TY - JOUR
T1 - GLOOME
T2 - Gain loss mapping engine
AU - Cohen, Ofir
AU - Ashkenazy, Haim
AU - Belinky, Frida
AU - Huchon, Dorothée
AU - Pupko, Tal
N1 - Funding Information:
Funding: Israel Science Foundation (878/09 and 600/06, respectively to T.P. and D.H.); D.H. and T.P. are also supported by the National Evolutionary Synthesis Center (NESCent), NSF #EF-0905606. O.C. and H.A. are fellows of the Edmond J. Safra program in bioinformatics.
PY - 2010/11
Y1 - 2010/11
N2 - SUMMARY: The evolutionary analysis of presence and absence profiles (phyletic patterns) is widely used in biology. It is assumed that the observed phyletic pattern is the result of gain and loss dynamics along a phylogenetic tree. Examples of characters that are represented by phyletic patterns include restriction sites, gene families, introns and indels, to name a few. Here, we present a user-friendly web server that accurately infers branch-specific and site-specific gain and loss events. The novel inference methodology is based on a stochastic mapping approach utilizing models that reliably capture the underlying evolutionary processes. A variety of features are available including the ability to analyze the data with various evolutionary models, to infer gain and loss events using either stochastic mapping or maximum parsimony, and to estimate gain and loss rates for each character analyzed.
AB - SUMMARY: The evolutionary analysis of presence and absence profiles (phyletic patterns) is widely used in biology. It is assumed that the observed phyletic pattern is the result of gain and loss dynamics along a phylogenetic tree. Examples of characters that are represented by phyletic patterns include restriction sites, gene families, introns and indels, to name a few. Here, we present a user-friendly web server that accurately infers branch-specific and site-specific gain and loss events. The novel inference methodology is based on a stochastic mapping approach utilizing models that reliably capture the underlying evolutionary processes. A variety of features are available including the ability to analyze the data with various evolutionary models, to infer gain and loss events using either stochastic mapping or maximum parsimony, and to estimate gain and loss rates for each character analyzed.
UR - http://www.scopus.com/inward/record.url?scp=78149254832&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btq549
DO - 10.1093/bioinformatics/btq549
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AN - SCOPUS:78149254832
SN - 1367-4803
VL - 26
SP - 2914
EP - 2915
JO - Bioinformatics
JF - Bioinformatics
IS - 22
M1 - btq549
ER -