What do we do?
At the Evolutionary Bioinformatics Group we are mainly concerned with determining the role of evolutionary innovation and constraint in animals. For this, we develop methods and databases to extract reliable information from genome and transcriptome data. These databases include Bgee, a database for gene expression evolution, and Selectome, a database of positive selection. While developing these resources, we also conduct research on ontologies, biocuration, and high-performance computing. Our biological focus is to link Evo-Devo with phylogenomics. Notably, we study the role of gene duplication in the divergence between genes and between species.
We added a new gene page to our Bgee database of gene expression evolution. Thanks to a new algorithm which ranks expression information of different types – from RNA-seq to in situ hybridization – we are now able to present the most relevant expression patterns for a gene, highlighting them among hundreds of expression patterns in different conditions.
We also distributed an R package allowing to access all Bgee data and to perform TopAnat computations. A manuscript describing it is in preprint at F1000research Bioconductor channel.
The group participated in a new paper resulting from the Master’s degree course “Sequence a genome”, during which Master’s degree students sequence, assemble and annotate new bacterial genomes, using for the first time PacBio and RNA-seq. The paper is based directly on the students’ work during the class.
Main publications 2016
- Kryuchkova N., Robinson-Rechavi M., 2016. A benchmark of gene expression tissue-specificity metrics. Briefings in Bioinformatics online doi: 10.1093/bib/bbw008
- Davydov I.I., Robinson-Rechavi M., Salamin N., 2016. State aggregation for fast likelihood computations in molecular evolution. Bioinformatics in press doi: 10.1093/bioinformatics/btw632
- Kryuchkova N., Robinson-Rechavi M., 2016. Tissue-specificity of gene expression diverges slowly between orthologs, and rapidly between paralogs. PLOS Computational Biology in press