Improved detection of aberrant splicing using the Intron Jaccard Index

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.

Analysis of 3,760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

“…Moreover, we annotated genetic variants falling into gene bodies, including deep intronic variants, with AbSplice-DNA, a tool predicting variants causing aberrant splicing 34 . On the RNA-seq data, we used OUTRIDER on protein-coding genes commonly expressed across the dataset to call aberrantly high or low expression and FRASER to call rare splicing events 39,40 . We also introduced a new method, NB-act, to call aberrant activation of genes usually not expressed (Methods).…”

“…intOGen captures recurrent mutational patterns 37 , and AbSplice predicts variants causing aberrant splicing 35 . Working on RNA-seq data, OUTRIDER calls aberrant expression levels of commonly-expressed genes 39 , NB-act calls overexpression of rarely expressed genes (Methods), and FRASER calls aberrant splicing events 40 . Resource: a unique collection of transcriptomic and genomic aberrations for 24 hematologic malignancy entities.…”

“…OUTRIDER (v1.17.2) 38 and FRASER (v.1.99.0) 39 were applied to expression from RNA-seq using DROP (v1.2.3) 40 , calling rare abnormal expression and splicing events. We grouped the samples into 14 study groups based on hematopoietic cell origins and pathologies [2][3][4][5] to ensure sufficient sample sizes (Table 1, N at least 58).…”

RNA-sequencing first approach generates new diagnostic candidates in Mendelian disorders

“…Once aberrant splicing events had been ascertained in cases that had a VUS or known molecular diagnosis, we used this data to identify open-source tools best placed to identify potential aberrant splicing in cases for which there was no candidate variant. FRASER2(21,22), rMATS-turbo v4.1.2(23), MAJIQ v2.4(24) and LeafCutterMD v0.2.9(25) were used to detect aberrant splicing across all samples. The tools chosen are some of the most commonly used for splicing analyses, where rMATS-turbo and MAJIQ are events-based methods while LeafCutterMD and FRASER2 are outlier approaches.…”