GELA: A Software Tool for the Analysis of Gene Expression Data

Abstract

Leveraging advances in transcriptome profiling technologies (RNA-seq), biomedical scientists are collecting ever-increasing gene expression profiles data with low cost and high throughput. Therefore, automatic knowledge extraction methods are becoming essential to manage them. In this work, we present GELA (Gene Expression Logic Analyzer), a novel pipeline able to perform a knowledge discovery process in gene expression profiles data of RNA-seq. Firstly, we introduce the RNA-seq technologies, then, we illustrate our gene expression profiles data analysis method (including normalization, clustering, and classification), and finally, we test our knowledge extraction algorithm on the public RNA-seq data sets of Breast Cancer and Stomach Cancer, and on the public microarray data sets of Psoriasis and Multiple Sclerosis, obtaining in both cases promising results.