ARCS: an aggregated related column scoring scheme for aligned sequences.
BACKGROUND
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.


BACKGROUND
The source code is available on http://bio.informatics.indiana.edu/projects/arcs and http://goldengate.case.edu/projects/arcs
Song
Bin
B
Electrical Engineering and Computer Science Department, Case Western Reserve University, Cleveland, OH, USA.
Choi
Jeong-Hyeon
JH
Chen
Guangyu
G
Szymanski
Jacek
J
Zhang
Guo-Qiang
GQ
Tung
Anthony K H
AK
Kang
Jaewoo
J
Kim
Sun
S
Yang
Jiong
J
D000465:Algorithms; D000595:Amino Acid Sequence; D017124:Conserved Sequence; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D012984:Software
D011506:Proteins