Research & Activities

We are passionate about developing new algorithms, machine learning and deep learning methods, and their applications to genomics. Our current focus is on characterizing human genomes, sequenced by exciting new technologies. Some of the current projects in the group include variation discovery in sequenced genomes and quantifying cancer evolution.
View Research & Other Activities →

Structural Variation

Developing algorithms for characterizing structural variations focused on complex and repetitive elements, rare variants and clinical relevant variants.

Cancer Evolution

Quantifying cancer evolution in multiple samples and reconstruction of tumor lineage trees using novel computational methods.

Functional Genomics

Advancing computational models for the purpose of analyzing RNA-seq, Methyl-Seq, ATAC-seq, and other functional assays in order to prioritize variants and discover driver events.


Below you can see some highlights. Please also check publications page →

1000 Genomes Project

A catalog of human genome variations in population-scale.


Various methods in Bioinformatics and/or ISMB conferences. Algorithms for Next Generation Sequencing, Structural Variation discovery, tumor heterogeneity and Protein-protein intraction prediction.

Handling Multiple Sequenced Genomes

The CommonLAW package presented on the cover of Genome Research introduces novel combinatorial formulations and algorithms for structural variation discovery among a number of sequenced donor genomes, with the help of a complete reference genome. CommonLAW significantly reduces the false positive rate in detecting structural variation events when compared with conventional methods. (Cover illustration by Azalia Musa, modified by Andres Wanner and Iman Hajirasouliha.)

Want to discuss a project?

We are always keen to establish new collaborations with computational scientists and experimentalists.

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