Open AccessRNA secondary structural alignment with conditional random fields
Author(s) -
Kengo Sato,
Yasubumi Sakakibara
Publication year - 2005
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bti1139
Subject(s) - crfs , discriminative model , conditional random field , computer science , overfitting , structural alignment , rna , artificial intelligence , coding (social sciences) , algorithm , computational biology , sequence alignment , data mining , pattern recognition (psychology) , mathematics , genetics , gene , biology , statistics , artificial neural network , peptide sequence
The computational identification of non-coding RNA regions on the genome is currently receiving much attention. However, it is essentially harder than gene-finding problems for protein-coding regions because non-coding RNA sequences do not have strong statistical signals. Since comparative sequence analysis is effective for non-coding RNA detection, efficient computational methods are expected for structural alignment of RNA sequences. Several methods have been proposed to accomplish the structural alignment tasks for RNA sequences, and we found that one of the most important points is to estimate an accurate score matrix for calculating structural alignments.
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