期刊
BIOINFORMATICS
卷 30, 期 19, 页码 2837-2839出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btu380
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资金
- NSF CAREER Grant [DBI-0953738]
- NSF [IOS-1126998]
- Direct For Biological Sciences
- Division Of Integrative Organismal Systems [1126998] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Div Of Biological Infrastructure [0953738] Funding Source: National Science Foundation
A Summary: Plant microRNA prediction tools that use small RNA-sequencing data are emerging quickly. These existing tools have at least one of the following problems: (i) high false-positive rate; (ii) long running time; (iii) work only for genomes in their databases; (iv) hard to install or use. We developed miR-PREFeR (miRNA PREdiction From small RNA-Seq data), which uses expression patterns of miRNA and follows the criteria for plant microRNA annotation to accurately predict plant miRNAs from one or more small RNA-Seq data samples of the same species. We tested miR-PREFeR on several plant species. The results show that miR-PREFeR is sensitive, accurate, fast and has low-memory footprint.
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