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News
May 1
Our paper Reliable detection of subclonal
single-nucleotide variants in tumor cell populations appeared in Nature Communications today (see also ETH Life). In this work, we present the deepSNV algorithm and demonstrate its capability to detect subclonal mutations present in only 1/10,000 cells.
Gerstung et al. (2012) Nat Commun 3:811. DOI: 10.1038/ncomms1814.

The rapid development of new high-throughput sequencing technologies has opened new avenues for biological investigation on an ever-growing scale, and there is great interest among statisticians in the methodological challenges inherent in this quickly evolving domain. Addressing the new statistical demands is a prerequisite for sustained progress in biological and biomedical research predicated on these new high-throughput technologies.
This interdisciplinary workshop is intended to be a forum for (i) the dissemination of cutting-edge biotechnological and methodological developments and (ii) the identification of challenging data analysis problems. The focus is on statistical, mathematical, and computational aspects addressing concrete real-world biological questions and medical applications. Specific workshop topics include algorithms for the analysis of deep sequencing data, applications to infectious diseases and cancer, integration of heterogeneous high-throughput genomic data types, and advances in statistical computing for these problems.
June 5 to 10, 2011
Centro Stefano Franscini, Monte Verita, Ascona, Switzerland
Schedule (updated June 6)
Abstract book (updated May 31)
Slides of the seminars and posters are available as PDF files from the dedicate page.
Margaret Taub and Bernhard Renard were the winners of the CSF Award for, respectively, the best presentation and the best poster.
Niko Beerenwinkel, Darlene Goldstein, Peter Bühlmann

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