Improved detection of aberrant splicing using the Intron Jaccard Index.
| Title: | Improved detection of aberrant splicing using the Intron Jaccard Index. |
|---|---|
| Authors: | Scheller IF; School of Computation, Information and Technology, Technical University of Munich, Garching, 85748, Germany.; Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, 85764, Germany.; Lutz K; School of Computation, Information and Technology, Technical University of Munich, Garching, 85748, Germany.; Mertes C; School of Computation, Information and Technology, Technical University of Munich, Garching, 85748, Germany.; Munich Data Science Institute, Technical University of Munich, Garching, 85748, Germany.; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, 81675, Germany.; Yépez VA; School of Computation, Information and Technology, Technical University of Munich, Garching, 85748, Germany.; Gagneur J; School of Computation, Information and Technology, Technical University of Munich, Garching, 85748, Germany.; Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, 85764, Germany.; Munich Data Science Institute, Technical University of Munich, Garching, 85748, Germany.; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, 81675, Germany. |
| Source: | MedRxiv : the preprint server for health sciences [medRxiv] 2023 Apr 03. Date of Electronic Publication: 2023 Apr 03. |
| Publication Type: | Preprint; Journal Article |
| Language: | English |
| Journal Info: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| Abstract: | Detection of aberrantly spliced genes is an important step in RNA-seq-based rare disease diagnostics. We recently developed FRASER, a denoising autoencoder-based method for aberrant splicing detection that outperformed alternative approaches. However, as FRASER's three splice metrics are partially redundant and tend to be sensitive to sequencing depth, we introduce here a more robust intron excision metric, the Intron Jaccard Index, that combines alternative donor, alternative acceptor, and intron retention signal into a single value. Moreover, we optimized model parameters and filter cutoffs using candidate rare splice-disrupting variants as independent evidence. On 16,213 GTEx samples, our improved algorithm called typically 10 times fewer splicing outliers while increasing the proportion of candidate rare splice-disrupting variants by 10 fold and substantially decreasing the effect of sequencing depth on the number of reported outliers. Application on 303 rare disease samples confirmed the reduction fold-change of the number of outlier calls for a slight loss of sensitivity (only 2 out of 22 previously identified pathogenic splicing cases not recovered). Altogether, these methodological improvements contribute to more effective RNA-seq-based rare diagnostics by a drastic reduction of the amount of splicing outlier calls per sample at minimal loss of sensitivity. |
| Comments: | Update in: Am J Hum Genet. 2023 Dec 7;110(12):2056-2067. doi: 10.1016/j.ajhg.2023.10.014.. (PMID: 38006880) |
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| Grant Information: | U01 HG007690 United States HG NHGRI NIH HHS; U54 NS108251 United States NS NINDS NIH HHS; U01 HG010219 United States HG NHGRI NIH HHS; U01 HG007674 United States HG NHGRI NIH HHS; U01 HG007672 United States HG NHGRI NIH HHS; U01 HG010233 United States HG NHGRI NIH HHS; U01 HG010230 United States HG NHGRI NIH HHS; U01 HG007943 United States HG NHGRI NIH HHS; U01 HG010217 United States HG NHGRI NIH HHS; U01 HG007942 United States HG NHGRI NIH HHS; U01 HG010215 United States HG NHGRI NIH HHS; U01 HG007708 United States HG NHGRI NIH HHS; U01 TR001395 United States TR NCATS NIH HHS; U01 HG007709 United States HG NHGRI NIH HHS; U54 NS093793 United States NS NINDS NIH HHS; U01 HG007530 United States HG NHGRI NIH HHS; U01 TR002471 United States TR NCATS NIH HHS; U01 HG007703 United States HG NHGRI NIH HHS |
| Entry Date(s): | Date Created: 20230417 Latest Revision: 20240917 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC10104204 |
| DOI: | 10.1101/2023.03.31.23287997 |
| PMID: | 37066374 |
| Database: | MEDLINE |
Preprint; Journal Article