dc.contributor.author | MacLean, D | |
dc.contributor.author | Moulton, V | |
dc.contributor.author | Studholme, DJ | |
dc.date.accessioned | 2015-06-12T15:24:29Z | |
dc.date.issued | 2010-02-18 | |
dc.description.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. | en_GB |
dc.description.sponsorship | Gatsby Charitable Foundation | en_GB |
dc.identifier.citation | Vol. 11, pp. 93 | en_GB |
dc.identifier.doi | 10.1186/1471-2105-11-93 | |
dc.identifier.other | 1471-2105-11-93 | |
dc.identifier.uri | http://hdl.handle.net/10871/17526 | |
dc.language.iso | en | en_GB |
dc.publisher | BioMed Central | en_GB |
dc.relation.url | http://www.ncbi.nlm.nih.gov/pubmed/20167070 | en_GB |
dc.relation.url | http://www.biomedcentral.com/1471-2105/11/93 | en_GB |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en_GB |
dc.subject | Algorithms | en_GB |
dc.subject | Base Sequence | en_GB |
dc.subject | Computational Biology | en_GB |
dc.subject | MicroRNAs | en_GB |
dc.subject | Sequence Alignment | en_GB |
dc.subject | Sequence Analysis, RNA | en_GB |
dc.subject | Software | en_GB |
dc.title | Finding sRNA generative locales from high-throughput sequencing data with NiBLS. | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2015-06-12T15:24:29Z | |
dc.identifier.issn | 1471-2105 | |
exeter.place-of-publication | England | |
dc.description | Journal Article | en_GB |
dc.description | Copyright © 2010 MacLean et al; licensee BioMed Central Ltd. | en_GB |
dc.identifier.journal | BMC Bioinformatics | en_GB |