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Nat Commun ; 12(1): 529, 2021 01 22.
Artigo em Inglês | MEDLINE | ID: mdl-33483494


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

Algoritmos , Processamento Alternativo , Biologia Computacional/métodos , RNA-Seq/métodos , Análise de Sequência de RNA/métodos , Internet , Íntrons/genética , Software
Nat Protoc ; 16(2): 1276-1296, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33462443


RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects in individuals affected by genetically undiagnosed rare disorders. Pioneering studies have shown that RNA-seq could increase the diagnosis rates over DNA sequencing alone by 8-36%, depending on the disease entity and tissue probed. To accelerate adoption of RNA-seq by human genetics centers, detailed analysis protocols are now needed. We present a step-by-step protocol that details how to robustly detect aberrant expression levels, aberrant splicing and mono-allelic expression in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on the detection of RNA outliers pipeline (DROP), a modular computational workflow that integrates all the analysis steps, can leverage parallel computing infrastructures and generates browsable web page reports.

Sequência de Bases/genética , Expressão Gênica/genética , Análise de Sequência de RNA/métodos , Diagnóstico , Técnicas e Procedimentos Diagnósticos , Doença/genética , Perfilação da Expressão Gênica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , RNA/genética , Software , Fluxo de Trabalho