Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/77193

TítuloUPIMAPI, reCOGnizer and KEGGCharter: bioinformatics tools for functional annotation and visualization of (meta)-omics datasets
Autor(es)Sequeira, J. C.
Rocha, Miguel
Alves, M. M.
Salvador, Andreia Filipa Ferreira
Palavras-chaveGenomics
Metagenomics
Metatranscriptomics
Functional annotation
Metabolic pathways mapping
Differential expression analysis
Data9-Abr-2022
EditoraElsevier
RevistaComputational and Structural Biotechnology Journal
CitaçãoSequeira, J. C.; Rocha, Miguel; Alves, M. Madalena; Salvador, Andreia F., UPIMAPI, reCOGnizer and KEGGCharter: bioinformatics tools for functional annotation and visualization of (meta)-omics datasets. Computational and Structural Biotechnology Journal, 20, 1798-1810, 2022
Resumo(s)Omics and meta-omics technologies are powerful approaches to explore microorganisms functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and meta-omics analyses, together with knowledgebases encompassing information on genes, proteins, taxonomic and functional annotation, among other types of information, are valuable resources for analyzing omics data. Although several bioinformatics resources are available for meta-omics analyses, many require significant computational expertise. Web interfaces are more user-friendly, but often struggle to handle large data files, such as those obtained in metagenomics, metatranscriptomics, or metaproteomics experiments. In this work, we present three novel bioinformatics tools, which are available through user-friendly command-line interfaces, can be run sequentially or stand-alone, and combine popular resources for functional annotation. UPIMAPI performs sequence homology-based annotation and obtains data from UniProtKB (e.g., protein names, EC numbers, Gene Ontology, Taxonomy, cross-references to external databases). reCOGnizer performs multithreaded domain homology-based annotation of protein sequences with several functional databases (i.e., CDD, NCBIfam, Pfam, Protein Clusters, SMART, TIGRFAM, COG and KOG) and in addition, obtains information on domain names and descriptions and EC numbers. KEGGCharter represents omics results, including differential gene expression, in KEGG metabolic pathways. In addition, it shows the taxonomic assignment of the enzymes represented, which is particularly useful in metagenomics studies in which several microorganisms are present. reCOGnizer, UPIMAPI and KEGGCharter together provide a comprehensive and complete functional characterization of large datasets, facilitating the interpretation of microbial activities in nature and in biotechnological processes.
TipoArtigo
DescriçãoAll the scripts and commands used in this paper are available at: https://github.com/iquasere/annotation_paper. The real dataset analysed in this paper was deposited in ENA database under the study accession PRJEB50269 (https://www.ebi.ac.uk/ena/browser/view/PRJEB50269).
URIhttps://hdl.handle.net/1822/77193
DOI10.1016/j.csbj.2022.03.042
ISSN2001-0370
Versão da editorahttps://www.sciencedirect.com/journal/computational-and-structural-biotechnology-journal
Arbitragem científicayes
AcessoAcesso restrito UMinho
Aparece nas coleções:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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