TY - GEN
T1 - Improved phylogenetic motif detection using parsimony
AU - Roshan, Usman
AU - Livesay, Dennis R.
AU - La, David
PY - 2005
Y1 - 2005
N2 - We have recently demonstrated (La et al, Proteins, 58:2005) that sequence fragments approximating the overall familial phylogeny, called phylogenetic motifs (PMs), represent a promising protein functional site prediction strategy. Previous results across a structurally and functionally diverse dataset indicate that phylogenetic motifs correspond to a wide variety of known functional characteristics. Phylogenetic motifs are detected using a sliding window algorithm that compares neighbor joining trees on the complete alignment to those on the sequence fragments. In this investigation we identify PMs using heuristic maximum parsimony trees. We show that when using parsimony the functional site prediction accuracy of PMs improves substantially, particularly on divergent datasets. We also show that the new PMs found using parsimony are not necessarily conserved in sequence, and, therefore, would not be detected by traditional motif (information content-based) approaches.
AB - We have recently demonstrated (La et al, Proteins, 58:2005) that sequence fragments approximating the overall familial phylogeny, called phylogenetic motifs (PMs), represent a promising protein functional site prediction strategy. Previous results across a structurally and functionally diverse dataset indicate that phylogenetic motifs correspond to a wide variety of known functional characteristics. Phylogenetic motifs are detected using a sliding window algorithm that compares neighbor joining trees on the complete alignment to those on the sequence fragments. In this investigation we identify PMs using heuristic maximum parsimony trees. We show that when using parsimony the functional site prediction accuracy of PMs improves substantially, particularly on divergent datasets. We also show that the new PMs found using parsimony are not necessarily conserved in sequence, and, therefore, would not be detected by traditional motif (information content-based) approaches.
UR - http://www.scopus.com/inward/record.url?scp=33751174062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33751174062&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2005.38
DO - 10.1109/BIBE.2005.38
M3 - Conference contribution
AN - SCOPUS:33751174062
SN - 0769524761
SN - 9780769524764
T3 - Proceedings - BIBE 2005: 5th IEEE Symposium on Bioinformatics and Bioengineering
SP - 19
EP - 26
BT - Proceedings - BIBE 2005
T2 - BIBE 2005: 5th IEEE Symposium on Bioinformatics and Bioengineering
Y2 - 19 October 2005 through 21 October 2005
ER -