CoLIde: A bioinformatics tool for CO-expression-based small RNA Loci Identification using high-throughput sequencing data

Mohorianu, Irina-Ioana, Stocks, Matthew Benedict, Wood, John, Dalmay, Tamas ORCID: https://orcid.org/0000-0003-1492-5429 and Moulton, Vincent ORCID: https://orcid.org/0000-0001-9371-6435 (2013) CoLIde: A bioinformatics tool for CO-expression-based small RNA Loci Identification using high-throughput sequencing data. RNA Biology, 10 (7). pp. 1221-1230. ISSN 1547-6286

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Abstract

Small RNAs (sRNAs) are 20–25 nt non-coding RNAs that act as guides for the highly sequence-specific regulatory mechanism known as RNA silencing. Due to the recent increase in sequencing depth, a highly complex and diverse population of sRNAs in both plants and animals has been revealed. However, the exponential increase in sequencing data has also made the identification of individual sRNA transcripts corresponding to biological units (sRNA loci) more challenging when based exclusively on the genomic location of the constituent sRNAs, hindering existing approaches to identify sRNA loci. To infer the location of significant biological units, we propose an approach for sRNA loci detection called CoLIde (Co-expression based sRNA Loci Identification) that combines genomic location with the analysis of other information such as variation in expression levels (expression pattern) and size class distribution. For CoLIde, we define a locus as a union of regions sharing the same pattern and located in close proximity on the genome. Biological relevance, detected through the analysis of size class distribution, is also calculated for each locus. CoLIde can be applied on ordered (e.g., time-dependent) or un-ordered (e.g., organ, mutant) series of samples both with or without biological/technical replicates. The method reliably identifies known types of loci and shows improved performance on sequencing data from both plants (e.g., A. thaliana, S. lycopersicum) and animals (e.g., D. melanogaster) when compared with existing locus detection techniques.

Item Type:	Article
Uncontrolled Keywords:	small rna,srna,microrna,mirna,high throughput sequencing,srna loci,expression level,pattern,srnaome
Faculty \ School:	Faculty of Science > School of Biological Sciences Faculty of Science > School of Computing Sciences Faculty of Science > School of Pharmacy
UEA Research Groups:	Faculty of Science > Research Groups > Computational Biology > Computational biology of RNA (former - to 2018) Faculty of Science > Research Groups > Plant Sciences Faculty of Science > Research Groups > Computational Biology > Phylogenetics (former - to 2018) Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Depositing User:	Pure Connector
Date Deposited:	11 Nov 2013 16:12
Last Modified:	13 Jun 2023 07:59
URI:	https://ueaeprints.uea.ac.uk/id/eprint/44387
DOI:	10.4161/rna.25538

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