Welcome to the Genomics of Gene Expression Lab

We have created statistical methods for time-course analysis of gene expression data (maSigPro), multifactorial designs (ASCA-genes) and non-parametric approaches in RNA-seq differential expression analysis (NOISeq). Our ARSyN method is an ASCA based approach to identify and remove batch effects in NGS datasets. Moreover, the QualiMap tool assesses the quality of short read mapped data, while SpongeScan can be used to identify lncRNAs acting as microRNA sponges.

We are developing methods and software for the analysis of alternative isoform expression and its effect on the phenotype. These methodologies leverage long reads technologies for the accurate detection of full-length transcripts. Our tools include SQANTI, for the quality control of long-reads transcriptomics data, IsoAnnot, for functional annotation with isoform resolution, and tappAS, for statistical analysis of these new data.

We have created a wide array of tools for the analysis of multi-omics data, namely NGS, metabolomics and proteomics data. These include annotation of multi-omics experiments (STATegraEMS), experimental design (MultiPower and MultiML), removal of multi-omics batch effects (MultiBaC), simulation of multi-omics datasets (MOSim), statistical integration (MORE), and visualization of multi-omics data (Paintomics). We have also created the STATegra and MultiMip6 multi-omics datasets that are made available to the scientific community.

We have developed a network approach to study the relevance of the unknown component of microbial communities, based on 16S data.  We have applied these methods to extreme environmental habitats and identified MDM hubs that are selected for functional characterization and for the identification of survival and adaptation to harsh conditions.