Characterization of statistical features for plant microRNA prediction
Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin le...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2011
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/165931 |
Similar Items: Characterization of statistical features for plant microRNA prediction
- Genome-wide identification of microRNA targets in the neglected disease pathogens of the genus Echinococcus
- Using small RNA-seq data to detect siRNA duplexes induced by plant viruses
- Impact of down-regulation of starch branching enzyme IIb in rice by artificial microRNA- and hairpin RNA-mediated RNA silencing
- Plant microRNAs: Recent Advances and Future Challenges
- Deep sequencing of virus derived small interfering RNAs and RNA from viral particles shows highly similar mutational landscape of a plant virus population
- Artificial microRNA-derived resistance to Cassava brown streak disease