MirLibSpark: A Scalable NGS Plant MicroRNA Prediction Pipeline for Multi-Library Functional Annotation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915129221709824 |
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| author | Wu, Chao-Jung Remita, Amine M. Diallo, Abdoulaye Baniré |
| author_facet | Wu, Chao-Jung Remita, Amine M. Diallo, Abdoulaye Baniré |
| contents | The emergence of the Next Generation Sequencing increases drastically the volume of transcriptomic data. Although many standalone algorithms and workflows for novel microRNA (miRNA) prediction have been proposed, few are designed for processing large volume of sequence data from large genomes, and even fewer further annotate functional miRNAs by analyzing multiple libraries. We propose an improved pipeline for a high volume data facility by implementing mirLibSpark based on the Apache Spark framework. This pipeline is the fastest actual method, and provides an accuracy improvement compared to the standard. In this paper, we deliver the first distributed functional miRNA predictor as a standalone and fully automated package. It is an efficient and accurate miRNA predictor with functional insight. Furthermore, it compiles with the gold-standard requirement on plant miRNA predictions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_17998 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MirLibSpark: A Scalable NGS Plant MicroRNA Prediction Pipeline for Multi-Library Functional Annotation Wu, Chao-Jung Remita, Amine M. Diallo, Abdoulaye Baniré Distributed, Parallel, and Cluster Computing Genomics J.3; I.5.3; D.2.11 The emergence of the Next Generation Sequencing increases drastically the volume of transcriptomic data. Although many standalone algorithms and workflows for novel microRNA (miRNA) prediction have been proposed, few are designed for processing large volume of sequence data from large genomes, and even fewer further annotate functional miRNAs by analyzing multiple libraries. We propose an improved pipeline for a high volume data facility by implementing mirLibSpark based on the Apache Spark framework. This pipeline is the fastest actual method, and provides an accuracy improvement compared to the standard. In this paper, we deliver the first distributed functional miRNA predictor as a standalone and fully automated package. It is an efficient and accurate miRNA predictor with functional insight. Furthermore, it compiles with the gold-standard requirement on plant miRNA predictions. |
| title | MirLibSpark: A Scalable NGS Plant MicroRNA Prediction Pipeline for Multi-Library Functional Annotation |
| topic | Distributed, Parallel, and Cluster Computing Genomics J.3; I.5.3; D.2.11 |
| url | https://arxiv.org/abs/2501.17998 |