miRCat2: Accurate prediction of plant and animal microRNAs from next-generation sequencing datasets
- Submitting institution
-
The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182600993
- Type
- D - Journal article
- DOI
-
10.1093/bioinformatics/btx210
- Title of journal
- Bioinformatics
- Article number
- -
- First page
- 2446
- Volume
- 33
- Issue
- 16
- ISSN
- 1367-4803
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- 17
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- MicroRNAs form an important class of small RNA molecules that regulate the expression of genes by interacting with their target messenger RNAs. We present a novel computational approach called miRCat2 for identifying microRNAs in next generation sequencing data, which incorporates a new entropy-based technique for filtering out noise. miRCat2 can be downloaded within the “UEA sRNA Workbench” (Bioinformatics 2018, 34, 3382-3384), software developed through a BBSRC grant that has been downloaded over 24,500 times as of January 2021.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
