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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Zhang, Junpeng Liu, Lin Wei, Xuemei Zhao, Chunwen Luo, Yanbi Li, Jiuyong Le, Thuc Duy |
| Abstract | Background RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level. Results Here, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets. Conclusions Scan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples. |
| Related Links | https://bmcbiol.biomedcentral.com/counter/pdf/10.1186/s12915-024-02020-x.pdf |
| Ending Page | 19 |
| Page Count | 19 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17417007 |
| DOI | 10.1186/s12915-024-02020-x |
| Journal | BMC Biology |
| Issue Number | 1 |
| Volume Number | 22 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-09-27 |
| Access Restriction | Open |
| Subject Keyword | Life Sciences miRNA mRNA Single-cell RNA-sequencing Bulk RNA-sequencing Sample-specific miRNA regulation Sample correlation network Human cancer |
| Content Type | Text |
| Resource Type | Article |
| Subject | Ecology, Evolution, Behavior and Systematics Structural Biology Biochemistry, Genetics and Molecular Biology Plant Science Biotechnology Physiology Agricultural and Biological Sciences Cell Biology Developmental Biology |
| Journal Impact Factor | 4.4/2023 |
| 5-Year Journal Impact Factor | 5.4/2023 |
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