Improved detection of aberrant splicing using the Intron Jaccard Index

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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the German Bundesministerium für Bildung und Forschung (BMBF) through the ERA PerMed project PerMiM [01KU2016B to IS, VAY and JG]; by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - via the projects "Identification of host genetic variation predisposing to severe COVID-19 by genetics, transcriptomics and functional analyses" [466168909 to VAY and JG], "Identification and Characterization of Long COVID-19 patients using whole-blood transcriptomics" [466168626 to KL and JG], and Nationale Forschungsdateninfrastruktur (NFDI) 1/1 "GHGA - German Human Genome-Phenome Archive" [441914366 to CM and JG]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Does not apply, as only existing public datasets were used. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes No new data was generated for this study. The GTEx data used in this manuscript was obtained from dbGaP accession number phs000424.v8.p1. The intron counts of the Yépez et al. dataset were downloaded from Zenodo. The UDN dataset was obtained from dbGaP accession number phs001232.v4.p2.