Computational Genomics - overview
Research at the interface of algorithmics and genomics
We pursue basic and exploratory research at the interface of algorithmics and genomics. We address genomics (and beyond) related questions through mathematical and statistical modeling, combinatorics and algorithmics.
Research Areas
Cancer Genomics
Analyzing sequencing data (DNA, RNA, ChIP ...), including the use of Watson, for genomic medicine studies
TDA (Topological Data Analysis)
Persistent Homology and Statistical Learning on Genomic data.
Understanding the genomes and transcriptomes from communities of microorganisms in several context (e.g. food safety)
Population Genomics
Understanding the dynamics of recombining populations, human migratory history, and other stories from genetic data.
Plant Genomics
Analyzing plant traits and populations.
Pattern Discovery
Researching comparative genomics, combinatorial, k-mer, and epigenomic patterns.
Related Links
Awards
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Logic, Topology and Genomics
Bring together tools from logic and topology in a novel way, as a method towards analyzing large genomic data-sets.
Group members
IBM 100 Icons of Progress
Computational Genomic Git
Computational Genomics GitHub
Visit our GitHub to see some our latest projects.