Finding sRNA generative locales from high-throughput sequencing data with NiBLS

MacLean, Daniel, Moulton, Vincent and Studholme, David J. (2010) Finding sRNA generative locales from high-throughput sequencing data with NiBLS. BMC Bioinformatics, 11. ISSN 1471-2105

Full text not available from this repository. (Request a copy)

Abstract

Background: Next-generation sequencing technologies allow researchers to obtain millions of sequence reads in a single experiment. One important use of the technology is the sequencing of small non-coding regulatory RNAs and the identification of the genomic locales from which they originate. Currently, there is a paucity of methods for finding small RNA generative locales. Results: We describe and implement an algorithm that can determine small RNA generative locales from high-throughput sequencing data. The algorithm creates a network, or graph, of the small RNAs by creating links between them depending on their proximity on the target genome. For each of the sub-networks in the resulting graph the clustering coefficient, a measure of the interconnectedness of the subnetwork, is used to identify the generative locales. We test the algorithm over a wide range of parameters using RFAM sequences as positive controls and demonstrate that the algorithm has good sensitivity and specificity in a range of Arabidopsis and mouse small RNA sequence sets and that the locales it generates are robust to differences in the choice of parameters. Conclusions: NiBLS is a fast, reliable and sensitive method for determining small RNA locales in high-throughput sequence data that is generally applicable to all classes of small RNA.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Vishal Gautam
Date Deposited: 10 Mar 2011 10:33
Last Modified: 27 Sep 2020 23:29
URI: https://ueaeprints.uea.ac.uk/id/eprint/22031
DOI: 10.1186/1471-2105-11-93

Actions (login required)

View Item View Item