Kircher Lab - Computational Genome Biology
Martin Kircher joined BIH in March 2017 as a new junior research group leader in the field of Bioinformatics. He describes the work of his 'Computational Genome Biology' group and experience in a brief portrait.
Our research focuses on computational approaches of identifying functionally relevant genetic changes in disease and adaptation as well as developing more sensitive methods in diagnostics (especially exome, genome and cell-free DNA sequencing). Generally, our research spans the fields of sequence analysis, data mining, machine learning and functional genomics.
What are you working on? (With which objectives are you dealing?)
Based on a broad interest in genetics, epigenetics, and human adaptation, my group develops computational solutions to overcome technical and experimental obstacles in high-throughput sequencing-based protocols. Our main focus area are computational approaches for identifying functionally relevant genetic.
We develop and maintain a widely used variant effect scoring tool (Combined Annotation Dependent Depletion, CADD), that uses machine learning to integrate more than 80 different gene-based and genome-wide annotations. CADD was the first tool to predict variant deleteriousness for all possible single base-pair alterations genome-wide, while also allowing to score multi-base and insertion/deletion changes.
Further, we collaborate with experimental groups to obtain and analyze experimental measures of non-coding sequence activity, specifically from Massively Parallel Reporter Assays (MPRA). Despite the majority of all mutations affecting non-coding sequences and a growing evidence of substantial phenotypic effects as well as clinical relevance, alterations in regulatory sequences remain less well understood than those in coding sequence. The lab uses experimental data to infer computational models of regulatory sequence effects with the goal of contributing to a better understanding of regulatory sequence function and later integrating regulatory sequence models in the next generation of genome-wide variant scores.
How can patients benefit from your research one day?
With a significant reduction in sequencing costs, whole exome and genome sequencing already became the first line of diagnosis for rare diseases over the last few years. This trend will continue and eventually sequencing will become a standard clinical tool. My group develops computational tools to analyze these sequencing data sets and to provide clinicians with summaries of the information available for identified genetic alterations, thereby directly supporting and improving medical decision making.
About the JRG Leader
Dr Kircher's training (B.Sc. and M.Sc. hon.) is in bioinformatics/computational molecular biology (Saarland University, 2003–2007) and he completed his PhD (Dr. rer. nat.) as a collaboration between the MPI for evolutionary Anthropology and the Mathematics and Computer Sciences Department of Leipzig University (2007–2011) supervised by Janet Kelso, PhD, Prof. Svante Pääbo and Prof. Peter Stadler. Between 2012 and early 2017, he held a Senior Research Fellow position with Jay Shendure in the Department of Genome Sciences at the University of Washington, Seattle, USA. He was an analysis group member of the University of Washington's Center for Mendelian Genomics and has been actively involved in several studies identifying disease causal variants from exome and whole genome sequencing data.
His interests in overcoming specific technical issues while at same time tackling broader scientific questions provided him with hands-on experiences in different research areas: epigenetics (e.g. imprinting, microRNAs, and DNA methylation), high-throughput sequencing (e.g. base calling, multiplex sequencing, quality control, and data processing), gene expression (e.g. tissue- and cell-type specific, during development and between species), ancient DNA and human evolution (e.g. species/sub-species differences, missing evolution, genetic admixture), genetics and rare diseases (e.g. exome and whole genome analysis, linkage analysis), functional genomics (e.g. integration of diverse annotations, massively parallel functional assays), and tissue-of-origin composition of cell-free DNA.
The most important thing for him is ...
... to put his computational skills to good use and advance research, for example by devising computational approaches to answer scientific questions and push technological limits.
F. Inoue, M. Kircher, B. Martin, G.M. Cooper, D.M. Witten, M.T. McManus, N. Ahituv, J. Shendure. A systematic comparison reveals substantial differences in chromosomal versus episomal encoding of enhancer activity, Genome Research, 2016 Nov 9. pii: gr.212092.116.
M.W. Snyder, M. Kircher, A.J. Hill, R.M. Daza, and J. Shendure, Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin, Cell, 2016 Jan 14; 164(1-2):57-68.
M. Kircher, D.M. Witten, P. Jain, B.J. O'Roak, G.M. Cooper, and J. Shendure, A general framework for estimating the relative pathogenicity of human genetic variants, Nature Genetics, 46, 310-315.
M. Meyer, M. Kircher, M. Gansauge, H. Li, F. Racimo, S. Mallick, J.G. Schraiber, F. Jay, K. Prüfer, C. de Filippo, P.H. Sudmant, C. Alkan, Q. Fu, R. Do, N. Rohland, A. Tandon, M. Siebauer, R.E. Green, K. Bryc, A.W. Briggs, U. Stenzel, J. Dabney, J. Shendure, J. Kitzman, M.F. Hammer, M.V. Shunkov, A.P. Derevianko, N. Patterson, A.M. Andrés, E.E. Eichler, M. Slatkin, D. Reich, J. Kelso, and S. Pääbo. A high coverage genome sequence from an archaic Denisovan individual, Science, 2012 Aug 31.
D. Reich, R.E. Green, M. Kircher, J. Krause, N. Patterson, E.Y. Durand, B. Viola, A.W. Briggs, U. Stenzel, P.L.F. Johnson, T. Maricic, J.M. Good, T. Marques-Bonet, C. Alkan, Q. Fu, S. Mallick, H. Li, M. Meyer, E.E. Eichler, M. Stoneking, M. Richards, S. Talamo, M.V. Shunkov, A.P. Derevianko, J.-J. Hublin, J. Kelso, M. Slatkin, and S. Pääbo, Genetic history of an archaic hominin group from Denisova Cave in Siberia, Nature, 468(7327):1053-1060.
M. Kircher, U. Stenzel, and J. Kelso, Improved base calling for the Illumina Genome Analyzer using machine learning strategies, Genome Biology, 10(8):R83.