The Genomics of Gene Expression is led by Dr. Ana Conesa, bioinformatics professor at the University of Florida. We are interested in understanding functional aspects of gene expression at the genome-wide level and across different organisms and its relationship with diseases and traits. For that, we develop statistical methods and software tools that analyze the dynamic aspects of transcriptomes, integrate these with other types of molecular data and annotate them functionally, with a special focus on Next Generation Sequencing (NGS) data. Functional genomics research is complemented with the development of bioinformatics software for the analysis of genomics data.
Our current research focusses on several topics:
- Algorithms for short-reads NGS analysis. 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.
- Functional annotation of isoforms and long-reads methods: 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.
- Integration of multi-omic 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.
- Network analysis of the Microbial Dark Matter: 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.
Throughout the years, the Conesa Lab has been funded by: