Efectos de los métodos de análisis de genes, de datos RNA-Seq, sobre el análisis de sobre-representación de conjuntos de genes.

LAURA PRATO; JUAN CRUZ RODRIGUEZ; ANDREA S LLERA; ELMER A FERNÁNDEZ

Córdoba

Congreso; XXI CONGRESO ARGENTINO DE BIOINGENIERIA; 2017

The transcriptomic analysis is essential for detecting biological alterations. At the present, this data is obtained mainly through two technologies: microarrays and RNA-Seq. However, these technologies present a difference in the statistical distribution of their resulting expression data. Usually, the first step in this type of analysis is the detection of differentially expressed genes, which has been extensively studied for both types of data. Nevertheless, it is essential to carry out a deeper analysis, known as functional analysis. At present, there are no scientific studies that give a recommendation on which differentially expressed genes detection method to use when feeding functional analysis.In the present work, the most commonly used methods are compared, from the point of view of functional analysis results. Moreover, similarities and differences between results coming from microarrays and RNA-Seq data are studied.The results indicate that the best alternative when performing functional analysis from RNA-Seq data is Voom+Limma. Further, it is shown that both technologies provide results in common, but in addition, each one is able to focus more strongly whether on more specific or general gene sets.