Gene set analysis methods for the functional interpretation of non-mRNA data-Genomic range and ncRNA data.

Journal: Briefings in bioinformatics
Published Date: Sep 25, 2020

Abstract

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

Authors

  • Antonio Mora
    Department of Information Structure and Organization, Universidad Politécnica (UPM), Madrid 28031, Department of Biochemistry and Molecular Biology I, Universidad Complutense (UCM), Madrid 28040, Department of Computer Architecture and Computer Technology, Universidad de Granada (UGR), Granada 18071, Spain, CITIC, Campanillas, Malaga 29590, Spain, Department of Molecular Evolution, Centro de Astrobiología (CSIC-INTA), Torrejón de Ardoz, Madrid 28850 and Centro de Investigación Biomédica en Red de enfermedades hepáticas y digestivas (CIBERehd), Barcelona 08036, Spain Department of Information Structure and Organization, Universidad Politécnica (UPM), Madrid 28031, Department of Biochemistry and Molecular Biology I, Universidad Complutense (UCM), Madrid 28040, Department of Computer Architecture and Computer Technology, Universidad de Granada (UGR), Granada 18071, Spain, CITIC, Campanillas, Malaga 29590, Spain, Department of Molecular Evolution, Centro de Astrobiología (CSIC-INTA), Torrejón de Ardoz, Madrid 28850 and Centro de Investigación Biomédica en Red de enfermedades hepáticas y digestivas (CIBERehd), Barcelona 08036, Spain.

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