Cited by 141Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsRNA Research and Splicing

Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome

Open access
Stevenson, Kraig R, Coolon, Joseph D, Wittkopp, Patricia J·2013·BMC Genomics

Abstract

Background: RNA-seq can be used to measure allele-specific expression (ASE) by assigning sequence reads to individual alleles; however, relative ASE is systematically biased when sequence reads are aligned to a single reference genome. Aligning sequence reads to both parental genomes can eliminate this bias, but this approach is not always practical, especially for non-model organisms. To improve accuracy of ASE measured using a single reference genome, we identified properties of differentiating sites responsible for biased measures of relative ASE. Results: We found that clusters of differentiating sites prevented sequence reads from an alternate allele from aligning to the reference genome, causing a bias in relative ASE favoring the reference allele. This bias increased with greater sequence divergence between alleles. Increasing the number of mismatches allowed when aligning sequence reads to the reference genome and restricting analysis to genomic regions with fewer differentiating sites than the number of mismatches allowed almost completely eliminated this systematic bias. Accuracy of allelic abundance was increased further by excluding differentiating sites within sequence reads that could not be aligned uniquely within the genome (imperfect mappability) and reads that overlapped one or more insertions or deletions (indels) between alleles. Conclusions: After aligning sequence reads to a single reference genome, excluding differentiating sites with at least as many neighboring differentiating sites as the number of mismatches allowed, imperfect mappability, and/or an indel(s) nearby resulted in measures of allelic abundance comparable to those derived from aligning sequence reads to both parental genomes.

AI snapshot

Written from the paper’s full text.

TL;DR

When RNA-seq reads are aligned to a single reference genome, clusters of differentiating sites and nearby indels cause reads from the alternate allele to misalign, biasing relative allele-specific expression toward the reference allele. Excluding differentiating sites with as many or more neighboring differentiating sites than mismatches allowed, imperfect mappability, and/or nearby indels yields allelic abundance measures comparable to aligning reads to both parental genomes.

Key findings

  • In simulated 36-base reads from two *D. melanogaster* genotypes aligned with one mismatch, 50.9% of differentiating sites had unequal allelic abundance, 99.3% of which were biased toward the reference allele; among sites with at least one neighboring differentiating site, 99.8% favored the reference allele. Allowing two or three mismatches increased equal abundance to 80.2% and 91.9%, while aligning to both parental genomes with zero mismatches gave 99.0% equal abundance.
  • Bias increased with sequence divergence: the interspecific *D. melanogaster*/*D. simulans* simulation (1,130,435 differentiating sites) showed stronger reference bias than the intraspecific comparison, but parental-genome alignment made divergence impact negligible. Longer 50-base reads were more biased at the same mismatch allowance (40.6%, 73.0%, and 88.9% equal at one, two, or three mismatches), but matching the mismatch ratio (three for 36-base, four for 50-base) gave 91.9% and 92.1% equal abundance.
  • Imperfect mappability and nearby indels also caused inaccurate measures: for interspecific sites with perfect mappability, ~94% showed equal abundance when aligned to a single reference genome versus 99.9% when aligned to both parental genomes. In real F1 hybrid genomic DNA reads, equal allelic abundance was 70.4%, 88.9%, and 93.3% with one, two, or three mismatches to a single genome and 96.9% with parental genomes; excluding problematic sites raised single-reference measures to 96.3–96.6%, comparable to parental alignment.
  • Filtering retained 46.6–86.9% of differentiating sites in the intraspecific simulation and 8.3–50.5% in the interspecific simulation, but 81.8–91.8% and 66.3–95.2% of exons, respectively, still contained at least one reliable differentiating site.

Methods

  • Simulated equal numbers of 36- and 50-base reads from constitutive *D. melanogaster* exons for the dm3 reference allele versus a DGRP line_40 alternative allele (93,781 altered coding sites; 93,395,272 36-base reads, ~3.4 Gb). Reads were aligned with Bowtie v0.12.7 uniquely allowing 0–3 mismatches to a single reference genome and to both allele-specific genomes, then quantified with SAMtools and custom Perl/R scripts.
  • Interspecific simulation and real-data validation used 60,040 orthologous *D. melanogaster*/*D. simulans* exons (1,130,435 differentiating sites) and published 36-base Illumina genomic DNA reads from female F1 hybrids. Reads were aligned to the single *D. melanogaster* exome or both parental exomes; binomial exact tests with a 0.05 false discovery rate were applied to sites with ≥20 overlapping reads.
  • Neighboring differentiating sites were counted as the maximum number of differences in any overlapping read; mappability was calculated with the GEM library; indels were identified from FSA pairwise alignments.

Limitations

  • The paper states that true relative ASE is rarely known, so real-data validation used genomic DNA from F1 hybrids, where maternal and paternal alleles are expected in equal amounts, rather than RNA. It also states that filtering is expected to preferentially eliminate the most rapidly evolving exons.
  • The paper notes that aligning reads to both parental genomes is not always practical, especially for non-model organisms, and the single-reference correction depends on knowing differentiating sites and the mismatch allowance used.
  • Evident from the text: analyses are limited to *Drosophila* simulations and one *D. melanogaster*/*D. simulans* F1 hybrid dataset; performance in other taxa, at other polymorphism levels, and with newer aligners or read lengths is not tested.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/1471-2164-14-536

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

25 of 29 cited sources checked · 12 supported · 1 flagged · accuracy not yet measured

  • Premisecitation

    Aligning sequence reads to both parental genomes can eliminate the systematic bias in relative allele-specific expression favoring the reference allele.

    “two alternative strategies for aligning reads have been shown to eliminate the systematic bias in measures of relative ASE favoring the reference allele”
    • Also stated in: § The systematic bias in measures of ASE correlates with the density of differentiating sites · boosted
      “aligning reads to the maternal and paternal genomes, which is a strategy that has previously been shown to produce unbiased measures of relative ASE [15,18-20,25]”
    Quote in the paperSubstantial claim4 of 5 sources checked
    • cites [15] McManus CJ, Coolon JD, Duff MO, Eipper-Mains J, Graveley BR, Wittkopp PJ: Regulatory divergence in Drosophila revealed by mRNA-seq. Genome Res. 2010, 20: 816-825. 10.1101/gr.102491.109. source

      The Results and Discussion section says that reads from mixed parental and hybrid samples were independently aligned to both parental genomes, supporting the alignment strategy. The paper reports checks on allele-assignment accuracy, but our copy does not show that this strategy eliminated systematic bias favoring a reference allele. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
    • cites [18] Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N, Bhardwaj N, Rubin M, Snyder M, Gerstein M: AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol. 2011, 7: 522- source
    • cites [19] Rivas-Astroza M, Xie D, Cao X, Zhong S: Mapping personal functional data to personal genomes. Bioinformatics. 2011, 27: 3427-3429. 10.1093/bioinformatics/btr578. source
    • cites [20] Satya RV, Zavaljevski N, Reifman J: A new strategy to reduce allelic bias in RNA-Seq readmapping. Nucleic Acids Res. 2012, 40: e127-10.1093/nar/gks425. source

      The Results on simulated reads report that mapping to the paper’s enhanced reference almost completely eliminated the average bias favoring the reference allele. But the Methods describe an enhanced reference containing alternate alleles at known SNPs, not alignment to both parental genomes; the paper does not establish that specific strategy’s effect. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites [25] Graze RM, McIntyre LM, Main BJ, Wayne ML, Nuzhdin SV: Regulatory divergence in Drosophila melanogaster and D. simulans, a genomewide analysis of allele-specific expression. Genetics. 2009, 183: 547-561. 10.1534/genetics.109.105957. 61– 1SI–21SI source

      The Materials and Methods and File S1 passages describe tiling-array probes, hybridization bias, and DNA controls—not alignment of sequence reads to both parental genomes. Searches for read alignment and reference-allele bias found no passage testing whether that strategy eliminates systematic bias. No page numbers were available in our copy. (LLM, conf=0.75)

      Experimental — accuracy not yet measured
  • Premisecitation

    Relative allele-specific expression is systematically biased when sequence reads are aligned to a single reference genome.

    “relative ASE is systematically biased when sequence reads are aligned to a single reference genome”
    • Also stated in: § Background · plain
      “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.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Degner et al. [14] Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, Pritchard JK: Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009, 25: 3207-3212. 10.1093/bioinformatics/btp579. source
  • Premisecitation

    The alignment parameters used by Degner et al. allowed two or fewer bases within each read to differ from the reference genome.

    “The alignment parameters they used allowed two or fewer bases within each read to differ from the reference genome.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Degner et al. [14] Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, Pritchard JK: Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009, 25: 3207-3212. 10.1093/bioinformatics/btp579. source
  • Premisecitation

    A systematic bias occurs when reads from a heterozygous individual are aligned to a single reference genome.

    “a systematic bias occurs when reads from a heterozygous individual are aligned to a single reference genome [14].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [14] Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, Pritchard JK: Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009, 25: 3207-3212. 10.1093/bioinformatics/btp579. source
  • Findingcitation

    Simulated datasets contained either ~10-fold or ~100-fold more differentiating sites than the human genotypes used to validate other methods for measuring relative allele-specific expression.

    “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].”
    Quote in the paperSubstantial claim3 of 3 sources checked
    • cites [14] Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, Pritchard JK: Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009, 25: 3207-3212. 10.1093/bioinformatics/btp579. source

      Methods §2.2 describes using human HapMap genotypes to measure allele-specific expression, and Results §3 reports 1,981 heterozygous SNPs meeting the read threshold. The paper does not establish that the citing document’s simulated datasets had approximately 10 or 100 times as many differentiating sites, so the numerical comparison is not supported by this source. (LLM, conf=0.78)

      Experimental — accuracy not yet measured
    • cites [18] Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N, Bhardwaj N, Rubin M, Snyder M, Gerstein M: AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol. 2011, 7: 522- source

      The Introduction (page not provided) says HapMap III SNP calls used in earlier studies were approximately 10-fold fewer than those available from the 1000 Genomes pilot II genotypes. It does not compare simulated datasets with those genotypes. Its approximately 100-fold figure concerns the number of possible comparisons between allele-specific binding and expression SNPs, not the number of differentiating sites. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
    • cites [20] Satya RV, Zavaljevski N, Reifman J: A new strategy to reduce allelic bias in RNA-Seq readmapping. Nucleic Acids Res. 2012, 40: e127-10.1093/nar/gks425. source

      The Materials and Methods (Simulated data; page unavailable) describe reads simulated at human SNP loci, and the Results (Results on RNA-Seq data; page unavailable) describe evaluating mapping methods with data from two Yoruba HapMap individuals. The paper does not state that simulated datasets had approximately 10- or 100-fold more differentiating sites than those human genotypes. (LLM, conf=0.82)

      Experimental — accuracy not yet measured
  • Classified as probably not a substantial claim. 13
  • May combine two claims. 12
  • The quote only partly states this claim. 2
  • Classified as probably not a substantial claim.

    Premisecitation

    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.

    “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.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Degner et al. [14] Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, Pritchard JK: Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009, 25: 3207-3212. 10.1093/bioinformatics/btp579. source

      Methods §2.2 says reads could be mapped with fewer than three mismatching bases, so a read did not need to match the reference genome perfectly to be counted. Allele counts were determined by the base observed at a heterozygous SNP, not by whether the read had any difference anywhere from the reference genome. (LLM, conf=0.88)

      Checked by a reasoning model — may be wrong.

      Experimental — accuracy not yet measured
  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    After aligning reads, differentiating sites with fewer than 20 overlapping reads were excluded, and binomial exact tests with a false discovery rate threshold of 0.05 were used to test each differentiating site for a statistically significant difference in relative allelic abundance.

    “Therefore, after aligning reads, we excluded differentiating sites with fewer than 20 overlapping reads and used binomial exact tests with a false discovery rate threshold of 0.05 to test each differentiating site for a statistically significant difference in relative allelic abundance [15,27].”
    Quote in the paperSubstantial claim2 of 2 sources checked
    • cites [15] McManus CJ, Coolon JD, Duff MO, Eipper-Mains J, Graveley BR, Wittkopp PJ: Regulatory divergence in Drosophila revealed by mRNA-seq. Genome Res. 2010, 20: 816-825. 10.1101/gr.102491.109. source

      Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [27] Fontanillas P, Landry CR, Wittkopp PJ, Russ C, Gruber JD, Nusbaum C, Hartl DL: Key considerations for measuring allelic expression on a genomic scale using high-throughput sequencing. Mol Ecol. 2010, 19 (1): 212-227. source

      Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • Rests on an assumption: In F1 hybrids, all maternal and paternal alleles are present in equal amounts.
  • May combine two claims.

    Premisecitation

    Mapping reads to specific genes is relatively straightforward with available bioinformatics tools, but using these tools to distinguish between reads derived from alternative alleles of the same gene remains challenging.

    “Mapping reads to specific genes is relatively straightforward with the bioinformatics tools available today [10-13], but using these tools to distinguish between reads derived from alternative alleles of the same gene remains challenging [9].”
    Quote in the paperSubstantial claim5 of 5 sources checked
    • cites [10] Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10: R25-10.1186/gb-2009-10-3-r25. source
    • cites [11] Li H, Durbin R: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010, 26: 589-595. 10.1093/bioinformatics/btp698. source
    • cites [12] Li H, Ruan J, Durbin R: Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008, 18: 1851-1858. 10.1101/gr.078212.108. source

      In “Single end read mapping” (page unavailable), the paper says sequence variants can cause reads to align incorrectly to a reference, supporting a difficulty relevant to allele-derived reads. Its “Consensus genotype calling” section (page unavailable) addresses inferring genotypes from multiple reads, but the paper does not specifically say that available tools struggle to distinguish individual reads from alternative alleles of the same gene. (LLM, conf=0.80)

      Experimental — accuracy not yet measured
    • cites [13] Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352. source
    • cites [9] DeVeale B, van der Kooy D, Babak T: Critical evaluation of imprinted gene expression by RNA-Seq: a new perspective. PLoS Genet. 2012, 8: e1002600-10.1371/journal.pgen.1002600. source
  • May combine two claims.

    Premisereasoning

    The polymorphism-aware alignment strategy is likely most effective for intraspecific studies of species with relatively low levels of polymorphism because the number of possible haplotypes increases exponentially with the number of polymorphic sites.

    “This is a viable strategy for both model and non-model species, but will likely be most effective for intraspecific studies of species like humans with relatively low levels of polymorphism because the number of possible haplotypes increases exponentially with the number of polymorphic sites.”
    Quote in the paperNot a substantial claim2 of 2 sources checked
    • cites [21] Wu TD, Nacu S: Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010, 26: 873-881. 10.1093/bioinformatics/btq057. source

      Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
    • cites [22] Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM: A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Res. 2011, 21: 1728-1737. 10.1101/gr.119784.110. source

      Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

      Experimental — accuracy not yet measured
  • May combine two claims.

    Premisecitation

    Controlling for sites known to be polymorphic in humans prior to aligning simulated reads produced symmetrical measures of relative allele-specific expression, showing that the differentiating sites themselves caused the bias.

    “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.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Degner et al. [14] Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, Pritchard JK: Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009, 25: 3207-3212. 10.1093/bioinformatics/btp579. source

      The Results report that masking known SNP sites before mapping eliminated the overall reference-allele bias in simulated and real data. But strong biases remained at individual SNPs; the Results attribute these to similarities between flanking sequences and other genomic regions. Thus, the paper does not show that the differentiating sites themselves caused the bias. (LLM, conf=0.88)

      Experimental — accuracy not yet measured
  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Measures of allele-specific expression are particularly important for studying the regulation of gene expression because they can be used to distinguish cis- and trans-regulatory changes and to detect genomic imprinting.

    “Measures of allele-specific expression (ASE) are particularly important for studying the regulation of gene expression because they can be used to distinguish cis- and trans-regulatory changes [6,7] and to detect genomic imprinting [8,9].”
    Quote in the paperSubstantial claim2 of 4 sources checked
    • cites [6] Wittkopp PJ, Haerum BK, Clark AG: Evolutionary changes in cis and trans gene regulation. Nature. 2004, 430: 85-88. 10.1038/nature02698. source
    • cites [7] Cowles CR, Hirschhorn JN, Altshuler D, Lander ES: Detection of regulatory variation in mouse genes. Nat Genet. 2002, 32: 432-437. 10.1038/ng992. source
    • cites [8] Coolon JD, Stevenson KR, McManus CJ, Graveley BR, Wittkopp PJ: Genomic imprinting absent in Drosophila melanogaster adult females. Cell Rep. 2012, 2: 69-75. 10.1016/j.celrep.2012.06.013. source
    • cites [9] DeVeale B, van der Kooy D, Babak T: Critical evaluation of imprinted gene expression by RNA-Seq: a new perspective. PLoS Genet. 2012, 8: e1002600-10.1371/journal.pgen.1002600. source
  • May combine two claims.

    Findingown data

    In simulated D. melanogaster data with one mismatch allowed, 50.9% of differentiating sites had unequal measures of allelic abundance, and 99.3% of these were biased toward the reference allele.

    “We found that 50.9% of differentiating sites had unequal measures of allelic abundance, 99.3% of which were biased toward the reference allele.”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.

    Findingown data

    Subsequently excluding differentiating sites with indels nearby increased the percentage of sites with equal allelic abundance by an additional approximately 0.1%.

    “subsequently excluding differentiating sites with indels nearby increased the percentage of genes with equal allelic abundance an additional ~0.1% (Figure 5B).”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Findingown data

    Aligning simulated reads independently to the reference and alternative genomes with zero mismatches allowed resulted in 99.0% of differentiating sites showing equal representation of the two alleles, with the rest showing no systematic bias toward either allele.

    “We found that 99.0% of differentiating sites showed equal representation of the two alleles, with the rest showing no systematic bias toward either allele (Figure 2D).”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Findingown data

    For 50-base reads aligned to a single reference genome, 40.6%, 73.0%, and 88.9% of differentiating sites showed equal representation of the two alleles when one, two, or three mismatches were allowed, respectively.

    “We found that 40.6%, 73.0%, and 88.9% of differentiating sites showed equal representation of the two alleles when aligned to a single reference genome with one, two or three mismatches allowed (Figure 2E-G).”
    Quote in the paperSubstantial claim
  • The quote only partly states this claim.

    Findingown data

    Excluding differentiating sites within sequence reads that could not be aligned uniquely (imperfect mappability) increased the accuracy of allelic abundance.

    “For both the 36- and 50-base reads simulated from the two D. melanogaster genotypes, > 97.9% of sites with perfect mappability showed the expected equal abundance of the reference and alternative alleles under all mapping conditions (Figure 3A-H).”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Further restricting the set of differentiating sites to those with perfect mappability increased the percentage of sites with equal allelic abundance by approximately 0.1%.

    “Further restricting the set of differentiating sites to those with perfect mappability increased these percentages ~0.1%”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Only 1.9% of all reads were excluded because they failed to map uniquely to at least one genome when aligning to both genomes.

    “Only 1.9% of all reads were excluded because they failed to map uniquely to at least one genome.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Findingown data

    Sites with perfect mappability that had an indel nearby showed more reads assigned to D. melanogaster than D. simulans allele when reads were aligned to only the D. melanogaster genome, whereas sites with perfect mappability that lacked such an indel did not.

    “Consistent with this hypothesis, we found that sites with perfect mappability that had an indel nearby showed more reads assigned to D. melanogaster than D. simulans allele when reads were aligned to only the D. melanogaster genome, whereas sites with perfect mappability that lacked such an indel did not (Figure 4A-C).”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Findingown data

    Simulated datasets showed that reads containing as many or more neighboring differentiating sites as mismatches allowed during alignment, imperfect mappability, and/or an indel(s) between alleles can cause inaccurate measures of relative allelic abundance.

    “our simulated datasets showed that reads containing (1) as many or more neighboring differentiating sites as mismatches allowed during alignment, (2) imperfect mappability, and/or (3) an indel(s) between alleles can cause inaccurate measures of relative allelic abundance.”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given

    The two D. melanogaster genotypes compared did not include any indels.

    “The two D. melanogaster genotypes compared did not include any indels, as described in the main text.”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim.

    Premisereasoning

    Because real sequencing data involves stochastic sampling, the proportion of the reference allele observed was not always expected to be 0.5.

    “Because real sequencing data involves stochastic sampling, the proportion of the reference allele observed was not always expected to be 0.5.”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Study design and conductreasoning

    Indels were not a factor in comparisons of the two D. melanogaster strains because the alternative allele was constructed by changing only single nucleotides in the reference allele.

    “Indels were not a factor in our comparisons of the two D. melanogaster strains because the alternative allele was constructed by changing only single nucleotides in the reference allele.”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    To quantify transcript abundance using RNA-seq, each short sequence read is compared to an annotated reference genome, and the number of reads aligning to a gene is used as a proxy for its relative expression level.

    “To quantify transcript abundance using RNA-seq, each short sequence read (hereafter simply called a “read”) is compared to an annotated reference genome. Assignment of a read to a specific gene is made by finding the region of the genome with the highest sequence similarity, and the number of reads aligning to a gene is used as a proxy for its relative expression level [4].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [4] Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008, 5: 621-628. 10.1038/nmeth.1226. source
  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Because the reference and alternative alleles differed only at predefined differentiating sites, only reads overlapping these sites had the possibility to be informative for relative allele-specific expression.

    “Because the reference and alternative alleles differed only at these predefined differentiating sites, only reads overlapping these sites had the possibility to be informative for relative ASE.”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    The specific D. melanogaster line used for simulation was chosen because it had the fewest sites with evidence of residual heterozygosity.

    “This specific line was chosen because it had the fewest sites with evidence of residual heterozygosity.”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim.

    Study design and conductown data

    Binomial exact tests with a false discovery rate of 0.05 were used to assess statistical significance, for differentiating sites with 20 or more overlapping reads.

    “Binomial exact tests and a false discovery rate of 0.05 were used to assess statistical significance in all cases.”
    • Also stated in: § Quantifying relative ASE in an F1 hybrid between D. melanogaster and D. simulans · plain
      “We performed binomial exact tests for differentiating sites with 20 or more overlapping reads, controlling the false discovery rate at 0.05 to correct for multiple comparisons.”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The most rapidly evolving exons are expected to be preferentially eliminated by excluding problematic sites.

    “although the most rapidly evolving exons are expected to be preferentially eliminated.”
    Quote in the paperNot a substantial claim
23 of 23
  • The authors state this as an assumption.

    The number of differentiating sites with allele-specific mappability differences is similar between alleles.

    “Assuming the number of such differentiating sites is similar between alleles, differences in allele-specific mappability should not systematically favor one allele or the other, but will still cause errors in relative ASE.”
  • The paper relies on this without support.

    In F1 hybrids, all maternal and paternal alleles are present in equal amounts.

    “we used sequence data that was collected in a comparable manner from genomic DNA extracted from F1 hybrids, in which all maternal and paternal alleles are expected to be present in equal amounts.”
  • The paper relies on this without support.

    The true value of relative allele-specific expression is rarely known.

    “Assessing the accuracy of relative ASE measurements derived from RNA-seq data is challenging because the true value of relative ASE is rarely known.”
  • The paper relies on this without support.

    Independent empirical methods for measuring relative allele-specific expression are not suitable for quantifying relative allele-specific expression on a genomic scale.

    “Independent empirical methods for measuring relative ASE such as Pyrosequencing and qPCR can be used to validate RNA-seq data for individual genes, but they are not suitable for quantifying relative ASE on a genomic scale.”
  • The authors state this as an assumption.

    Reads aligning uniquely to one or the other allele-specific genome are evidence that the read is allele-specific, while reads aligning equally well to both genomes are not.

    “We posited that reads aligning uniquely to one or the other allele-specific genome was evidence that that read was allele-specific, while reads aligning equally well to both genomes was not.”
  • The paper relies on this without support.

    Sequence divergence is lower within D. melanogaster than between D. melanogaster and D. simulans.

    “is consistent with the lower sequence divergence within than between species.”
6 of 6

Self-check

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

Each contradiction between two passages listed here was confirmed by an independent check.

  • Neighboring-site filter uses two different thresholds

    Minor

    The first passage says sites are excluded only when the number of neighboring differentiating sites is greater than the number of mismatches allowed. The figure caption instead defines retained, unbiased sites as having fewer neighbors than that number, which also excludes sites with an equal number.

  • An exon-read position is omitted from the stated range

    Minor

    For a read of length k in an exon of length n, a read can start at position n-k+1. The stated range ends at n-k, contradicting the claim that reads were generated from every possible position.

  • Reported filtering outcome switches from sites to genes

    Minor

    The analysis says it measures the percentage of retained differentiating sites with equal allelic abundance, but reports the final increase as a percentage of genes. No gene-level percentage is described for this comparison.

  • Indels assigned to the intraspecific dataset despite its having none

    Minor

    The text describes excluding sites near indels from the intraspecific dataset, but elsewhere states that the two D. melanogaster alleles used for that comparison had no indels.

4 of 4

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Stevenson, K. R., Coolon, J. D., & Wittkopp, P. J.
Affiliations
University of Michigan (US)
Corresponding author
Wittkopp, Patricia J
Venue
BMC Genomics
Published
2013-08-07
Volume
14
Issue
1
Pages
536-536
ISSN
1471-2164 (ISSN-L)
OpenAlex
W2164158485
Cited by
140
References
29
Citations by year
2026: 3 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 4 · 2021: 15 · 2020: 14 · 2019: 12 · 2018: 9 · 2017: 9 · 2016: 14 · 2015: 16 · 2014: 19 · 2013: 2
FWCI
6.86
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • BMC Genomics (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Deep Blue (University of Michigan) (submittedVersion)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • National Science Foundation (1021398, 0903629)
  • National Institutes of Health (1f32gm089009-01a1)
  • National Institute of General Medical Sciences (1F32-GM089009-01A1)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
allele-specific expression, RNA-seq, reference genome bias, indels, non-model organisms, read mapping
MeSH terms
Alleles, Animals, Drosophila, Exons, Genome, Sequence Analysis, RNA

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.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-13.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-13.

Cited sources are checked by gpt-6-sol.

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 ›

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