RNentropy: an entropy-based tool for the detection of significant variation of gene expression across multiple RNA-Seq experiments.
Nucleic Acids Res
; 46(8): e46, 2018 05 04.
Article
in En
| MEDLINE
| ID: mdl-29390085
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Software
/
Sequence Analysis, RNA
/
Gene Expression Profiling
Type of study:
Diagnostic_studies
Limits:
Animals
/
Humans
/
Male
Language:
En
Journal:
Nucleic Acids Res
Year:
2018
Type:
Article
Affiliation country:
Italy