ARCS: an aggregated related column scoring scheme for aligned sequences

  • Authors:

  • Bin Song;Jeong-Hyeon Choi;Guangyu Chen;Jacek Szymanski;Guo-Qiang Zhang;Anthony K. H. Tung;Jaewoo Kang;Sun Kim;Jiong Yang

  • Affiliations:

  • Electrical Engineering and Computer Science Department, Case Western Reserve University Cleveland, OH, USA;School of Informatics, Indiana University Bloomington, IN, USA;Electrical Engineering and Computer Science Department, Case Western Reserve University Cleveland, OH, USA;Electrical Engineering and Computer Science Department, Case Western Reserve University Cleveland, OH, USA;Electrical Engineering and Computer Science Department, Case Western Reserve University Cleveland, OH, USA;Department of Computer Science, National University of Singapore Singapore;Department of Computer Science and Engineering, Korea University Seoul, Korea;School of Informatics, Indiana University Bloomington, IN, USA;Electrical Engineering and Computer Science Department, Case Western Reserve University Cleveland, OH, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2006

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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 scoring 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. Availability: The source code is available on http://bio.informatics.indiana.edu/projects/arcs and http://goldengate.case.edu/projects/arcs Contacts: jiong.yang@case.edu, sunkim2@indiana.edu Supplementary Material:http://bio.informatics.indiana.edu/projects/arcs and http://goldengate.case.edu/projects/arcs