RNentropy

RNentropy identifies genes or transcripts with significant variation in expression across multiple conditions from RNA sequencing (RNA-Seq) data using information-theoretic measures.

Key Features:

• Multi-condition Analysis: Handles RNA-Seq datasets with any number of samples and biological replicates across conditions such as different individuals, developmental stages, or tissue types.
• Information Theory-Based Approach: Employs information-theoretic measures to quantify variation and identify significant changes in gene or transcript expression.
• Identification of Over- or Under-expressed Genes: Pinpoints specific samples where genes are overexpressed or underexpressed relative to other conditions.

Scientific Applications:

• Yeast Studies: Analysis of 48 biological replicates from two different yeast conditions.
• Human Tissue Samples: Examination of samples extracted from six human tissues across three individuals.
• Mouse Brain Cell Types: Investigation involving seven distinct mouse brain cell types.
• Human Liver Samples: Study of liver samples from six different individuals.

Methodology:

Applies information-theoretic measures to expression estimates derived from RNA-Seq samples across multiple experimental conditions, identifies samples with relative over- or under-expression, and was validated via comparative analyses with other bioinformatic methods.