A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.

Martín Garrido-Rodriguez,Marco A Calzado,Joaquin Dopazo,Daniel Lopez-Lopez,Francisco M Ortuno,Eduardo Muñoz,María Peña-Chilet

doi:10.1371/journal.pcbi.1008748

Abstract

MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.

Highlights

Because of the plummeting in the cost of sequencing technologies during the last decade, RNA massive sequencing (RNA-seq) has become mainstream to study the transcriptome [1]
MIGNON is the first workflow able to perform an integrative analysis of transcriptomic and genomic data in the proper functional context, provided by a mechanistic model of signaling pathway activity, making the most of the information contained in RNA-Seq data
The use of pipelines to perform the different steps of transcriptomic data processing have become a widespread practice. The core of these is usually composed by spliced aligners as STAR [3], HISAT2 [4] or Rail-RNA [5], which map reads against a reference genome, or by pseudo-alignment tools as Salmon [6] or Kallisto [7], that directly obtain a quantification for the regions of interest using probabilistic models

Summary

Author summary

RNA massive sequencing RNA-seq is the most extensively used technique for gene expression profiling in a single assay. Traditional hybridization-based transcriptomics methodologies (microarrays) miss this information, RNA-seq data contains information on variants present in the transcripts that can affect the function of the gene product, which is systematically ignored by current RNA-. A workflow to integrate RNA-seq genomic and transcriptomic data into pathway models babelomics.github.io/MIGNON/. MIGNON is the first workflow able to perform an integrative analysis of transcriptomic and genomic data in the proper functional context, provided by a mechanistic model of signaling pathway activity, making the most of the information contained in RNA-Seq data. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This is a PLOS Computational Biology Software paper

Introduction

Design and implementation

Results

Conclusions