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      PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool

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          Summary

          Drug development has been hampered by a high failure rate in clinical trials due to our incomplete understanding of drug functions across organs and species. Therefore, elucidating species- and tissue-specific drug functions can provide insights into therapeutic efficacy, potential adverse effects, and interspecies differences necessary for effective translational medicine. Here, we present PharmOmics, a drug knowledgebase and analytical tool that is hosted on an interactive web server. Using tissue- and species-specific transcriptome data from human, mouse, and rat curated from different databases, we implemented a gene-network-based approach for drug repositioning. We demonstrate the potential of PharmOmics to retrieve known therapeutic drugs and identify drugs with tissue toxicity using in silico performance assessment. We further validated predicted drugs for nonalcoholic fatty liver disease in mice. By combining tissue- and species-specific in vivo drug signatures with gene networks, PharmOmics serves as a complementary tool to support drug characterization and network-based medicine.

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          Highlights

          • Development of PharmOmics, a platform for drug repositioning and toxicity prediction

          • Contains >18000 species/tissue-specific gene signatures for 941 drugs and chemicals

          • Benchmarked and validated network-based drug repositioning and toxicity prediction

          • PharmOmics is freely accessible via an online web server to facilitate user access

          Abstract

          Bioinformatics; Biocomputational method; Systems biology; In silico biology

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          Most cited references72

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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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            Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

            Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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              NCBI GEO: archive for functional genomics data sets—update

              The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                10 March 2022
                15 April 2022
                10 March 2022
                : 25
                : 4
                : 104052
                Affiliations
                [1 ]Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095, USA
                [2 ]Interdepartmental Program of Molecular Toxicology, University of California, Los Angeles, Los Angeles, CA 90095, USA
                [3 ]Interdepartmental Program of Molecular, Cellular, & Integrative Physiology, Los Angeles, Los Angeles, CA 90095, USA
                [4 ]Interdepartmental Program of Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
                [5 ]Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
                Author notes
                []Corresponding author xyang123@ 123456ucla.edu
                [6]

                These authors contributed equally

                [7]

                Lead contact

                Article
                S2589-0042(22)00322-4 104052
                10.1016/j.isci.2022.104052
                8957031
                35345455
                19bd17d8-ff63-44b2-9319-0edf6757c73e
                © 2022 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 November 2021
                : 29 January 2022
                : 8 March 2022
                Categories
                Article

                bioinformatics,biocomputational method,systems biology,in silico biology

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