Finding sRNA generative locales from high-throughput sequencing data with NiBLS.
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.
MacLean
Daniel
D
The Sainsbury Laboratory, John Innes Centre, Colney Lane, Norwich NR47UH, UK. dan.maclean@sainsbury-laboratory.ac.uk
Moulton
Vincent
V
Studholme
David J
DJ
D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D035683:MicroRNAs / Q000737:chemistry*; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
D035683:MicroRNAs