RobiNA

RobiNA performs integrated RNA-Seq analysis to identify differentially expressed genes using R/Bioconductor statistical methods (DESeq and edgeR).

Key Features:

• Data Input Flexibility: Accepts raw FastQ files, SAM/BAM alignment files, and counts tables as input.
• Quality Control and Filtering: Provides quality checking and flexible filtering options for RNA-Seq data.
• Statistical Analysis: Implements DESeq and edgeR within an R/Bioconductor framework for differential expression analysis.
• Large Dataset Handling: Supports processing of large RNA-Seq datasets.

Scientific Applications:

• Transcriptome profiling: Analysis of gene expression profiles across different conditions or treatments using RNA-Seq data.
• Model and non-model organism studies: Application to RNA-Seq datasets from both model organisms and newly discovered or non-model species.
• Differential gene expression discovery: Identification of genes with significant expression changes between conditions.

Methodology:

Accepts diverse input formats (FastQ, SAM/BAM, counts), performs quality control and filtering, and conducts differential expression analysis using DESeq and edgeR within an R/Bioconductor framework.