A k-mer scheme to predict piRNAs and characterize locust piRNAs
Open accessAbstract
Motivation: 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. Results: 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. Availability: 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. Contact: lkang@ioz.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.
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piRNAs lead PIWI proteins to degrade specific targets recognized by complementary RNA sequence, or to methylate DNA, modify histones, or contribute to chromatin opening.
“piRNAs lead PIWI proteins to degrade any specific target, recognized by the complementary RNA sequence, or even to methylate DNA, histone modification, or contribute to chromatin opening.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [70]
The Introduction (page unavailable) says piRNAs form complexes with PIWI proteins, supporting their association. It does not say that piRNAs guide PIWI proteins to complementary targets for degradation, or that they cause DNA methylation, histone modification, or chromatin opening; searches for those mechanisms found no supporting passage. (LLM, conf=0.83)
Experimental — accuracy not yet measuredpiRNAs are 21-35 nucleotides long, longer than miRNAs, and can repress complementary transposons.
“piRNAs are somehow longer than miRNAs, being 21–35 nucleotides long, and can repress complementary transposons [70].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [70]
The Introduction (page unavailable) says piRNAs are slightly longer than miRNAs, but gives their general length as 25–32 nt, not 21–35 nt. Results §3.6 (page unavailable) reports matches between predicted locust piRNAs and transposons; it does not show that those piRNAs repress complementary transposons. (LLM, conf=0.88)
Experimental — accuracy not yet measuredpiRNAs are more expressed in germ cells than in somatic cells.
“piRNAS are more expressed in germ cells than in somatic cells.”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [70]
Aberrant expression of piRNAs and PIWI proteins is related to a wide diverse type of human cancers.
“Various reports show that the aberrant expression from piRNAS and PIWI proteins relates to a wide diverse type of human cancers; therefore, they have been proposed as markers in cancer clinical diagnosis [70].”
From Revisiting Epigenetics Fundamentals and Its Biomedical Implications (2024) · cites this paper as [70]
The Introduction describes piRNA interactions with PIWI proteins, and the Methods and Results concern computational piRNA prediction. No passage in our copy links aberrant piRNA or PIWI expression to human cancers or proposes them as clinical diagnostic markers (searched: cancer, tumour, tumor, malignancy, biomarkers, diagnosis). (LLM, conf=0.75)
Experimental — accuracy not yet measured
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Revisiting Epigenetics Fundamentals and Its Biomedical Implications
Meza-Menchaca, T., Albores-Medina, A., Heredia-Mendez, A. J., Ruíz-May, E., Ricaño-Rodríguez, J., Gallegos-García, V., Esquivel, A., Vettoretti-Maldonado, G., & Campos-Parra, A. D. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25147927 · Cited by 15 · see citing works
- Authors
- Zhang, Y., Wang, X., & Kang, L.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Kang, Le
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-01-11
- Volume
- 27
- Issue
- 6
- Pages
- 771-776
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3051322
- OpenAlex
- W2160828096
- Cited by
- 151
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 8 · 2023: 6 · 2022: 34 · 2021: 20 · 2020: 10 · 2019: 13 · 2018: 6 · 2017: 11 · 2016: 16 · 2015: 7 · 2014: 10 · 2013: 5 · 2012: 2
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Hebei University of Science and Technology
- National Natural Science Foundation of China (30830022, 10926054)
- China Postdoctoral Science Foundation (20090460519)
- Hebei University
- National Key Research and Development Program of China (2006CB102000-1)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Chromosomal and Genetic Variations
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- piRNA, locusts, small RNA, transposable elements, non-model organisms
- MeSH terms
- Algorithms, Animals, Grasshoppers, Humans, Software, Sequence Analysis, RNA, Retroelements, Computational Biology, RNA, Small Interfering
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