Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.With the increasing obtainability of multi-OMICs data comes the need for easy to use data analysis tools. Here, the authors introduce Metascape, a biologist-oriented portal that provides a gene list annotation, enrichment and interactome resource and enables integrated analysis of multi-OMICs datasets.

[1]  S. Brunak,et al.  A scored human protein–protein interaction network to catalyze genomic interpretation , 2017, Nature Methods.

[2]  Paul Pavlidis,et al.  Assessing identity, redundancy and confounds in Gene Ontology annotations over time , 2013, Bioinform..

[3]  Gwendolyn M. Jang,et al.  Meta- and Orthogonal Integration of Influenza "OMICs" Data Defines a Role for UBR4 in Virus Budding. , 2015, Cell host & microbe.

[4]  A. Orth,et al.  Screening the mammalian extracellular proteome for regulators of embryonic human stem cell pluripotency , 2010, Proceedings of the National Academy of Sciences.

[5]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[6]  Rosa D. Hernansaiz-Ballesteros,et al.  Babelomics 5.0: functional interpretation for new generations of genomic data , 2015, Nucleic Acids Res..

[7]  Hedi Peterson,et al.  g:Profiler—a web server for functional interpretation of gene lists (2016 update) , 2016, Nucleic Acids Res..

[8]  Qi Zheng,et al.  GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis , 2008, Nucleic Acids Res..

[9]  J. Ioannidis,et al.  Meta-analysis methods for genome-wide association studies and beyond , 2013, Nature Reviews Genetics.

[10]  Cory Y. McLean,et al.  GREAT improves functional interpretation of cis-regulatory regions , 2010, Nature Biotechnology.

[11]  Kimberly Van Auken,et al.  WormBase 2017: molting into a new stage , 2017, Nucleic Acids Res..

[12]  Yanhui Hu,et al.  FlyBase at 25: looking to the future , 2016, Nucleic Acids Res..

[13]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[14]  Edith D. Wong,et al.  Saccharomyces Genome Database: the genomics resource of budding yeast , 2011, Nucleic Acids Res..

[15]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[16]  Clara Pizzuti,et al.  Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods , 2014, Bioinform..

[17]  angesichts der Corona-Pandemie,et al.  UPDATE , 1973, The Lancet.

[18]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[19]  David Martin,et al.  GOToolBox: functional analysis of gene datasets based on Gene Ontology , 2004, Genome Biology.

[20]  Gary D Bader,et al.  Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation , 2010, PloS one.

[21]  Sergio Contrino,et al.  InterMine: extensive web services for modern biology , 2014, Nucleic Acids Res..

[22]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[23]  David J. Adams,et al.  The IFITM Proteins Mediate Cellular Resistance to Influenza A H1N1 Virus, West Nile Virus, and Dengue Virus , 2009, Cell.

[24]  Henning Hermjakob,et al.  The Reactome pathway knowledgebase , 2013, Nucleic Acids Res..

[25]  Ralf Herwig,et al.  Analyzing and interpreting genome data at the network level with ConsensusPathDB , 2016, Nature Protocols.

[26]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Anushya Muruganujan,et al.  PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements , 2016, Nucleic Acids Res..

[28]  Astrid Gall,et al.  Ensembl 2018 , 2017, Nucleic Acids Res..

[29]  May D. Wang,et al.  GoMiner: a resource for biological interpretation of genomic and proteomic data , 2003, Genome Biology.

[30]  Purvesh Khatri,et al.  A meta-analysis of lung cancer gene expression identifies PTK7 as a survival gene in lung adenocarcinoma. , 2014, Cancer research.

[31]  Alok J. Saldanha,et al.  Java Treeview - extensible visualization of microarray data , 2004, Bioinform..

[32]  Purvesh Khatri,et al.  Ontological analysis of gene expression data: current tools, limitations, and open problems , 2005, Bioinform..

[33]  Xiaomao Li,et al.  Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Endometrial Cancer , 2020, Frontiers in Genetics.

[34]  L. Mirny,et al.  Protein complexes and functional modules in molecular networks , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Y. Benjamini,et al.  More powerful procedures for multiple significance testing. , 1990, Statistics in medicine.

[36]  Y. Lv,et al.  WISP1 Predicts Clinical Prognosis and Is Associated With Tumor Purity, Immunocyte Infiltration, and Macrophage M2 Polarization in Pan-Cancer , 2020, Frontiers in Genetics.

[37]  Daniel Becker,et al.  Genome-wide RNAi screen identifies human host factors crucial for influenza virus replication , 2010, Nature.

[38]  Lin Cheng,et al.  The differential expression patterns of paralogs in response to stresses indicate expression and sequence divergences , 2020, BMC Plant Biology.

[39]  Pengfei Qin,et al.  Identification of the key genes and microRNAs in adult acute myeloid leukemia with FLT3 mutation by bioinformatics analysis , 2020, International journal of medical sciences.

[40]  P. Liberali,et al.  Single-cell and multivariate approaches in genetic perturbation screens , 2014, Nature Reviews Genetics.

[41]  Zalmiyah Zakaria,et al.  A Review on Bioinformatics Enrichment Analysis Tools Towards Functional Analysis of High Throughput Gene Set Data , 2015 .

[42]  R. König,et al.  Human Host Factors Required for Influenza Virus Replication , 2010, Nature.

[43]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[44]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[45]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[46]  Julio Saez-Rodriguez,et al.  OmniPath: guidelines and gateway for literature-curated signaling pathway resources , 2016, Nature Methods.

[47]  Amy S. Espeseth,et al.  Host Cell Factors in HIV Replication: Meta-Analysis of Genome-Wide Studies , 2009, PLoS pathogens.

[48]  Andrew D. Rouillard,et al.  Enrichr: a comprehensive gene set enrichment analysis web server 2016 update , 2016, Nucleic Acids Res..

[49]  Lincoln D. Stein,et al.  Impact of outdated gene annotations on pathway enrichment analysis , 2016, Nature Methods.

[50]  Steven J. M. Jones,et al.  Circos: an information aesthetic for comparative genomics. , 2009, Genome research.

[51]  Shuang Li,et al.  Selenium deficiency-induced redox imbalance leads to metabolic reprogramming and inflammation in the liver , 2020, Redox biology.

[52]  A. Butte,et al.  Leveraging big data to transform target selection and drug discovery , 2016, Clinical pharmacology and therapeutics.

[53]  Kara Dolinski,et al.  The BioGRID interaction database: 2017 update , 2016, Nucleic Acids Res..

[54]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[55]  Jing Wang,et al.  WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit , 2017, Nucleic Acids Res..

[56]  Gary D Bader,et al.  Pathway and network analysis of cancer genomes , 2015, Nature Methods.

[57]  J. Carazo,et al.  GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists , 2007, Genome Biology.

[58]  Jing Chen,et al.  ToppGene Suite for gene list enrichment analysis and candidate gene prioritization , 2009, Nucleic Acids Res..

[59]  Tatiana A. Tatusova,et al.  Entrez Gene: gene-centered information at NCBI , 2004, Nucleic Acids Res..

[60]  Christina Backes,et al.  Multi-omics enrichment analysis using the GeneTrail2 web service , 2016, Bioinform..

[61]  Zhou Du,et al.  agriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update , 2017, Nucleic Acids Res..

[62]  Hans-Werner Mewes,et al.  CORUM: the comprehensive resource of mammalian protein complexes , 2007, Nucleic Acids Res..