PMFastR: a new approach to multiple RNA structure alignment

Authors:
Daniel DeBlasio;Jocelyne Bruand;Shaojie Zhang
Affiliations:
University of Central Florida, Orlando, FL;University of California, San Diego, La Jolla, CA;University of Central Florida, Orlando, FL
Venue:
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Year:
2009

Citing 8
Cited 1

Alignment of RNA base pairing probability matrices

Bioinformatics
Exploiting conserved structure for faster annotation of non-coding RNAs without loss of accuracy

Bioinformatics
MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons

Bioinformatics
Searching Genomes for Noncoding RNA Using FastR

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A sequence-based filtering method for ncRNA identification and its application to searching for riboswitch elements

Bioinformatics
StrAl: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time

Bioinformatics
Multiple structural alignment and clustering of RNA sequences

Bioinformatics
Clustal W and Clustal X version 2.0

Bioinformatics

Prediction of RNA secondary structure including kissing hairpin motifs

WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multiple RNA structure alignment is particularly challenging because covarying mutations make sequence information alone insufficient. Many existing tools for multiple RNA alignments first generate pairwise RNA structure alignments and then build the multiple alignment using only the sequence information. Here we present PMFastR, an algorithm which iteratively uses a sequence-structure alignment procedure to build a multiple RNA structure alignment. PMFastR has low memory consumption allowing for the alignment of large sequences such as 16S and 23S rRNA. The algorithm also provides a method to utilize a multi-core environment. Finally, we present results on benchmark data sets from BRAliBase, which shows PMFastR outperforms other state-of-the-art programs. Furthermore, we regenerate 607 Rfam seed alignments and show that our automated process creates similar multiple alignments to the manually-curated Rfam seed alignments.