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Thomas Sakoparnig
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.
Thomas Sakoparnig
 thomas_sakoparnig |
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Current Research Projects
Associating chromatin proteins, DNA binding factors and histone modifications with gene expression on a genome wide scale
Research Interests
- Statistical machine learning
- Next Generation Sequencing
- Algorithm design
- High Performance Computing
Publications
Thesis
- Improved MicroRNA Target Prediction Based on a Proteomic Approach, MSc Thesis, 2010
Curriculum Vitae
| 2010 | Since November, PhD Student at the D-BSSE of ETH Zurich |
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2010 |
MSc with Distinction in Bioinformatics, King's College London, London, UK |
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2009 |
Bachelor degree in Biomedical Informatics, University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria |
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