Tools and best practices for data processing in allelic expression analysis

By Stephane E. Castel, Ami Levy-Moonshine, Pejman Mohammadi, Eric Banks, Tuuli Lappalainen. Genome Biology, 2015

359 citations recorded by the source corpus.

Allelic expression analysis has become important for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. We analyze the properties of allelic expression read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting such errors, show that our quality control measures improve the detection of relevant allelic expression, and introduce tools for the high-throughput production of allelic expression data from RNA-sequencing data.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7920a23376d86c1e1e73e82c770a534ff9bf84b2

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Citations in the corpus

Impacts of Neanderthal-introgressed sequences on the landscape of human gene expression (2017, neutral): “This step eliminates the contribution of erroneous heterozygous genotypes to false ASE signal, but has the side effect of filtering true cases of monoallelic expression (Castel et al., 2015).”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “There are already excellent reviews for major application or computational areas, such as de novo (or reference-based) transcriptome assembly [45], allele-specific expression analyses [46], expression quantitative trait loci mapping [47], splicing [48], analysis of gene regulatory”
Translating RNA sequencing into clinical diagnostics: opportunities and challenges (2016, background): “Recent reports provide recommendations for bioinformatic analysis and data processing to address these and other challenges and introduce tools for improved detection of ASE from RNA-seq data 42, 48 .”
Improving genetic diagnosis in Mendelian disease with transcriptome sequencing (2016, neutral): “e (38) d(39)m,(40) asja (41) dda (42) kja (43) jj (44) kl (45) jk (46)”
Landscape of X chromosome inactivation across human tissues (2016, neutral): “The analysis was restricted to biallelic SNPs due to known issues in mapping bias in RNA-seq against indels 37 .”
Genetic effects on gene expression across human tissues (2017, neutral): “1 ×10−3 for (75,100]% vs [0,25]%, (75,100 vs (25,50]% and (75,100]% vs (50,75]%, respectively).”