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Projects
- Algorithm Engineering
- Predict IV
- Algorithm Engineering for High Throughput Sequencing Data
- OpenMS ( Knut Reinert )
- SeqAn ( Knut Reinert Andreas Döring David Weese )
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Algorithm Engineering
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There are two main research goals in the proposed project: (1) To design, analyze, implement, and experimentally validate efficient and versatile genome comparison algorithms based upon suitable computation models and (2) to integrate all required algorithmic components in the SeqAn library for biological sequence analysis for the purpose of disseminating the core algorithms and data structures to the bioinformatics and algorithm engineering community. The software developed will be part of the SeqAn library. |
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OpenMS - A C++ library for
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The OpenMS library is intended to provide a flexible framework for the differential analysis of HPLC/MS data. OpenMS offers algorithms for peak picking, protein ID, labeled and label-free quantitation. OpenMS has been and is being used in several BMBF and EU projects. |
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Predict IV
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The goal of Predict IV is the profiling the toxicity of new drugs: a non animal-based approach integrating toxico-dynamics and biokinetics. Our group develops as a partner algorithms for iTRAQ labeled MS measurements. |
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SeqAn - A C++ library for
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The software library SEQAN is intended to allow rapid prototyping of algorithms for analyzing large sets of sequences.The main emphasis lies on NGS data. SeqAn supports handling of large sets of reads and is in worldwide use for developing analysis tools for genomic sequence analysis. |
