Name
Abstract | ||
|---|---|---|
Identifying piwi-interacting RNAs (piRNAs) of non-model organisms is a difficult and unsolved problem because piRNAs lack conservative secondary structure motifs and sequence homology in different species.In this article, a k-mer scheme is proposed to identify piRNA sequences, relying on the training sets from non-piRNA and piRNA sequences of five model species sequenced: rat, mouse, human, fruit fly and nematode. Compared with the existing 'static' scheme based on the position-specific base usage, our novel 'dynamic' algorithm performs much better with a precision of over 90% and a sensitivity of over 60%, and the precision is verified by 5-fold cross-validation in these species. To test its validity, we use the algorithm to identify piRNAs of the migratory locust based on 603 607 deep-sequenced small RNA sequences. Totally, 87 536 piRNAs of the locust are predicted, and 4426 of them matched with existing locust transposons. The transcriptional difference between solitary and gregarious locusts was described. We also revisit the position-specific base usage of piRNAs and find the conservation in the end of piRNAs. Therefore, the method we developed can be used to identify piRNAs of non-model organisms without complete genome sequences.The web server for implementing the algorithm and the software code are freely available to the academic community at http://59.79.168.90/piRNA/index.php. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1093/bioinformatics/btr016 | Bioinformatics |
Keywords | Field | DocType |
pirna sequence,model species,non-model organism,migratory locust,5-fold cross-validation,locust pirnas,k-mer scheme,position-specific base usage,gregarious locust,locust transposons,different species | Genome,Locust,Small RNA,Biology,Piwi-interacting RNA,Transposable element,Bioinformatics,Academic community,k-mer,Migratory locust | Journal |
Volume | Issue | ISSN |
27 | 6 | 1367-4811 |
Citations | PageRank | References |
11 | 0.75 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Zhang | 1 | 247 | 34.88 |
| Xianhui Wang | 2 | 11 | 0.75 |
| Le Kang | 3 | 12 | 4.49 |