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Sequence-based heuristics for faster annotation of non-coding RNA families
Author(s) -
Zasha Weinberg,
Walter L. Ruzzo
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/bti743
Subject(s) - heuristics , computer science , non coding rna , source code , heuristic , annotation , theoretical computer science , computational biology , rna , artificial intelligence , biology , genetics , gene , operating system
Non-coding RNAs (ncRNAs) are functional RNA molecules that do not code for proteins. Covariance Models (CMs) are a useful statistical tool to find new members of an ncRNA gene family in a large genome database, using both sequence and, importantly, RNA secondary structure information. Unfortunately, CM searches are extremely slow. Previously, we created rigorous filters, which provably sacrifice none of a CM's accuracy, while making searches significantly faster for virtually all ncRNA families. However, these rigorous filters make searches slower than heuristics could be.

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