Detection of aberrant gene expression events in RNA sequencing data

Yépez, Vicente A.; Mertes, Christian; Müller, Michaela; Klaproth-Andrade, Daniela; Wachutka, Leonhard; Frésard, Laure; Gusic, Mirjana; Scheller, Ines F.; Goldberg, Patricia F.; Gagneur, Julien

doi:10.1038/s41596-020-00462-5

Cited by 89 publications

(96 citation statements)

References 63 publications

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“…Transcriptome sequencing (RNA-seq) is deemed to enhance identifying Mendelian variants by ~10-30% (Kremer et al, 2017;Gonorazky et al, 2019). Allelic imbalances in transcriptomics data can identify imprinting, uni-parental disomy, and X chromosome inactivation (Gonorazky et al, 2019;Yépez et al, 2021). However, the tissue transcriptome does not necessarily reflect the proteome, metabolome, and phenome (Wang et al, 2019).…”

Section: Remaining Challengesmentioning

confidence: 99%

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

Stevanin

2021

Front. Genet.

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Hereditary spinocerebellar degeneration (SCD) encompasses an expanding list of rare diseases with a broad clinical and genetic heterogeneity, complicating their diagnosis and management in daily clinical practice. Correct diagnosis is a pillar for precision medicine, a branch of medicine that promises to flourish with the progressive improvements in studying the human genome. Discovering the genes causing novel Mendelian phenotypes contributes to precision medicine by diagnosing subsets of patients with previously undiagnosed conditions, guiding the management of these patients and their families, and enabling the discovery of more causes of Mendelian diseases. This new knowledge provides insight into the biological processes involved in health and disease, including the more common complex disorders. This review discusses the evolution of the clinical and genetic approaches used to diagnose hereditary SCD and the potential of new tools for future discoveries.

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Section: Remaining Challengesmentioning

confidence: 99%

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

Stevanin

2021

Front. Genet.

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“…Expression outliers are defined as the gene-sample combinations with an FDR ≤0.05, resulting in a handful of outliers per sample, depending on the number of samples. In a subset of GEUVADI's cohort (Lappalainen et al, 2013) comprising 100 control samples from different sequencing centers (CNAG CRG, N 31; ICMB, N 28; and UNIGE, N 41) and ancestries (British, N 12; Finnish, N 26; Tuscan, N 22; Utah, N 16; and Yoruba, N 24), OUTRIDER's denoising autoencoder was able to control for covariation (Yépez et al, 2021b). The sample size of each subgroup was relatively similar; therefore, the autoencoder is yet to be tested in cases where a subgroup is substantially underrepresented.…”

Section: Aberrant Expressionmentioning

confidence: 99%

“…Application of FRASER in the rare disease cohort from Kremer et al (2017) identified all three previously detected pathogenic splicing aberrations, plus an intron-retention event missed by LeafCutter, and a synonymous variant causing a splice defect missed by Kremer et al (Mertes et al, 2021). FRASER was also applied to the GEUVADIS multicenter and multi-ancestry cohort and was able to remove sample covariation for all metrics (Yépez et al, 2021b). In addition, FRASER has been used by Murdock et al (2021) leading to the diagnosis of four out of 78 subjects and by Yépez et al (2021a) leading to the diagnosis of 19 (12 in combination with aberrant expression) subjects from various ancestries.…”

Section: Splicing Outliersmentioning

confidence: 99%

“…Obtaining MAE in genes constrained to variation, shorter genes, or ethnicities similar to the reference genome is, therefore, limited. So far, the added value of MAE has been lower with respect to that of aberrant expression or splicing and often in combination with aberrant expression (Kremer et al, 2017;Gonorazky et al, 2019;Yépez et al, 2021b).…”

Section: Mono-allelic Expressionmentioning

confidence: 99%

“…The diagnostic potential of complementing DNA sequencing with RNAseq for solving previously inconclusive WES cases is unequivocal. RNAseq-based diagnostics has led to a diagnostic yield of 7.5-18% in rare disease cohorts with no prior patient restrictions (Kremer et al, 2017;Frésard et al, 2019;Murdock et al, 2021;Yépez et al, 2021b) and of 35% in a cohort of patients including cases with predicted splice defects (Cummings et al, 2017;Gonorazky et al, 2019). In these studies, RNAseq led to the validation of novel pathogenic variants in known disease genes [e.g., chr21:47,409,881 C>T in the COL6A1 gene in Cummings et al (2017)], but also to the discovery and validation of a new disease gene, TIMMDC1, where a deep intronic variant caused the activation of a cryptic splice site in two unrelated families (Kremer et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

Schlieben

Prokisch

Yépez

2021

Front. Mol. Biosci.

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Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10–35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.

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Genetic basis of mitochondrial diseases

Gusic

2021

FEBS Letters

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Mitochondrial disorders are monogenic disorders characterized by a defect in oxidative phosphorylation and caused by pathogenic variants in one of over 340 different genes. The implementation of whole exome sequencing has led to a revolution in their diagnosis, duplicated the number of associated disease genes, and significantly increased the diagnosed fraction. However, the genetic etiology of a substantial fraction of patients exhibiting mitochondrial disorders remains unknown, highlighting limitations in variant detection and interpretation, which calls for improved computational and DNA sequencing methods, as well as the addition of OMICS tools. More intriguingly, this also suggests that some pathogenic variants lie outside of the proteincoding genes and that the mechanisms beyond the Mendelian inheritance and the mtDNA are of relevance. This review covers the current status of the genetic basis of mitochondrial diseases, discusses current challenges and perspectives, and explores the contribution of factors beyond the protein-coding regions and monogenic inheritance in the expansion of the genetic spectrum of disease.

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Detection of aberrant gene expression events in RNA sequencing data

Cited by 89 publications

References 63 publications

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

The History of Gene Hunting in Hereditary Spinocerebellar Degeneration: Lessons From the Past and Future Perspectives

How Machine Learning and Statistical Models Advance Molecular Diagnostics of Rare Disorders Via Analysis of RNA Sequencing Data

Genetic basis of mitochondrial diseases

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