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      MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data

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          Abstract

          Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets demand powerful, yet user-friendly bioinformatics tools to reveal key patterns, important biomarkers, and potential activities. Here we introduce MicrobiomeAnalyst 2.0 to support comprehensive statistics, visualization, functional interpretation, and integrative analysis of data outputs commonly generated from microbiome studies. Compared to the previous version, MicrobiomeAnalyst 2.0 features three new modules: (i) a Raw Data Processing module for amplicon data processing and taxonomy annotation that connects directly with the Marker Data Profiling module for downstream statistical analysis; (ii) a Microbiome Metabolomics Profiling module to help dissect associations between community compositions and metabolic activities through joint analysis of paired microbiome and metabolomics datasets; and (iii) a Statistical Meta-Analysis module to help identify consistent signatures by integrating datasets across multiple studies. Other important improvements include added support for multi-factor differential analysis and interactive visualizations for popular graphical outputs, updated methods for functional prediction and correlation analysis, and expanded taxon set libraries based on the latest literature. These new features are demonstrated using a multi-omics dataset from a recent type 1 diabetes study. MicrobiomeAnalyst 2.0 is freely available at microbiomeanalyst.ca.

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          Most cited references58

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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              The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

              SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                05 July 2023
                11 May 2023
                11 May 2023
                : 51
                : W1
                : W310-W318
                Affiliations
                Department of Microbiology and Immunology, McGill University , Quebec, Canada
                Institute of Parasitology, McGill University , Quebec, Canada
                Institute of Parasitology, McGill University , Quebec, Canada
                Institute of Parasitology, McGill University , Quebec, Canada
                Institute of Parasitology, McGill University , Quebec, Canada
                Department of Microbiology and Immunology, McGill University , Quebec, Canada
                Institute of Parasitology, McGill University , Quebec, Canada
                Department of Animal Science, McGill University , Quebec, Canada
                Author notes
                To whom correspondence should be addressed: Tel: +1 514 398 8668; Email: jeff.xia@ 123456mcgill.ca
                Author information
                https://orcid.org/0000-0002-7555-736X
                https://orcid.org/0000-0003-2040-2624
                Article
                gkad407
                10.1093/nar/gkad407
                10320150
                37166960
                09a4bbfe-e85b-48d9-b0dc-b9152405fc88
                © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 03 May 2023
                : 17 April 2023
                : 28 February 2023
                Page count
                Pages: 9
                Funding
                Funded by: China Scholarship Council, DOI 10.13039/501100004543;
                Funded by: Genome Canada, DOI 10.13039/100008762;
                Funded by: Genome Quebec;
                Funded by: Canada Research Chairs, DOI 10.13039/501100001804;
                Funded by: Natural Sciences and Engineering Research Council of Canada, DOI 10.13039/501100000038;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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