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About The Lab
The Genomics of Gene Expression is led by Dr. Ana Conesa and Dr. Sonia Tarazona from the Institute for Integrative Systems Biology (I2SysBio) and Politechnical University of Valencia (UPV), respectively.
We are interested in understanding functional aspects of gene expression by combining a wide variety of high-throughput molecular techniques, including transcriptomics, epigenomics, proteomics, metabolomics, metagenomics and single-cell data, both for model and non-model species. Our lab develops statistical methods and user-friendly software tools to analyze these multi-omics data. Our most current interest is the application of long reads sequencing technologies for transcriptome analysis, the integration of multi-omics data to model chromatin-metabolome regulation, and the combination of environmental sequencing data to investigate the function of Microbial Dark Matter.
Our current research interests are:
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.