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Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data.
Open accessAbstract
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.
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“Reads perfectly matching the reference genome were assigned to the reference allele, while reads with at least one difference from the reference genome were assigned to an alternative allele.”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as Degner et al. [14]
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“The rationale for N-masking in allele-specific alignments is that the mapping bias towards the reference allele is eliminated and both alleles of the same read get placed in the same position in the genome equally well.”
From SNPsplit: Allele-specific splitting of alignments between genomes with known SNP genotypes (2016) · cites this paper as [3]
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“Simulated datasets contained either ~10-fold or ~100-fold more differentiating sites than the human genotypes used to validate other methods for measuring relative ASE [14,18,20].”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as [14]
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In the citing paper
“Controlling for sites known to be polymorphic in humans prior to aligning the simulated reads produced symmetrical measures of relative ASE, showing that the differentiating sites themselves caused this bias.”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as Degner et al. [14]
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In the citing paper
“Genetic variants that distinguish individual genomes from the reference sequence can cause reads to be misaligned, resulting in biased estimates of transcript abundance.”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Degner et al. 2009
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“The alignment parameters they used allowed two or fewer bases within each read to differ from the reference genome.”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as Degner et al. [14]
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“a systematic bias occurs when reads from a heterozygous individual are aligned to a single reference genome [14].”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as [14]
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“Despite simulating an equal number of reads from each allele, a bias was observed causing reads to be assigned more often to the reference allele than the alternative allele.”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as Degner et al. [14]
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“Differences between alleles in sequences that appear more than once in the genome can also cause reads to be excluded for one allele but not the other [14].”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as [14]
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“relative ASE is systematically biased when sequence reads are aligned to a single reference genome”
From Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome (2013) · cites this paper as Degner et al. [14]
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Citations
Cites
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Understanding mechanisms underlying human gene expression variation with RNA sequencing
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Metadata
- Authors
- Degner, J. F., Marioni, J. C., Pai, A. A., Pickrell, J. K., Nkadori, E., Gilad, Y., & Pritchard, J. K.
- Affiliations
- Howard Hughes Medical Institute (US)
- Corresponding author
- Degner, Jacob F.
- Marioni, John C.
- Gilad, Yoav
- Pritchard, Jonathan K.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-10-06
- Volume
- 25
- Issue
- 24
- Pages
- 3207-3212
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2788925
- OpenAlex
- W2113312463
- Cited by
- 502
- References
- 19
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 21 · 2023: 17 · 2022: 18 · 2021: 47 · 2020: 41 · 2019: 37 · 2018: 31 · 2017: 26 · 2016: 41 · 2015: 46 · 2014: 48 · 2013: 36 · 2012: 28
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Yale University
- National Institutes of Health
- National Institute of Mental Health (R01MH084703)
- National Institute of General Medical Sciences (GM077959, R01GM077959, T 532 GM007197-34, T32GM007197)
- Argonne National Laboratory
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- allele-specific expression, single nucleotide polymorphisms, heterozygous SNPs, RNA sequencing, cis-regulatory variation
- MeSH terms
- Alleles, Base Sequence, Humans, Software, Genome, Human, Sequence Analysis, RNA, Computational Biology, Polymorphism, Single Nucleotide, Gene Expression Profiling
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