Alternative splicing identification software tools | Gene expression microarray data analysis

A method for detecting differential alternative splicing in exon array data. FIRMA has been developed for Affymetrix exon arrays, but could in principle other exon arrays, tiling arrays or splice junction arrays. R code implementing FIRMA is contributed to the package aroma.affymetrix.

A package to analyze small to extremely large Affymetrix datasets. Aroma.affymetrix allows to analyze any number of arrays of various chip types, e.g. 10,000s of expression arrays, SNP chips, exon arrays and so on. This software does not require any normalisation step and does not summarise the probe intensities.

An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).

Enables you to go beyond simple identification of differentially expression by providing visualizations of complex gene pathway networks, miRNA and target gene interactions, and alternative splicing events. TAC Software was designed with the end user in mind and enables investigators to analyze and explore data at their own pace. Combined with the comprehensive coverage of Affymetrix’ high-density microarray solutions and Expression Console Software, TAC Software provides an easy-to-use, integrated solution to go from raw data to biological results in just a few clicks.

Detects alternative splicing from expression data. ANOSVA is a statistical method that employs statistical testing principles to identify potential splice variation from expression data. The software requires no transcript information and can thus be applied when the level of annotation is poor. When validated against spiked clone experiments, it yielded no false positives and few false negatives.