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  • 1.
    Ameur, Adam
    et al.
    Swedish Inst. of Computer Science.
    Aurell, Erik
    Swedish Inst. of Computer Science.
    Carlsson, Mats
    Swedish Inst. of Computer Science.
    Orzechowski Westholm, Jakub
    Swedish Inst. of Computer Science.
    Global gene expression analysis by combinatorial optimization2004In: In Silico Biology, ISSN 1386-6338, Vol. 4, no 2, p. 225-241Article in journal (Refereed)
    Abstract [en]

    Generally, there is a trade-off between methods of gene expression analysis that are precise but labor-intensive, e.g. RT-PCR, and methods that scale up to global coverage but are not quite as quantitative, e.g. microarrays. In the present paper, we show how how a known method of gene expression profiling (K. Kato, Nucleic Acids Res. 23, 3685-3690 (1995)), which relies on a fairly small number of steps, can be turned into a global gene expression measurement by advanced data post-processing, with potentially little loss of accuracy. Post-processing here entails solving an ancillary combinatorial optimization problem. Validation is performed on in silico experiments generated from the FANTOM data base of full-length mouse cDNA. We present two variants of the method. One uses state-of-the-art commercial software for solving problems of this kind, the other a code developed by us specifically for this purpose, released in the public domain under GPL license.

  • 2.
    Andrade, Jorge
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology.
    Berglund, Lisa
    KTH, School of Biotechnology (BIO), Gene Technology.
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Gene Technology.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    Using Grid Technology for Computationally Intensive Applied Bioinformatics Analyses2006In: In Silico Biology, ISSN 1386-6338, Vol. 6, no 6, p. 495-504Article in journal (Refereed)
    Abstract [en]

    For several applications and algorithms used in applied bioinformatics, a bottle neck in terms of computational time may arise when scaled up to facilitate analyses of large datasets and databases. Re-codification, algorithm modification or sacrifices in sensitivity and accuracy may be necessary to accommodate for limited computational capacity of single work stations. Grid computing offers an alternative model for solving massive computational problems by parallel execution of existing algorithms and software implementations. We present the implementation of a Grid-aware model for solving computationally intensive bioinformatic analyses exemplified by a blastp sliding window algorithm for whole proteome sequence similarity analysis, and evaluate the performance in comparison with a local cluster and a single workstation. Our strategy involves temporary installations of the BLAST executable and databases on remote nodes at submission, accommodating for dynamic Grid environments as it avoids the need of predefined runtime environments (preinstalled software and databases at specific Grid-nodes). Importantly, the implementation is generic where the BLAST executable can be replaced by other software tools to facilitate analyses suitable for parallelisation. This model should be of general interest in applied bioinformatics. Scripts and procedures are freely available from the authors.

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