AI Article Synopsis
- RNA-sequencing (RNA-Seq) has become the leading method for analyzing bacterial transcriptomes, but there is no universally accepted standard for predicting differentially expressed genes.
- SCORE is a new tool that utilizes a consensus method involving several established tools to improve prediction accuracy and generate a clear output for RNA-Seq data analysis.
- By simplifying the RNA-Seq workflow from preprocessing to ontology analysis, SCORE aims to establish new gold standards for future research in this field.
Article Abstract
Summary: 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.
Availability And Implementation: https://github.com/SiWolf/SCORE.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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| http://dx.doi.org/10.1093/bioinformatics/btaa681 | DOI Listing |
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