ArticleAmerican journal of human genetics2018
OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing Data.
Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers.
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140 citing papers in PubMed, 223 citations in OpenAlex.
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- Poly(A)+ selection limits detection of long and alternatively spliced transcripts compared with rRNA depletion in RNA-Sequencing.BMC genomics · 2026Article
- AXOLOTL: an accurate method for detecting aberrant gene expression in rare diseases using coexpression constraints.Bioinformatics (Oxford, England) · 2026Article
- RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease.Annals of clinical and translational neurology · 2026Article
- Variants in the proteasome regulator PSMF1 cause a phenotypic spectrum from parkinsonism to perinatal lethality.Nature communications · 2026Article
- Biallelic variants in RNU2-2 cause a remarkably frequent developmental and epileptic encephalopathy.Nature genetics · 2026Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- MCVAE-based multi-omic anomaly detection in Fragile X Syndrome.NAR molecular medicine · 2026Article
- A Homozygous Deep Intronic SNX14 Variant Activates Pseudo-Exon Inclusion in a Patient with SCAR20.Genes · 2026Article
- The Signal in the Extreme: A Systematic Outlier Framework Identifies Discrete Immunometabolic Subtypes in Human and Cellular Models.Medical sciences (Basel, Switzerland) · 2026Article
80 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (Outlier in RNA-Seq Finder), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read-count expectations according to the gene covariation resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best recall of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of controlling for covariation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a dataset, for identifying outlier samples with too many aberrantly expressed genes, and for detecting aberrant gene expression on the basis of false-discovery-rate-adjusted p values. Overall, OUTRIDER provides an end-to-end solution for identifying aberrantly expressed genes and is suitable for use by rare-disease diagnostic platforms.
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