Detection of aberrant splicing events in RNA-seq data with FRASER

Abstract

12Aberrant splicing is a major cause of rare diseases, yet its prediction from genome 13 sequence remains in most cases inconclusive. Recently, RNA sequencing has proven to 14 be an effective complementary avenue to detect aberrant splicing. Here, we developed 15 FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike 16 existing methods, FRASER captures not only alternative splicing but also intron retention 17 events. This typically doubles the number of detected aberrant events and identified a 18 pathogenic intron retention in MCOLN1. FRASER automatically controls for latent 19 confounders, which are widespread and substantially affect sensitivity. Moreover, 20 FRASER is based on a count distribution and multiple testing correction, reducing the 21 number of calls by two orders of magnitude over commonly applied z score cutoffs, with 1 a minor sensitivity loss. The application to rare disease diagnostics is demonstrated by 2 reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. 3 FRASER is easy to use and freely available. 4

Transcriptome-directed analysis for Mendelian disease diagnosis overcomes limitations of conventional genomic testing

“…This workflow analyzes RNA-seq datasets to identify genes with aberrant expression levels using OUTRIDER (44), and aberrant splicing using FRASER (45), amongst a large group of samples, while automatically controlling for latent confounders. To minimize tissue-specific differences, we processed the data from fibroblasts and whole blood separately, yielding >100 cases from each tissue, which was well above the recommended minimum of 50-60 samples to maximize outlier sensitivity (43).…”

“…The output of the DROP expression module is a list of outlier genes in each sample along with statistical information such as multiple-testing adjusted p-values, Z-scores, and foldchanges for each deviation compared to the cohort. We identified outlier genes with large under or overexpression in each sample at a false discovery rate (FDR) of 0.05 per OUTRIDER recommendations (44). The splicing module similarly provides relevant statistics and frequency of each abnormal splicing event within the dataset.…”

Integration of proteomics with genomics and transcriptomics increases the diagnostic rate of Mendelian disorders

“…To identify genes with aberrant RNA expression, we performed three outlier analyses, i) aberrant expression levels, ii) aberrant splicing, and iii) monoallelic expression of rare variants via the DROP pipeline (Yépez et al, 2021). To identify aberrant protein expression, we developed the algorithm PROTRIDER which estimates deviations from expected protein is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint…”

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“…From genes associated with autism/ID and epilepsy to (DROP) using the default, recommended settings (43). This workflow analyzes RNA-seq data sets to identify genes with aberrant expression levels using OUTRIDER (44), and aberrant splicing using FRASER (45), among a large group of samples, while automatically controlling for latent confounders. To minimize tissue-specific differences, we processed the data from skin fibroblasts and whole blood separately, yielding more than 100 cases from each tissue, which was well above the recommended minimum of 50-60 samples to maximize outlier sensitivity (43).…”

“…The splicing module similarly provides relevant statistics and frequency of each abnormal splicing event within the data set. Theorizing that rare splicing events are more likely to be pathogenic, we focused on potentially novel splicing events that were seen no more than 2 times in their respective tissue using established FRASER statistical cutoffs (45).…”