The Computational Biology Institute (IBC) aims at the development of innovative methods and software to analyze, integrate and contextualize large-scale biological data in the fields of health, agronomy and environment. Scalable computational solutions able to handle this ever-increasing volume of data constitute the present and future bottleneck that may limit their economic impact. Several branches of research will thus be combined: algorithmics (combinatorial, numerical, highly parallel, stochastic), modeling (discrete, qualitative, quantitative, probabilistic), and data management and information retrieval (integration, workflows, cloud). Concepts and tools will be validated using key applications in fundamental biology (transcriptomics, structure and function of proteins, development and morphogenesis), health (pathogens, cancer, stem cells), agronomy (plant genomics, tropical agriculture), and environment (population dynamic, biodiversity).The project is divided into five complementary work-packages that include the main aspects of processing biological data on a large scale:
- WP1-HTS: Methods for high-throughput sequencing analysis
- WP2-Evolution: Scaling-up evolutionary analyses
- WP3-Annotation: Structural and functional annotation of proteomes
- WP4-Imaging: Integrating cell and tissue imaging with Omics data
- WP5-Databases: Biological data and knowledge integration
IBC is a multidisciplinary project center supported for five years (2012-2017) by the French "Investissements d'Avenir" Call and its trustees. IBC currently involves 65 permanent researchers with broad multidisciplinary spectrum, based in one company and fourteen laboratories of Montpellier. IBC should become a privileged meeting place for computational biology and bioinformatics researchers, not only bringing together those involved with the original project, but also a large community of academic and industry researchers on regional, national and international levels. IBC activity will invite world-class researchers to collaborate with, organize scientific events, train young researchers, and promote results and exchange information with industrial partners.
Likelihood-free model choice
Handbook of Approximate Bayesian...
Ribo-seq enlightens codon usage bias
DNA Research dsw062, 2017