Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data

By J. Degner, John C. Marioni, A. Pai, Joseph K. Pickrell, Everlyne Nkadori, Yoav Gilad, J. K. Pritchard. Bioinform., 2009

527 citations recorded by the source corpus.

Motivation: Next-generation sequencing has become an important tool for genome-wide quantification of DNA and RNA. However, a major technical hurdle lies in the need to map short sequence reads back to their correct locations in a reference genome. Here, we investigate the impact of SNP variation on the reliability of read-mapping in the context of detecting allele-specific expression (ASE). Results: We generated 16 million 35 bp reads from mRNA of each of two HapMap Yoruba individuals. When we mapped these reads to the human genome we found that, at heterozygous SNPs, there was a significant bias toward higher mapping rates of the allele in the reference sequence, compared with the alternative allele. Masking known SNP positions in the genome sequence eliminated the reference bias but, surprisingly, did not lead to more reliable results overall. We find that even after masking, ∼5–10% of SNPs still have an inherent bias toward more effective mapping of one allele. Filtering out inherently biased SNPs removes 40% of the top signals of ASE. The remaining SNPs showing ASE are enriched in genes previously known to harbor cis-regulatory variation or known to show uniparental imprinting. Our results have implications for a variety of applications involving detection of alternate alleles from short-read sequence data. Availability: Scripts, written in Perl and R, for simulating short reads, masking SNP variation in a reference genome and analyzing the simulation output are available upon request from JFD. Raw short read data were deposited in GEO (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE18156. Contact: jdegner@uchicago.edu; marioni@uchicago.edu; gilad@uchicago.edu; pritch@uchicago.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/bee6cb619eb674041d9e42caf68708333d2506c4

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In Genetics

Citations in the corpus

Impacts of Neanderthal-introgressed sequences on the landscape of human gene expression (2017, unknown): “As reference allele bias has the potential to confound analysis of ASE (Degner et al., 2009), we undertook multiple steps to filter potentially biased sites in accordance with best practices (Castel et al., 2015), as e1 Cell 168, 916–927.e1–e5, February 23, 2017 well as confirming the robustness of…”
FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing (2016, background): “In sequencing data, it has been observed that there is a significant bias toward higher mapping rates for the reference allele compared to those for the variant allele at heterozygous loci (14).”
RNA Sequencing and Analysis (2015, unknown): “Conventional workflows to detect ASE involve counting reads containing each allele at heterozygous sites and applying a statistical test, such as the binomial test or the Fisher's exact test (Degner et al. 2009; Rozowsky et al. 2011; Wei and Wang 2013).”
Tools and best practices for data processing in allelic expression analysis (2015, background): “An even more difficult caveat in AE analysis is allelic mapping bias: in RNA-seq data aligned to the reference genome, a read carrying the alternative allele of a variant has at least one mismatch, and thus has a lower probability to align correctly than the reference reads [26 – 28].”
Computational methods for transcriptome annotation and quantification using RNA-seq (2011, background): “Similarly, an increase in sensitivity using seed meth-ods has been observed when aligning reads to polymorphic regions in a species for quantification of allele-specific gene expression 46 .”
Genome graphs and the evolution of genome inference (2017, background): “Reference allele bias is the tendency to underreport data whose underlying DNA does not match a reference allele (Degner et al. 2009; Brandtetal.2015).”
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, background): “expression (7,12–14), transcript assembly (15) or allelespecific expression (16,17), our focus in this article is on differential expression for pre-determined genomic features.”