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Article
Non-Coding RNA Covariance Model Combination Using Mixed Primary-Secondary Structure Alignment
Computational Intelligence Methods for Bioinformatics and Biostatistics
  • Jennifer A. Smith, Boise State University
Document Type
Conference Proceeding
Publication Date
1-1-2013
Abstract

Covariance models are very effective for finding new members of non-coding RNA sequence families in genomic data. However, the computation burden of applying CM-based search algorithms can be prohibitive. When annotating the genome of a newly sequenced organism it is usually desired to search the sequence data using a large number of ncRNA families. Computational burden can be reduced if the families are clustered into statistically similar models and a single cluster-average representative model produced. The database is then searched with the representative model for each cluster at a relatively low detection threshold. The output of this pre-filtered database is then processed with the individual family members of the cluster. A base-pair conflict metric has previously been proposed for use in model clustering. In this work an alternative metric using standard alignment algorithms and a special mixed primary-secondary structure scoring matrix is proposed.

Comments

This title is within the series Lecture Notes in Computer Science.

Citation Information
Jennifer A. Smith. "Non-Coding RNA Covariance Model Combination Using Mixed Primary-Secondary Structure Alignment" Computational Intelligence Methods for Bioinformatics and Biostatistics (2013)
Available at: http://works.bepress.com/jennifer_smith/22/