Combining computational approaches and functional analysis for characterizing the deleterious effect of <scp><i>RHD</i></scp> gene variants on splicing in the Chinese population
Our findings suggest that combining computational prediction with SpliceAI and/or SPiP using specific settings is an efficient approach (accuracy = 0.941) for selecting candidate SNVs that are prone to disrupt RHD splicing and alter D phenotype subsequently.
Authors
- Yann Fichou (ORCID: https://orcid.org/0000-0002-5104-9125)
- Caroline Bénech
- Shuangshuang Jia (ORCID: https://orcid.org/0000-0002-0092-8576)
- Mingming Sun (ORCID: https://orcid.org/0000-0002-3117-7066)
- Yanli Ji (ORCID: https://orcid.org/0000-0001-6792-5612)
- Gaëlle Richard (ORCID: https://orcid.org/0000-0002-9080-4074)
Institutions
- Inserm (FR)
- Université de Bretagne Occidentale (FR)
- École nationale d'ingénieurs de Brest (FR)
- Guangzhou Blood Center (CN)
- Kingmed Diagnostics (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Transfusion
- Published
- 2025-06-20
- DOI
- https://doi.org/10.1111/trf.18316
- Citations
- 2
- Primary Topic
- RNA Research and Splicing
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Natural Science Foundation of China