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Auflistung nach Autor:in "Strickert, Marc"

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  • Konferenzbeitrag
    CUDA-based multi-core implementation of MDS-based bioinformatics algorithms
    (German conference on bioinformatics 2009, 2009) Fester, Thilo; Schreiber, Falk; Strickert, Marc
    Solving problems in bioinformatics often needs extensive computational power. Current trends in processor architecture, especially massive multi-core processors for graphic cards, combine a large number of cores into a single chip to improve the overall performance. The Compute Unified Device Architecture (CUDA) provides programming interfaces to make full use of the computing power of graphics processing units. We present a way to use CUDA for substantial performance improvement of methods based on multi-dimensional scaling (MDS). The suitability of the CUDA architecture as a high-performance computing platform is studied by adapting a MDS algorithm on specific hardware properties. We show how typical bioinformatics problems related to dimension reduction and network layout benefit from the multi-core implementation of the MDS algorithm. CUDA-based methods are introduced and compared to standard solutions, demonstrating 50-fold acceleration and above.
  • Konferenzbeitrag
    Utilizing promoter pair orientations for HMM-based analysis of ChIP-chip data
    (German Conference on Bioinformatics, 2008) Seifert, Michael; Keilwagen, Jens; Strickert, Marc; Grosse, Ivo
    Array-based analysis of chromatin immunoprecipitation data (ChIP-chip) is a powerful technique for identifying DNA target regions of individual transcription factors. Here, we present three approaches, a standard log-fold-change analysis (LFC), a basic method based on a Hidden Markov Model (HMM), and an ex- tension of the HMM approach to an HMM with scaled transition matrices (SHMM) to incorporate different promoter pair orientations. We compare the prediction of ABI3 target genes for the three methods and evaluate these genes using Geneves- tigator expression profiles and transient assays. We find that the application of the SHMM leads to a superior identification of ABI3 target genes. The software and the ChIP-chip data set used in our case study can be downloaded from http://dig.ipk- gatersleben.de/SHMMs/ChIPchip/ChIPchip.html.