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      Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

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          Abstract

          Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Gene Ontology: tool for the unification of biology

            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                04 May 2021
                2021
                : 10
                : e65088
                Affiliations
                [1 ]Department CIBIO, University of Trento TrentoItaly
                [2 ]Harvard T.H. Chan School of Public Health BostonUnited States
                [3 ]The Broad Institute of MIT and Harvard CambridgeUnited States
                [4 ]Department of Food Quality and Nutrition, Research and Innovation Center, Edmund Mach Foundation San Michele all’AdigeItaly
                [5 ]IEO, European Institute of Oncology IRCCS MilanItaly
                University of California, San Francisco United States
                McGill University Canada
                University of California, San Francisco United States
                University of California, Davis United States
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-8105-9607
                http://orcid.org/0000-0003-0322-482X
                http://orcid.org/0000-0003-1414-5924
                http://orcid.org/0000-0002-2768-2975
                http://orcid.org/0000-0001-6661-4046
                https://orcid.org/0000-0002-1110-0096
                https://orcid.org/0000-0002-8798-7068
                https://orcid.org/0000-0002-1583-5794
                Article
                65088
                10.7554/eLife.65088
                8096432
                33944776
                c7d7967c-450d-4862-b5b6-47e9f17345f4
                © 2021, Beghini et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 22 November 2020
                : 21 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: 716575
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003407, Ministero dell’Istruzione, dell’Università e della Ricerca;
                Award ID: RBFR13EWWI_001
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010677, H2020 Health;
                Award ID: 825410
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100010678, H2020 Food;
                Award ID: 818368
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 1U01CA230551
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000062, National Institute of Diabetes and Digestive and Kidney Diseases;
                Award ID: R24DK110499 and U54DE023798
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000289, Cancer Research UK;
                Award ID: C10674/A27140
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008871, Juvenile Diabetes Research Foundation United States of America;
                Award ID: 3-SRA-2016-141-Q-R
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;
                Award ID: R01HG005220
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Computational and Systems Biology
                Microbiology and Infectious Disease
                Custom metadata
                The bioBakery 3 platform enables improved, integrated, and strain-level analysis of microbial communities from large-scale meta-omic data.

                Life sciences
                microbiome,metagenomics,microbial genomics,computational analysis,human,other
                Life sciences
                microbiome, metagenomics, microbial genomics, computational analysis, human, other

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