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SCORE: Smart Consensus Of RNA Expression—a consensus tool for detecting differentially expressed genes in bacteria
Author(s) -
Silver A. Wolf,
Lennard Epping,
Sandro Andreotti,
Knut Reinert,
Torsten Semmler
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa681
Subject(s) - rna seq , preprocessor , computational biology , computer science , workflow , rna , merge (version control) , data mining , biology , gene , transcriptome , gene expression , artificial intelligence , genetics , information retrieval , database
RNA-sequencing (RNA-Seq) is the current method of choice for studying bacterial transcriptomes. To date, many computational pipelines have been developed to predict differentially expressed genes from RNA-Seq data, but no gold-standard has been widely accepted. We present the Snakemake-based tool Smart Consensus Of RNA Expression (SCORE) which uses a consensus approach founded on a selection of well-established tools for differential gene expression analysis. This allows SCORE to increase the overall prediction accuracy and to merge varying results into a single, human-readable output. SCORE performs all steps for the analysis of bacterial RNA-Seq data, from read preprocessing to the overrepresentation analysis of significantly associated ontologies. Development of consensus approaches like SCORE will help to streamline future RNA-Seq workflows and will fundamentally contribute to the creation of new gold-standards for the analysis of these types of data.

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