STATegra: Developing new resources for the integrative analysis of multi-omics data

Ponente: Ana Conesa

Professor Bioinformatics, Microbiology and Cell Science Department, University of Florida, USA
Head of Genomics of Gene Expression Lab, Prince Felipe Research Center, Spain

Host: Javier De Las Rivas

Fecha:

Hora: 12:30

Salón de Actos del CIC

background

Next generation sequencing has speed up genome analysis and brought omics research closer to many organisms and biological scenarios. Today an increasing number of research projects propose the combined use of different omics platforms to investigate diverse aspects of genome functioning. However, standard methodologies for the integration of diverse omics data types are not yet ready and researchers frequently face post-experiment question on how to combine data of different nature, variability, and significance into an analysis routine that sheds more light than the analysis of individual datasets separately. Novel statistical and bioinformatics tools are needed for these emerging analysis scenarios. STATegra is a FP7 project aimed to address these questions and provide resources for gene expression-centered, multi-omics data analysis.

results

We have created a multi-omics data-set for method development consisting of a controlled mouse B-cell differentiation experiment with 6 time points and treatments. We have measured up to 8 different omics: RNA-seq, microRNA-seq, single-cell-RNA-seq, RRBS-seq (DNA methylation), ChIP-seq, DNase-seq, Metabolomics and Proteomics. We have developed a set of integrative analysis tools centered on gene expression data. I will present approaches for:

• Comparison of Performance Metrics across omics types and multi-omics power analysis
• Data exploration to reveal shared and data-type specific signal properties
• Mapping chromatin signals to gene annotations.
• Pair-wise (e.g. RNA-seq vs DNase-seq) integrative analysis of time course data
• Define regulatory programs for co-expressed genes based on chromatin and post-transcriptional regulation.
• Integrate ChIP-seq, RNA-seq and Metabolomics data to study the impact of transcriptional regulation on metabolic changes.
• Pathway-level integrative analysis of multi-omics data.

Methods are being implemented either in R under the STATegRa Bioconductor package or as web-based tools such as Paintomics.

conclusions

STATegra provides a wide variety of resources for the analysis of multi-omics data, available to the scientific community through open access distributions