Martin Kircher

Martin is a Computational Molecular Biologist by training (B.Sc./M.Sc.hon. at Saarland University, Germany) and received his PhD (Dr. rer. nat.) in Computer Sciences from the University of Leipzig, Germany, in 2011. He has a broad interest in genomics, epigenetics and evolution as well as the development of sequencing protocols, technologies and bioinformatics approaches to advance our capabilities in these fields.

Martin did his Master's thesis on microRNAs and genomic imprinting in the DLK1/GTL2 region in the labs of Jörn Walter and Thomas Lengauer. In 2007, he entered the field of ancient DNA at a time when high-throughput sequencing completely transformed Svante Pääbo's department at the Max Planck Institute for Evolutionary Anthropology. During his PhD, Martin was advised by Janet Kelso and Peter Stadler. He developed a base caller for the Illumina/Solexa systems (IBIS), implemented the analysis pipelines that underlie the Neandertal and Denisova genomes and analysed NGS data for different ancient DNA and comparative gene expression studies.

In the spring of 2012, he moved to the University of Washington (Seattle), when he joined Jay Shendure's lab in the development of methods for the functional scoring of variants (CADD), studying epigenetic signatures and nucleosome patterns in cell-free DNA, and developing methods to functionally assess regulatory variants using massively parallel reporter assays (MPRA). For more than three years, Martin was a member of the analysis group of the University of Washington's Center for Mendelian Genomics and has been actively involved in several studies identifying disease causal variants.

He will start a computational research group at the Berlin Institute of Health (BIH) in Germany in March 2017. The research will be in the areas of sequence analysis, data mining and functional genomics. One focus will be non-coding sequence variation, specifically analysis of data from Massively Parallel Reporter Assays (MPRA) for promoter and enhancer variants as well computational approaches for identifying functionally relevant genetic changes.



Last updated: November 2016

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