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Cited by 375Genomics and Phylogenetic StudiesRNA Research and SplicingSingle-cell and spatial transcriptomics

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Tools and best practices for data processing in allelic expression analysis.

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Castel, Stephane E., Levy-Moonshine, Ami, Mohammadi, Pejman, Banks, Eric, Lappalainen, Tuuli·2015·Genome Biology
DOI 10.1186/s13059-015-0762-6PMC PMC4574606

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Abstract

ABSTRACT Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis -regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE 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 and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.

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Fig. 1 Allelic expression and its sources. a Schematic illustration of AE. b Biological sources of AE, with the x-axis denoting the approximate sharing of AE across tissues of an individual, and the y-axis having the estimated sharing of AE signal in one tissue across different individuals [5, 8, 12, 13, 15]. SNP single-nucleotide polymorphism
Fig. 1 Allelic expression and its sources. a Schematic illustration of AE. b Biological sources of AE, with the x-axis denoting the approximate sharing of AE across tissues of an individual, and the y-axis having the estimated sharing of AE signal in one tissue across different individuals [5, 8, 12, 13, 15]. SNP single-nucleotide polymorphismCC BY · PubMed Central
Fig. 2 Genomic coverage of AE data in Geuvadis CEU samples. a Cumulative distribution of RNA-seq read coverage per het-SNP (each line represents one sample). b, c The number of het-SNPs (b) and protein-coding genes (c) per sample as a function of coverage cutoff. d The number of protein-coding genes with AE data versus the number of het-SNPs they contain. Each line is the median for all samples at a specific coverage level
Fig. 2 Genomic coverage of AE data in Geuvadis CEU samples. a Cumulative distribution of RNA-seq read coverage per het-SNP (each line represents one sample). b, c The number of het-SNPs (b) and protein-coding genes (c) per sample as a function of coverage cutoff. d The number of protein-coding genes with AE data versus the number of het-SNPs they contain. Each line is the median for all samples at a specific coverage levelCC BY · PubMed Central
Fig. 3 Strategies for reducing mapping bias in AE analysis. a Summary of various strategies to correct for mapping bias (Baseline = STAR aligned only, Filtering = STAR aligned with bias and mappability filters, P. Genome = STAR aligned to a personalized genome generated with Allele-Seq, WASP = STAR aligned with removal of biased reads using WASP, Variant Aware = GSNAP in variant aware alignment mode). The boxplot (axis on the left) shows reference ratios for AE sites covered by eight or more reads. The mean reference ratio for each strategy is shown with a white dash; the solid black line indicates a reference ratio of 0.5, while dotted lines indicate ±0.05. The percentages of sites that are monoallelic reference (grey circle) or alternative (grey diamond) are plotted against the secondary axis. The number of sites with AE data for each strategy is shown as a percentage of the baseline strategy underneath their respective labels. Outliers are hidden for ease of viewing. b Percentage of sites that are removed when bias and mappability filters are applied to resulting data from all strategies, shown for each reference ratio bin
Fig. 3 Strategies for reducing mapping bias in AE analysis. a Summary of various strategies to correct for mapping bias (Baseline = STAR aligned only, Filtering = STAR aligned with bias and mappability filters, P. Genome = STAR aligned to a personalized genome generated with Allele-Seq, WASP = STAR aligned with removal of biased reads using WASP, Variant Aware = GSNAP in variant aware alignment mode). The boxplot (axis on the left) shows reference ratios for AE sites covered by eight or more reads. The mean reference ratio for each strategy is shown with a white dash; the solid black line indicates a reference ratio of 0.5, while dotted lines indicate ±0.05. The percentages of sites that are monoallelic reference (grey circle) or alternative (grey diamond) are plotted against the secondary axis. The number of sites with AE data for each strategy is shown as a percentage of the baseline strategy underneath their respective labels. Outliers are hidden for ease of viewing. b Percentage of sites that are removed when bias and mappability filters are applied to resulting data from all strategies, shown for each reference ratio binCC BY · PubMed Central
Fig. 4 Quality control of genotype data for AE analysis. a Median percentage of het-SNPs where RNA-seq reads from both alleles are observed across all tissues for GTEx samples, genotyped with different platforms: exome-seq (yellow), Illumina OMNI 5 M SNP array (blue), and sites imputed from OMNI 5 M genotype array (red). Grey arrowheads indicate outlier individuals that are likely to have lower genotype quality. b Total het-SNP read count versus the read count of the lesser-covered allele for an individual Geuvadis sample. Sites flagged as putative genotyping errors are marked in red, with RNA-seq data not rendering support for heterozygosity
Fig. 4 Quality control of genotype data for AE analysis. a Median percentage of het-SNPs where RNA-seq reads from both alleles are observed across all tissues for GTEx samples, genotyped with different platforms: exome-seq (yellow), Illumina OMNI 5 M SNP array (blue), and sites imputed from OMNI 5 M genotype array (red). Grey arrowheads indicate outlier individuals that are likely to have lower genotype quality. b Total het-SNP read count versus the read count of the lesser-covered allele for an individual Geuvadis sample. Sites flagged as putative genotyping errors are marked in red, with RNA-seq data not rendering support for heterozygosityCC BY · PubMed Central
Fig. 5 Technical covariates of AE. a Correlation of AE with technical covariates, measured as correlation (R2) between each covariate and the percentage of significant AE sites in a sample (binomial p < 0.05, het-SNPs with ≥30 reads), both before and after scaling to 30 reads. b Correlation of gene expression with technical covariates. As the gene expression statistic we use the median correlation of each sample to all other samples (D-statistic). Correlation to a biological covariate (population) is shown for comparison. Correlations were calculated from all Geuvadis samples by Spearman correlation for continuous covariates, or linear regression for categorical covariates. **p < 0.01, *p < 0.05, after Bonferroni correction. RIN RNA integrity number, Stdev standard deviation
Fig. 5 Technical covariates of AE. a Correlation of AE with technical covariates, measured as correlation (R2) between each covariate and the percentage of significant AE sites in a sample (binomial p < 0.05, het-SNPs with ≥30 reads), both before and after scaling to 30 reads. b Correlation of gene expression with technical covariates. As the gene expression statistic we use the median correlation of each sample to all other samples (D-statistic). Correlation to a biological covariate (population) is shown for comparison. Correlations were calculated from all Geuvadis samples by Spearman correlation for continuous covariates, or linear regression for categorical covariates. **p < 0.01, *p < 0.05, after Bonferroni correction. RIN RNA integrity number, Stdev standard deviationCC BY · PubMed Central
Fig. 6 QC measures reduce false positives, demonstrated with a binomial test for allelic imbalance. a QQ plot of p values generated from binomial testing after various QC measures. Baseline = STAR aligned testing against a null of 0.5 without any correction for double counting, mapping bias, or genotyping error; No Double Counting = as Baseline but without duplicates and overlapping mate pairs counted once; Site Filter = as No Double Counting but without biased and low mappability het-SNPs; Adjusted Null = As Site Filter but using mean per base reference ratio as the binomial null; WASP Filter = as Site Filter but with WASP filtering of reads; Monoallelic Filter = as Adjusted Null but removing monoallelic sites to account for putative genotyping error. b Histogram showing distribution of coverage for sites with significant (5 % FDR) allelic imbalance according to a binomial test (primary axis), and the percentage of all het-SNPs that show significant allelic imbalance in each coverage bin using increasing allelic effect cutoffs (secondary axis). c, d Multidimensional scaling (MDS) clustering of Geuvadis samples based on proportion of sites with significant AE that differs between sample pairs. Samples are colored by sequencing laboratory and labeled by population. If significant sites are assigned based on a simple binomial test (FDR 5 %), the samples cluster first by sequencing laboratory due to lab-specific differences in coverage (c). This effect is mostly removed in (d) by requiring significant sites to have FDR 5 % and effect size > 0.15
Fig. 6 QC measures reduce false positives, demonstrated with a binomial test for allelic imbalance. a QQ plot of p values generated from binomial testing after various QC measures. Baseline = STAR aligned testing against a null of 0.5 without any correction for double counting, mapping bias, or genotyping error; No Double Counting = as Baseline but without duplicates and overlapping mate pairs counted once; Site Filter = as No Double Counting but without biased and low mappability het-SNPs; Adjusted Null = As Site Filter but using mean per base reference ratio as the binomial null; WASP Filter = as Site Filter but with WASP filtering of reads; Monoallelic Filter = as Adjusted Null but removing monoallelic sites to account for putative genotyping error. b Histogram showing distribution of coverage for sites with significant (5 % FDR) allelic imbalance according to a binomial test (primary axis), and the percentage of all het-SNPs that show significant allelic imbalance in each coverage bin using increasing allelic effect cutoffs (secondary axis). c, d Multidimensional scaling (MDS) clustering of Geuvadis samples based on proportion of sites with significant AE that differs between sample pairs. Samples are colored by sequencing laboratory and labeled by population. If significant sites are assigned based on a simple binomial test (FDR 5 %), the samples cluster first by sequencing laboratory due to lab-specific differences in coverage (c). This effect is mostly removed in (d) by requiring significant sites to have FDR 5 % and effect size > 0.15CC BY · PubMed Central
Fig. 7 QC measures improve the power to detect biologically relevant AE at genes that have eQTLs (eGenes), where individuals that are heterozygous for the top eQTL SNP (eSNP) are expected to have more AE than homozygous individuals. Plot of median AE in heterozygous versus homozygous individuals for each eGene, before (a) and after (b) QC measures. Red points indicate a significant (1 % FDR) difference in AE level in the expected direction (AE het > AE homo, true positive), blue points indicate a significant difference in the opposite direction (AE het < AE homo, false positive), and the number of true and false positives is listed. c Boxplot of the percentage of individuals showing allelic imbalance (AE > 0.25) who are either heterozygous or homozygous for the top eQTL at each eGene before and after QC measures. Outliers are hidden for ease of viewing. d Mean percentage of het-SNPs that are found within heterozygous eGenes in bins of AE across individuals before and after QC measures. Error bars represent the standard error of the mean, and asterisks indicate a significant difference (1 % FDR) after applying QC measures for that bin
Fig. 7 QC measures improve the power to detect biologically relevant AE at genes that have eQTLs (eGenes), where individuals that are heterozygous for the top eQTL SNP (eSNP) are expected to have more AE than homozygous individuals. Plot of median AE in heterozygous versus homozygous individuals for each eGene, before (a) and after (b) QC measures. Red points indicate a significant (1 % FDR) difference in AE level in the expected direction (AE het > AE homo, true positive), blue points indicate a significant difference in the opposite direction (AE het < AE homo, false positive), and the number of true and false positives is listed. c Boxplot of the percentage of individuals showing allelic imbalance (AE > 0.25) who are either heterozygous or homozygous for the top eQTL at each eGene before and after QC measures. Outliers are hidden for ease of viewing. d Mean percentage of het-SNPs that are found within heterozygous eGenes in bins of AE across individuals before and after QC measures. Error bars represent the standard error of the mean, and asterisks indicate a significant difference (1 % FDR) after applying QC measures for that binCC BY · PubMed Central

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Research works printed but not linked: 32

  1. Adoue V, Schiavi A, Light N, Almlöf JC, Lundmark P, Ge B, et al. Allelic expression mapping across cellular lineages to establish impact of non-coding SNPs. Mol Syst Biol. 2014;10:754. · DOI
  2. Buil A, Brown AA, Lappalainen T, Viñuela A, Davies MN, Zheng H-F, et al. Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins. Nat Genet. 2015;47:88–91. · DOI
  3. Ge B, Pokholok DK, Kwan T, Grundberg E, Morcos L, Verlaan DJ, et al. Global patterns of cis variation in human cells revealed by high-density allelic expression analysis. Nat Genet. 2009;41:1216–22. · DOI
  4. Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 2014;24:14–24. · DOI · Paper page
  5. Kukurba KR, Zhang R, Li X, Smith KS, Knowles DA, How Tan M, et al. Allelic expression of deleterious protein-coding variants across human tissues. PLoS Genet. 2014;10, e1004304. · DOI
  6. MacArthur DG, Balasubramanian S, Frankish A, Huang N, Morris J, Walter K, et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science. 2012;335:823–8. · DOI · Paper page
  7. Montgomery SB, Lappalainen T, Gutierrez-Arcelus M, Dermitzakis ET. Rare and common regulatory variation in population-scale sequenced human genomes. PLoS Genet. 2011;7, e1002144. · DOI
  8. Baran Y, Subramaniam M, Biton A, Tukiainen T, Tsang EK, Rivas MA, et al. The landscape of genomic imprinting across diverse adult human tissues. Genome Res. 2015;25:927–36. · DOI
  9. Morcos L, Ge B, Koka V, Lam KCL, Pokholok DK, Gunderson KL, et al. Genome-wide assessment of imprinted expression in human cells. Genome Biol. 2011;12:R25. · DOI
  10. Gimelbrant A, Hutchinson JN, Thompson BR, Chess A. Widespread monoallelic expression on human autosomes. Science. 2007;318:1136–40. · DOI · Paper page
  11. Borel C, Ferreira PG, Santoni F, Delaneau O, Fort A, Popadin KY, et al. Biased allelic expression in human primary fibroblast single cells. Am J Hum Genet. 2015;96:70–80. · DOI · Paper page
  12. Soderlund CA, Nelson WM, Goff SA. Allele Workbench: transcriptome pipeline and interactive graphics for allele-specific expression. PLoS One. 2014;9, e115740. · DOI
  13. Leung D, Jung I, Rajagopal N, Schmitt A, Selvaraj S, Lee AY, et al. Integrative analysis of haplotype-resolved epigenomes across human tissues. Nature. 2015;518:350–4. · DOI
  14. Li H, Su X, Gallegos J, Lu Y, Ji Y, Molldrem JJ, et al. dsPIG: a tool to predict imprinted genes from the deep sequencing of whole transcriptomes. BMC Bioinformatics. 2012;13:271. · DOI
  15. Pirinen M, Lappalainen T, Zaitlen NA, GTEx Consortium, Dermitzakis ET, Donnelly P, et al. Assessing allele-specific expression across multiple tissues from RNA-seq read data. Bioinformatics. 2015;31:2497–504. · DOI
  16. Cho H, Davis J, Li X, Smith KS, Battle A, Montgomery SB. High-resolution transcriptome analysis with long-read RNA sequencing. PLoS One. 2014;9, e108095. · DOI
  17. Turro E, Su S-Y, Gonçalves Â, Coin LJM, Richardson S, Lewin A. Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads. Genome Biol. 2011;12:R13. · DOI · Paper page
  18. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303. · DOI · Paper page
  19. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–8. · DOI
  20. Romanel A, Lago S, Prandi D, Sboner A, Demichelis F. ASEQ: fast allele-specific studies from next-generation sequencing data. BMC Med Genomics. 2015;8:9. · DOI
  21. Panousis NI, Gutierrez-Arcelus M, Dermitzakis ET, Lappalainen T. Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies. Genome Biol. 2014;15:467. · DOI
  22. Gutierrez-Arcelus M, Ongen H, Lappalainen T, Montgomery SB, Buil A, Yurovsky A, et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet. 2015;11, e1004958. · DOI
  23. Kilpinen H, Waszak SM, Gschwind AR, Raghav SK, Witwicki RM, Orioli A, et al. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science. 2013;342:744–7. · DOI · Paper page
  24. Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan K-K, Cheng C, et al. Architecture of the human regulatory network derived from ENCODE data. Nature. 2012;489:91–100. · DOI
  25. Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, et al. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol. 2011;7:522–2. · DOI · Paper page
  26. Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81. · DOI · Paper page
  27. van de Geijn B, McVicker G, Gilad Y, Pritchard J. WASP: allele-specific software for robust discovery of molecular quantitative trait loci. bioRxiv. 2014. http://dx.doi.org/10.1101/011221 . · DOI
  28. Piskol R, Ramaswami G, Li JB. Reliable identification of genomic variants from RNA-seq data. Am J Hum Genet. 2013;93:641–51. · DOI
  29. Deelen P, Zhernakova DV, de Haan M, van der Sijde M, Bonder MJ, Karjalainen J, et al. Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels. Genome Med. 2015;7:30. · DOI
  30. Kumasaka N, Knights A, Gaffney D. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. bioRxiv. 2015. http://dx.doi.org/10.1101/018788 . · DOI
  31. 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;21:1728–37. · DOI · Paper page
  32. Zhang R, Li X, Ramaswami G, Smith KS, Turecki G, Montgomery SB, et al. Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing. Nat Methods. 2014;11:51–4. · DOI

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Metadata

Authors
Castel, S. E., Levy-Moonshine, A., Mohammadi, P., Banks, E., & Lappalainen, T.
Affiliations
  • New York Genome Center (US)
  • Broad Institute (US)
Corresponding author
  • Castel, Stephane E.
  • Lappalainen, Tuuli
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2015-09-17
Volume
16
Issue
1
Pages
195-195
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-015-0762-6
PMC
PMC4574606
OpenAlex
W2142933606
Cited by
375
References
43
Citations by year
2026: 29 · 2025: 36 · 2024: 25 · 2023: 45 · 2022: 25 · 2021: 47 · 2020: 33 · 2019: 35 · 2018: 37 · 2017: 38 · 2016: 21 · 2015: 4
FWCI
14.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • University of Miami (MH090936, MH090937, MH090951, MH090948, DA006227, MH090941)
  • Harvard University (MH090948)
  • University of Chicago (MH090951, MH090937)
  • Broad Institute (HHSN268201000029C)
  • New York Genome Center
  • Université de Genève (MH090941)
  • National Institutes of Health (10XS170, #HHSN261200800001E, HHSN268201000029C, 10XS171, HHSN261200800001C, 10ST1035, MH090937)
  • National Heart, Lung, and Blood Institute (HHSN268201000029C, HHSN261200800001E)
  • National Institute of Mental Health (R01MH101814, R01MH090937, HHSN261200800001E, R01MH090948, R01MH090941, R01MH090936, R01MH090951)
  • National Institute on Drug Abuse (DA006227, R01DA006227)
  • National Human Genome Research Institute (U01HG006569, HHSN261200800001E)
  • NIH Office of the Director (HHSN261200800001E)
  • National Cancer Institute (HHSN261200800001E))
  • National Institute of Neurological Disorders and Stroke
  • Common Fund
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
allelic expression, RNA-seq, quality control, genotyping errors, cis-regulatory variation, nonsense-mediated mRNA decay, bioinformatics software
MeSH terms
Alleles, Cell Line, Data Interpretation, Statistical, Humans, Software, Gene Expression, Sequence Analysis, RNA, Gene Expression Profiling, Genotyping Techniques

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