Journal
BIOINFORMATICS
Volume 33, Issue 16, Pages 2446-2454Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx210
Keywords
-
Categories
Funding
- Biotechnology and Biological Sciences Research Council (BBSRC) [BB/L021269/1]
- BBSRC [BB/L021269/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/L021269/1] Funding Source: researchfish
Ask authors/readers for more resources
Motivation: MicroRNAs are a class of similar to 21-22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure. Results: We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available