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      MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

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          Abstract

          We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC–MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

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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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            The MR-Base platform supports systematic causal inference across the human phenome

            Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base (http://www.mrbase.org): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.
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              KEGG for taxonomy-based analysis of pathways and genomes

              KEGG ( https://www.kegg.jp ) is a manually curated database resource integrating various biological objects categorized into systems, genomic, chemical and health information. Each object (database entry) is identified by the KEGG identifier (kid), which generally takes the form of a prefix followed by a five-digit number, and can be retrieved by appending /entry/kid in the URL. The KEGG pathway map viewer, the Brite hierarchy viewer and the newly released KEGG genome browser can be launched by appending /pathway/kid, /brite/kid and /genome/kid, respectively, in the URL. Together with an improved annotation procedure for KO (KEGG Orthology) assignment, an increasing number of eukaryotic genomes have been included in KEGG for better representation of organisms in the taxonomic tree. Multiple taxonomy files are generated for classification of KEGG organisms and viruses, and the Brite hierarchy viewer is used for taxonomy mapping, a variant of Brite mapping in the new KEGG Mapper suite. The taxonomy mapping enables analysis of, for example, how functional links of genes in the pathway and physical links of genes on the chromosome are conserved among organism groups.
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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 2024
                08 April 2024
                08 April 2024
                : 52
                : W1
                : W398-W406
                Affiliations
                Institute of Parasitology, McGill University ,Sainte-Anne-de-Bellevue, Quebec, Canada
                Department of Microbiology and Immunology, McGill University , Montreal, Quebec, Canada
                Institute of Parasitology, McGill University ,Sainte-Anne-de-Bellevue, Quebec, Canada
                Institute of Parasitology, McGill University ,Sainte-Anne-de-Bellevue, Quebec, Canada
                Institute of Parasitology, McGill University ,Sainte-Anne-de-Bellevue, Quebec, Canada
                Institute of Parasitology, McGill University ,Sainte-Anne-de-Bellevue, Quebec, Canada
                Department of Pharmacology and Alberta Diabetes Institute, University of Alberta , Edmonton, Alberta, Canada
                Department of Pharmacology and Alberta Diabetes Institute, University of Alberta , Edmonton, Alberta, Canada
                Departments of Biological Sciences and Computing Science, University of Alberta , Edmonton, Alberta, Canada
                The Jackson Laboratory for Genomic Medicine , Farmington, CT, USA
                University of Connecticut School of Medicine , Farmington, CT, USA
                Institute of Parasitology, McGill University ,Sainte-Anne-de-Bellevue, Quebec, Canada
                Department of Microbiology and Immunology, McGill University , Montreal, Quebec, Canada
                Author notes
                To whom correspondence should be addressed. Tel: +1 514 398 8668; Fax: +1 514 398 7857; Email: jeff.xia@ 123456mcgill.ca
                Author information
                https://orcid.org/0000-0003-2040-2624
                Article
                gkae253
                10.1093/nar/gkae253
                11223798
                38587201
                30663e8b-d278-4097-a19e-b25b88b0f237
                © The Author(s) 2024. 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
                : 26 March 2024
                : 14 March 2024
                : 31 January 2024
                Page count
                Pages: 9
                Funding
                Funded by: Genome Canada, DOI 10.13039/100008762;
                Funded by: CFI, DOI 10.13039/100008176;
                Funded by: US National Institutes of Health, DOI 10.13039/100000002;
                Award ID: U01 CA235493
                Funded by: Canadian Institutes of Health Research, DOI 10.13039/501100000024;
                Funded by: Juvenile Diabetes Research Foundation, DOI 10.13039/100022690;
                Funded by: Natural Sciences and Engineering Research Council of Canada, DOI 10.13039/501100000038;
                Funded by: Diabetes Canada, DOI 10.13039/100013528;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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