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The BIC platform

Scientific Discoveries Accelerator

The BIC, the Integrative and Computational Biology platform of BIAM, supports scientists and their partners in the analysis and interpretation of omics data. Co-led by three bioinformatics researchers, the BIC offers expertise, guidance, and training in bioinformatics and biostatistics. The team also develops tools and databases to meet the growing needs of the scientific community, both academic and industrial.

BIC’s Missions

  • Providing guidance to biologists
  • Supporting scientific projects requiring bioinformatics tools
  • Offering occasional staff training
  • Organizing thematic workshops for technological monitoring in bioinformatics

BIC’s Areas of Expertise

  • Processing omics data
  • Molecular phylogeny
  • Machine and deep learning
  • Scientific output in the form of publications, conference presentations, or patent filings
  • BIC’s activities are primarily intended for BIAM personnel, but its members may also collaborate with other public or industrial organizations

BIC actions are primarily aimed at BIAM staff, but its members can also collaborate with other public or industrial organizations.

BIC’s Achievements

Since its creation in 2021, the BIC has undertaken the following actions:

  • Organized 6 training sessions on genomics, sequence analysis, biostatistics, and molecular phylogeny at BIAM, open to all academics in the PACA region (around one hundred participants).
  • Established a user support service with the development and deployment of two databases (http://www.p2cs.org/and http://www.p2rp.org/), attracting up to 2,000 connections per month.
  • Developed and deployed bioinformatics tools
  • Analyzed and contributed to around fifteen research projects since 2022, involving sequence, phylogeny, transcriptomic, or proteomic analyses.

BIC platform contact : Caroline.monteil@cea.fr

Head of the BIC platform

Caroline Monteil

Key words

Bioinformatique; Transcriptomiques; Phylogénie; Protéomiques; Traitement des données omiques ; Phylogénie moléculaire ; Apprentissage automatique ; Apprentissage profond