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

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

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

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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.

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 · 10.1186/s13059-015-0762-6

Cites

0 citation errors · 0 not checkable

Statistics

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?

  • The quality of genotype calls is crucial for allele assignment, so genotype data needs careful collection and quality control and filtering to avoid biases and false positive hits.

    “The quality of the genotype calls is crucial for allele assignment, so the genotype data needs to be collected carefully and quality control and filtering is required to avoid biases and false positive hits 9.”

    From SNPsplit: Allele-specific splitting of alignments between genomes with known SNP genotypes (2016) · cites this paper as [9]

Self-check

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

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

Cites

Flagged first, then by citations

Research works printed but not linked: 36

  1. [1]Adoue V Schiavi A Light N Almlöf JC Lundmark P Ge B Allelic expression mapping across cellular lineages to establish impact of non-coding SNPs Mol Syst Biol 2014 10 754 10.15252/msb.20145114 25326100 PMC4299376 · DOI
  2. [2]Buil A Brown AA Lappalainen T Viñuela A Davies MN Zheng H-F Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins Nat Genet 2015 47 88 91 10.1038/ng.3162 25436857 PMC4643454 · DOI
  3. [3]Ge B Pokholok DK Kwan T Grundberg E Morcos L Verlaan DJ Global patterns of cis variation in human cells revealed by high-density allelic expression analysis Nat Genet 2009 41 1216 22 10.1038/ng.473 19838192 · DOI
  4. [4]Battle A Mostafavi S Zhu X Potash JB Weissman MM McCormick C Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals Genome Res 2014 24 14 24 10.1101/gr.155192.113 24092820 PMC3875855 · DOI · Paper page
  5. [5]Lappalainen T Sammeth M Friedländer MR 't Hoen PAC Monlong J Rivas MA Transcriptome and genome sequencing uncovers functional variation in humans Nature 2013 501 506 11 10.1038/nature12531 24037378 PMC3918453 · DOI · Paper page
  6. [6]Montgomery SB Sammeth M Gutierrez-Arcelus M Lach RP Ingle C Nisbett J Transcriptome genetics using second generation sequencing in a Caucasian population Nature 2010 464 773 7 10.1038/nature08903 20220756 PMC3836232 · DOI · Paper page
  7. [7]Pickrell JK Marioni JC Pai AA Degner JF Engelhardt BE Nkadori E Understanding mechanisms underlying human gene expression variation with RNA sequencing Nature 2010 464 768 72 10.1038/nature08872 20220758 PMC3089435 · DOI · Paper page
  8. [8]GTEx Consortium Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans Science 2015 348 648 60 10.1126/science.1262110 25954001 PMC4547484 · DOI · Paper page
  9. [9]Kukurba KR Zhang R Li X Smith KS Knowles DA How Tan M Allelic expression of deleterious protein-coding variants across human tissues PLoS Genet 2014 10 10.1371/journal.pgen.1004304 24786518 PMC4006732 · DOI
  10. [10]MacArthur DG Balasubramanian S Frankish A Huang N Morris J Walter K A systematic survey of loss-of-function variants in human protein-coding genes Science 2012 335 823 8 10.1126/science.1215040 22344438 PMC3299548 · DOI · Paper page
  11. [11]Montgomery SB Lappalainen T Gutierrez-Arcelus M Dermitzakis ET Rare and common regulatory variation in population-scale sequenced human genomes PLoS Genet 2011 7 10.1371/journal.pgen.1002144 21811411 PMC3141000 · DOI
  12. [13]Baran Y Subramaniam M Biton A Tukiainen T Tsang EK Rivas MA The landscape of genomic imprinting across diverse adult human tissues Genome Res 2015 25 927 36 10.1101/gr.192278.115 25953952 PMC4484390 · DOI
  13. [14]Morcos L Ge B Koka V Lam KCL Pokholok DK Gunderson KL Genome-wide assessment of imprinted expression in human cells Genome Biol 2011 12 R25 10.1186/gb-2011-12-3-r25 21418647 PMC3129675 · DOI
  14. [15]Gimelbrant A Hutchinson JN Thompson BR Chess A Widespread monoallelic expression on human autosomes Science 2007 318 1136 40 10.1126/science.1148910 18006746 · DOI · Paper page
  15. [16]Borel C Ferreira PG Santoni F Delaneau O Fort A Popadin KY Biased allelic expression in human primary fibroblast single cells Am J Hum Genet 2015 96 70 80 10.1016/j.ajhg.2014.12.001 25557783 PMC4289680 · DOI · Paper page
  16. [17]Soderlund CA Nelson WM Goff SA Allele Workbench: transcriptome pipeline and interactive graphics for allele-specific expression PLoS One 2014 9 10.1371/journal.pone.0115740 25541944 PMC4277417 · DOI
  17. [18]Leung D Jung I Rajagopal N Schmitt A Selvaraj S Lee AY Integrative analysis of haplotype-resolved epigenomes across human tissues Nature 2015 518 350 4 10.1038/nature14217 25693566 PMC4449149 · DOI
  18. [19]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. PMC3497615 23083219
  19. [20]Pirinen M Lappalainen T Zaitlen NA GTEx Consortium Dermitzakis ET Donnelly P Assessing allele-specific expression across multiple tissues from RNA-seq read data Bioinformatics 2015 31 2497 504 10.1093/bioinformatics/btv074 25819081 PMC4514921 · DOI
  20. [21]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 10.1371/journal.pone.0108095 25251678 PMC4176000 · DOI
  21. [22]Turro E Su S-Y Gonçalves  Coin LJM Richardson S Lewin A Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads Genome Biol 2011 12 R13 10.1186/gb-2011-12-2-r13 21310039 PMC3188795 · DOI · Paper page
  22. [23]McKenna A Hanna M Banks E Sivachenko A Cibulskis K Kernytsky A The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data Genome Res 2010 20 1297 303 10.1101/gr.107524.110 20644199 PMC2928508 · DOI · Paper page
  23. [24]DePristo MA Banks E Poplin R Garimella KV Maguire JR Hartl C A framework for variation discovery and genotyping using next-generation DNA sequencing data Nat Genet 2011 43 491 8 10.1038/ng.806 21478889 PMC3083463 · DOI
  24. [25]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 10.1186/s12920-015-0084-2 25889339 PMC4363342 · DOI
  25. [27]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 10.1186/s13059-014-0467-2 25239376 PMC4212091 · DOI
  26. [30]Gutierrez-Arcelus M Ongen H Lappalainen T Montgomery SB Buil A Yurovsky A Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing PLoS Genet 2015 11 10.1371/journal.pgen.1004958 25634236 PMC4310612 · DOI
  27. [31]Kilpinen H Waszak SM Gschwind AR Raghav SK Witwicki RM Orioli A Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription Science 2013 342 744 7 10.1126/science.1242463 24136355 PMC5502466 · DOI · Paper page
  28. [32]Gerstein MB Kundaje A Hariharan M Landt SG Yan K-K Cheng C Architecture of the human regulatory network derived from ENCODE data Nature 2012 489 91 100 10.1038/nature11245 22955619 PMC4154057 · DOI
  29. [33]Rozowsky J Abyzov A Wang J Alves P Raha D Harmanci A AlleleSeq: analysis of allele-specific expression and binding in a network framework Mol Syst Biol 2011 7 522 2 10.1038/msb.2011.54 21811232 PMC3208341 · DOI · Paper page
  30. [34]Wu TD Nacu S Fast and SNP-tolerant detection of complex variants and splicing in short reads Bioinformatics 2010 26 873 81 10.1093/bioinformatics/btq057 20147302 PMC2844994 · DOI · Paper page
  31. [35]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. PMC4626402 26366987 · DOI
  32. [36]Piskol R Ramaswami G Li JB Reliable identification of genomic variants from RNA-seq data Am J Hum Genet 2013 93 641 51 10.1016/j.ajhg.2013.08.008 24075185 PMC3791257 · DOI
  33. [38]Deelen P Zhernakova DV de Haan M van der Sijde M Bonder MJ Karjalainen J Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels Genome Med 2015 7 30 10.1186/s13073-015-0152-4 25954321 PMC4423486 · DOI
  34. [41]Kumasaka N, Knights A, Gaffney D. Fine-mapping cellular QTLs with RASQUAL and ATAC-seq. bioRxiv. 2015. http://dx.doi.org/10.1101/018788. PMC5098600 26656845 · DOI
  35. [42]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 10.1101/gr.119784.110 21873452 PMC3202289 · DOI · Paper page
  36. [43]Zhang R Li X Ramaswami G Smith KS Turecki G Montgomery SB Quantifying RNA allelic ratios by microfluidic multiplex PCR and sequencing Nat Methods 2014 11 51 4 10.1038/nmeth.2736 24270603 PMC3877737 · DOI

Cited by

Sorted by citations

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

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