We focus on finding a consensus motif of a set of
homologous or functionally related RNA molecules.
Recent approaches to this problem have been limited to
simple motifs, require sequence alignment, and make
prior assumptions concerning the data set. We use
genetic programming to predict RNA consensus motifs
based solely on the data set. Our system -- dubbed
GeRNAMo (Genetic programming of RNA Motifs) -- predicts
the most common motifs without sequence alignment and
is capable of dealing with any motif size. Our program
only requires the maximum number of stems in the motif,
and if prior knowledge is available the user can
specify other attributes of the motif (e.g., the range
of the motif's minimum and maximum sizes), thereby
increasing both sensitivity and speed. We describe
several experiments using either ferritin iron response
element (IRE); signal recognition particle (SRP); or
microRNA sequences, showing that the most common motif
is found repeatedly, and that our system offers
substantial advantages over previous methods.
%0 Journal Article
%1 Shahar:2007:tCBB
%A Michal, Shahar
%A Ivry, Tor
%A Schalit-Cohen, Omer
%A Sipper, Moshe
%A Barash, Danny
%D 2007
%J IEEE/ACM Transactions on Computational Biology and
Bioinformatics
%K RNA, STGP algorithms, common genetic microRNA, motif, programming,
%N 4
%P 596--610
%R doi:10.1109/tcbb.2007.1045
%T Finding a Common Motif of RNA Sequences Using
Genetic Programming: The GeRNAMo System
%V 4
%X We focus on finding a consensus motif of a set of
homologous or functionally related RNA molecules.
Recent approaches to this problem have been limited to
simple motifs, require sequence alignment, and make
prior assumptions concerning the data set. We use
genetic programming to predict RNA consensus motifs
based solely on the data set. Our system -- dubbed
GeRNAMo (Genetic programming of RNA Motifs) -- predicts
the most common motifs without sequence alignment and
is capable of dealing with any motif size. Our program
only requires the maximum number of stems in the motif,
and if prior knowledge is available the user can
specify other attributes of the motif (e.g., the range
of the motif's minimum and maximum sizes), thereby
increasing both sensitivity and speed. We describe
several experiments using either ferritin iron response
element (IRE); signal recognition particle (SRP); or
microRNA sequences, showing that the most common motif
is found repeatedly, and that our system offers
substantial advantages over previous methods.
@article{Shahar:2007:tCBB,
abstract = {We focus on finding a consensus motif of a set of
homologous or functionally related RNA molecules.
Recent approaches to this problem have been limited to
simple motifs, require sequence alignment, and make
prior assumptions concerning the data set. We use
genetic programming to predict RNA consensus motifs
based solely on the data set. Our system -- dubbed
GeRNAMo (Genetic programming of RNA Motifs) -- predicts
the most common motifs without sequence alignment and
is capable of dealing with any motif size. Our program
only requires the maximum number of stems in the motif,
and if prior knowledge is available the user can
specify other attributes of the motif (e.g., the range
of the motif's minimum and maximum sizes), thereby
increasing both sensitivity and speed. We describe
several experiments using either ferritin iron response
element (IRE); signal recognition particle (SRP); or
microRNA sequences, showing that the most common motif
is found repeatedly, and that our system offers
substantial advantages over previous methods.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Michal, Shahar and Ivry, Tor and Schalit-Cohen, Omer and Sipper, Moshe and Barash, Danny},
biburl = {https://www.bibsonomy.org/bibtex/2e386e446722e8208bc7b47181af37b7a/brazovayeye},
doi = {doi:10.1109/tcbb.2007.1045},
interhash = {4b710e3d2efa1126ab6c01b422291b69},
intrahash = {e386e446722e8208bc7b47181af37b7a},
journal = {IEEE/ACM Transactions on Computational Biology and
Bioinformatics},
keywords = {RNA, STGP algorithms, common genetic microRNA, motif, programming,},
month = {October-December},
notes = {ECJ},
number = 4,
pages = {596--610},
size = {15 pages},
timestamp = {2008-06-19T17:51:31.000+0200},
title = {Finding a Common Motif of {RNA} Sequences Using
Genetic Programming: The Ge{RNAM}o System},
volume = 4,
year = 2007
}