CoChat
Sign Up
Cited by 502Genomics and Phylogenetic StudiesRNA Research and SplicingSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 18 of the 19 references it cites were found in a registry or an index.

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

Open access
Degner, Jacob F., Marioni, John C., Pai, Athma A., Pickrell, Joseph K., Nkadori, Everlyne, Gilad, Yoav, Pritchard, Jonathan K.·2009·Bioinformatics
DOI 10.1093/bioinformatics/btp579PMC PMC2788925
Open this paper in CoChat — free accountOpen PDF

Abstract

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/bioinformatics/btp579

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1. RNA-Seq data show a higher variance in the relative expression of each allele and a skew toward high expression of the reference allele compared with the predicted distribution. (A) Estimated probability densities for the proportion of reads matching the reference allele (i.e. the allele given in the reference human genome sequence) at heterozygous SNPs in exons. Solid lines correspond to the observed distributions for known heterozygous SNPs with more than 20 reads in two Yoruba HapMap individuals. The dashed line shows the predicted distribution without reference bias or ASE. (B) QQ-plots of P-values for one-sided tests that expression of the reference allele is either higher (circles) or lower (triangles) than the non-reference allele. The horizontal dashed line is the P-value threshold corresponding to a FDR of 1.0%. Notice the enrichment of very significant P-values for overexpression of reference alleles.
Fig. 1. RNA-Seq data show a higher variance in the relative expression of each allele and a skew toward high expression of the reference allele compared with the predicted distribution. (A) Estimated probability densities for the proportion of reads matching the reference allele (i.e. the allele given in the reference human genome sequence) at heterozygous SNPs in exons. Solid lines correspond to the observed distributions for known heterozygous SNPs with more than 20 reads in two Yoruba HapMap individuals. The dashed line shows the predicted distribution without reference bias or ASE. (B) QQ-plots of P-values for one-sided tests that expression of the reference allele is either higher (circles) or lower (triangles) than the non-reference allele. The horizontal dashed line is the P-value threshold corresponding to a FDR of 1.0%. Notice the enrichment of very significant P-values for overexpression of reference alleles.CC BY-NC · PubMed Central
Fig. 2. Magnitude of read-mapping biases in simulated data. (A) The distribution (across SNPs) of the proportion of correctly mapped reads that carry the reference allele, compared with the non-reference allele. The y-axis is broken into two segments to show more clearly the rates of highly biased SNPs. Three different rates of sequencing errors are shown. (B) Read-mapping was performed as in (A), except that the reads were aligned against a version of the genome sequence in which all SNP locations were masked. Notice that for both analysis methods, some SNPs are strongly biased, and that SNP masking does not clearly improve the results. Sequencing errors can substantially increase the extent of bias.
Fig. 2. Magnitude of read-mapping biases in simulated data. (A) The distribution (across SNPs) of the proportion of correctly mapped reads that carry the reference allele, compared with the non-reference allele. The y-axis is broken into two segments to show more clearly the rates of highly biased SNPs. Three different rates of sequencing errors are shown. (B) Read-mapping was performed as in (A), except that the reads were aligned against a version of the genome sequence in which all SNP locations were masked. Notice that for both analysis methods, some SNPs are strongly biased, and that SNP masking does not clearly improve the results. Sequencing errors can substantially increase the extent of bias.CC BY-NC · PubMed Central
Fig. 3. Two examples in which homology with other genomic locations leads to read-mapping biases. (A) Example of a SNP where there is a bias toward the reference allele before and after SNP masking (rs506008) and (B) example of a SNP where there is a bias toward the non-reference allele after SNP masking (rs11585481). Each example shows the variable sites in: (top row) the reference version of the genome sequence in the true location; (next six rows) three sample reads carrying the reference and three sample reads carrying the non-reference alleles at the SNP and (bottom row) the sequence in a region of homology elsewhere in the genome. The right-hand columns show how each read is mapped with, and without SNP masking. In these examples a read is mapped to a particular location if it has a unique best match at that location, and is unmapped if there is a tie between possible locations. The SNP masking generates an 1 nt mismatch between both alleles and the reference sequence at the masked site.
Fig. 3. Two examples in which homology with other genomic locations leads to read-mapping biases. (A) Example of a SNP where there is a bias toward the reference allele before and after SNP masking (rs506008) and (B) example of a SNP where there is a bias toward the non-reference allele after SNP masking (rs11585481). Each example shows the variable sites in: (top row) the reference version of the genome sequence in the true location; (next six rows) three sample reads carrying the reference and three sample reads carrying the non-reference alleles at the SNP and (bottom row) the sequence in a region of homology elsewhere in the genome. The right-hand columns show how each read is mapped with, and without SNP masking. In these examples a read is mapped to a particular location if it has a unique best match at that location, and is unmapped if there is a tie between possible locations. The SNP masking generates an 1 nt mismatch between both alleles and the reference sequence at the masked site.CC BY-NC · PubMed Central
Fig. 4. Bias for three short-read alignment programs and for three read lengths. (A) The plot shows the distribution of the fraction of mapped reads that carry the reference allele. Simulated reads with an error rate of 0.01 were mapped to the masked genome using MAQ (black), BOWTIE (dark blue) and BWA (light blue). Other details as in Figure 2B. (B) Mapped with MAQ as in (A) except that reads contained no additional errors and read lengths were as indicated.
Fig. 4. Bias for three short-read alignment programs and for three read lengths. (A) The plot shows the distribution of the fraction of mapped reads that carry the reference allele. Simulated reads with an error rate of 0.01 were mapped to the masked genome using MAQ (black), BOWTIE (dark blue) and BWA (light blue). Other details as in Figure 2B. (B) Mapped with MAQ as in (A) except that reads contained no additional errors and read lengths were as indicated.CC BY-NC · PubMed Central
Fig. 5. Summary of the ASE results after SNP masking, and after excluding inherently biased SNPs. (A) Distribution of ASE P-values after masking known SNP variation. Masking has largely eliminated bias toward the reference allele (circles: overrepresentation of reference allele; triangles, overrepresentation of non-reference allele), however, the number of significant results is not reduced. Display is as in Figure 1B. The horizontal dashed line represents the P-value threshold of 5.5 × 10−5 that allowed an FDR of 1% in the analysis presented in Figure 1. The FDR for this analysis using the initial P-value threshold was also 1%. (B) Distribution of P-values after excluding SNPs with an inherent bias toward one allele, as determined by simulations of perfect reads. This set of significant results is likely much more reflective of genes that show genuine ASE. The FDR for this analysis using the initial P-value threshold here was 1.4%. (C) Barplot showing the number of significant results for the three read-mapping strategies used in this article, corresponding to Figures 1B, 5A and 5B, using a P-value cutoff of 5.5 × 10−5, corresponding to FDRs of 1.0%, 1.0% and 1.4%, respectively.
Fig. 5. Summary of the ASE results after SNP masking, and after excluding inherently biased SNPs. (A) Distribution of ASE P-values after masking known SNP variation. Masking has largely eliminated bias toward the reference allele (circles: overrepresentation of reference allele; triangles, overrepresentation of non-reference allele), however, the number of significant results is not reduced. Display is as in Figure 1B. The horizontal dashed line represents the P-value threshold of 5.5 × 10−5 that allowed an FDR of 1% in the analysis presented in Figure 1. The FDR for this analysis using the initial P-value threshold was also 1%. (B) Distribution of P-values after excluding SNPs with an inherent bias toward one allele, as determined by simulations of perfect reads. This set of significant results is likely much more reflective of genes that show genuine ASE. The FDR for this analysis using the initial P-value threshold here was 1.4%. (C) Barplot showing the number of significant results for the three read-mapping strategies used in this article, corresponding to Figures 1B, 5A and 5B, using a P-value cutoff of 5.5 × 10−5, corresponding to FDRs of 1.0%, 1.0% and 1.4%, respectively.CC BY-NC · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 19 references

Cited by

Sorted by citations

Related

See all

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.
ORCID
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
DOI
10.1093/bioinformatics/btp579
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review