Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/btl398
Title: ARCS: An aggregated related column scoring scheme for aligned sequences
Authors: Song, B.
Chen, G.
Szymanski, J.
Zhang, G.-Q.
Tung, A.K.H. 
Kang, J.
Kim, S.
Yang, J.
Issue Date: 2006
Source: Song, B., Chen, G., Szymanski, J., Zhang, G.-Q., Tung, A.K.H., Kang, J., Kim, S., Yang, J. (2006). ARCS: An aggregated related column scoring scheme for aligned sequences. Bioinformatics 22 (19) : 2326-2332. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/btl398
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. © 2006 Oxford University Press.
Source Title: Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/39857
ISSN: 13674803
DOI: 10.1093/bioinformatics/btl398
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