ArticleNucleic acids research2018
RNentropy: an entropy-based tool for the detection of significant variation of gene expression across multiple RNA-Seq experiments.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 45 citations in OpenAlex.
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- Genomic Insights into the Origin and Evolution of Molluscan Red-Bloodedness in the Blood Clam Tegillarca granosa.Molecular biology and evolution · 2021Article
- Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq Samples.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data.NPJ systems biology and applications · 2020Article
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6 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
RNA sequencing (RNA-Seq) has become the experimental standard in transcriptome studies. While most of the bioinformatic pipelines for the analysis of RNA-Seq data and the identification of significant changes in transcript abundance are based on the comparison of two conditions, it is common practice to perform several experiments in parallel (e.g. from different individuals, developmental stages, tissues), for the identification of genes showing a significant variation of expression across all the conditions studied. In this work we present RNentropy, a methodology based on information theory devised for this task, which given expression estimates from any number of RNA-Seq samples and conditions identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. To show the capabilities offered by our methodology, we applied it to different RNA-Seq datasets: 48 biological replicates of two different yeast conditions; samples extracted from six human tissues of three individuals; seven different mouse brain cell types; human liver samples from six individuals. Results, and their comparison to different state of the art bioinformatic methods, show that RNentropy can provide a quick and in depth analysis of significant changes in gene expression profiles over any number of conditions.
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