ARCS: An aggregated related column scoring scheme for aligned sequences

Bin Song, Guangyu Chen, Jacek Szymanski, Guo Qiang Zhang, Anthony K.H. Tung, Jaewoo Kang, Sun Kim, Jiong Yang

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)

    Abstract

    Motivation: Biologists frequently align multiple biological sequences to determine consensus sequences and/or search for predominant residues and conserved regions. Particularly, determining conserved regions in an alignment is one of the most important activities. Since protein sequences are often several-hundred residues or longer, it is difficult to distinguish biologically important conserved regions (motifs or domains) from others. The widely used tools, Logos, Al2co, Confind, and the entropy-based method, often fail to highlight such regions. Thus a computational tool that can highlight biologically important regions accurately will be highly desired. Results: This paper presents a new s coring scheme ARCS (Aggregated Related Column Score) for aligned biological sequences. ARCS method considers not only the traditional character similarity measure but also column correlation. In an extensive experimental evaluation using 533 PROSITE patterns, ARCS is able to highlight the motif regions with up to 77.7% accuracy corresponding to the top three peaks.

    Original languageEnglish
    Pages (from-to)2326-2332
    Number of pages7
    JournalBioinformatics
    Volume22
    Issue number19
    DOIs
    Publication statusPublished - 2006 Oct

    ASJC Scopus subject areas

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

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