Structure-based whole-genome realignment reveals many novel noncoding RNAs
Name
Will-2013-Structure-based whole-genome realignment reveals many novel noncoding RNAs.pdf
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3 MB
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Author(s)
Will, Sebastian
Berger, Bonnie
Yu, Michael
Date Issued
January 2013
Journal
Genome Research
Publisher
Cold Spring Harbor Laboratory Press
Citation
Will, S., M. Yu, and B. Berger. “Structure-based whole-genome realignment reveals many novel noncoding RNAs.” Genome Research 23, no. 6 (June 1, 2013): 1018-1027. © 2013, Published by Cold Spring Harbor Laboratory Press
Version
Final published version
Abstract
Recent genome-wide computational screens that search for conservation of RNA secondary structure in whole-genome alignments (WGAs) have predicted thousands of structural noncoding RNAs (ncRNAs). The sensitivity of such approaches, however, is limited, due to their reliance on sequence-based whole-genome aligners, which regularly misalign structural ncRNAs. This suggests that many more structural ncRNAs may remain undetected. Structure-based alignment, which could increase the sensitivity, has been prohibitive for genome-wide screens due to its extreme computational costs. Breaking this barrier, we present the pipeline REAPR (RE-Alignment for Prediction of structural ncRNA), which efficiently realigns whole genomes based on RNA sequence and structure, thus allowing us to boost the performance of de novo ncRNA predictors, such as RNAz. Key to the pipeline's efficiency is the development of a novel banding technique for multiple RNA alignment. REAPR significantly outperforms the widely used predictors RNAz and EvoFold in genome-wide screens; in direct comparison to the most recent RNAz screen on D. melanogaster, REAPR predicts twice as many high-confidence ncRNA candidates. Moreover, modENCODE RNA-seq experiments confirm a substantial number of its predictions as transcripts. REAPR's advancement of de novo structural characterization of ncRNAs complements the identification of transcripts from rapidly accumulating RNA-seq data.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
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DOI of Published Version
https://doi.org/10.1101/gr.137091.111
