., et al. (2019). Structural variation in the gut microbiome associates with host health. Nature 568 43–48. 10.1038/s41586-019-1065-y PubMed DOI

Zhernakova A., Kurilshikov A., Bonder M. J., Tigchelaar E. F., Schirmer M., Vatanen T., et al. (2016). Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352 565–569. 10.1126/science.aad3369 PubMed DOI PMC

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