ArticlePloS one2017
IpiRId: Integrative approach for piRNA prediction using genomic and epigenomic data.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- Comparison and benchmark of deep learning methods for non-coding RNA classification.PLoS computational biology · 2024Article
- LSTM4piRNA: Efficient piRNA Detection in Large-Scale Genome Databases Using a Deep Learning-Based LSTM Network.International journal of molecular sciences · 2023Article
- Non-coding RNAs in immunoregulation and autoimmunity: Technological advances and critical limitations.Journal of autoimmunity · 2023Review
- Computational approaches and challenges for identification and annotation of non-coding RNAs using RNA-Seq.Functional & integrative genomics · 2022Review
- piRNAs and PIWI Proteins as Diagnostic and Prognostic Markers of Genitourinary Cancers.Biomolecules · 2022Review
- A Review of Discovery Profiling of PIWI-Interacting RNAs and Their Diverse Functions in Metazoans.International journal of molecular sciences · 2021Review
- Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements.Biology · 2021Review
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
- The biogenesis and biological function of PIWI-interacting RNA in cancer.Journal of hematology & oncology · 2021Review
- Computational Methods and Online Resources for Identification of piRNA-Related Molecules.Interdisciplinary sciences, computational life sciences · 2021Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many computational tools have been proposed during the two last decades for predicting piRNAs, which are molecules with important role in post-transcriptional gene regulation. However, these tools are mostly based on only one feature that is generally related to the sequence. Discoveries in the domain of piRNAs are still in their beginning stages, and recent publications have shown many new properties. Here, we propose an integrative approach for piRNA prediction in which several types of genomic and epigenomic properties that can be used to characterize these molecules are examined. We reviewed and extracted a large number of piRNA features from the literature that have been observed experimentally in several species. These features are represented by different kernels, in a Multiple Kernel Learning based approach, implemented within an object-oriented framework. The obtained tool, called IpiRId, shows prediction results that attain more than 90% of accuracy on different tested species (human, mouse and fly), outperforming all existing tools. Besides, our method makes it possible to study the validity of each given feature in a given species. Finally, the developed tool is modular and easily extensible, and can be adapted for predicting other types of ncRNAs. The IpiRId software and the user-friendly web-based server of our tool are now freely available to academic users at: https://evryrna.ibisc.univ-evry.fr/evryrna/.
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