AlleleSeq: analysis of allele-specific expression and binding in a network framework.
Open accessAbstract
To study allele-specific expression (ASE) and binding (ASB), that is, differences between the maternally and paternally derived alleles, we have developed a computational pipeline (AlleleSeq). Our pipeline initially constructs a diploid personal genome sequence (and corresponding personalized gene annotation) using genomic sequence variants (SNPs, indels, and structural variants), and then identifies allele-specific events with significant differences in the number of mapped reads between maternal and paternal alleles. There are many technical challenges in the construction and alignment of reads to a personal diploid genome sequence that we address, for example, bias of reads mapping to the reference allele. We have applied AlleleSeq to variation data for NA12878 from the 1000 Genomes Project as well as matched, deeply sequenced RNA-Seq and ChIP-Seq data sets generated for this purpose. In addition to observing fairly widespread allele-specific behavior within individual functional genomic data sets (including results consistent with X-chromosome inactivation), we can study the interaction between ASE and ASB. Furthermore, we investigate the coordination between ASE and ASB from multiple transcription factors events using a regulatory network framework. Correlation analyses and network motifs show mostly coordinated ASB and ASE.
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Current approaches to allele-specific expression discard allelic multireads that map to both parents and fail to account for reads that are simultaneously genomic and allelic multireads.
“These methods discard allelic multireads that map to both parents and fail to account for reads that are simultaneously genomic and allelic multireads.”
From RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations (2014) · cites this paper as Rozowsky et al. 2011
The Materials and methods section (page not available) says AlleleSeq merges reads mapped to both parental genomes and, when the mappings tie, chooses one at random; it does not discard those reads. The section requires unique mappings within each genome, but does not explicitly discuss reads that are simultaneously genomic and allelic multireads. (LLM, conf=0.86)
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Experimental — accuracy not yet measuredSimulated 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].”
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 [18]
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 measuredAligning 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”
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 [18]
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- Authors
- 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.
- Affiliations
- Yale University (US)
- Weill Cornell Medicine (US)
- Stanford Medicine (US)
- Corresponding author
- Rozowsky, Joel
- Gerstein, Mark
- ORCID
- Venue
- Molecular Systems Biology
- Published
- 2011-08-02
- Volume
- 7
- Issue
- 1
- Pages
- 522-522
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC3208341
- OpenAlex
- W2134199796
- Cited by
- 316
- References
- 5
- Citations by year
- 2026: 6 · 2025: 15 · 2024: 23 · 2023: 13 · 2022: 11 · 2021: 27 · 2020: 22 · 2019: 19 · 2018: 17 · 2017: 21 · 2016: 29 · 2015: 32 · 2014: 30 · 2013: 29 · 2012: 21
- FWCI
- 10.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute on Drug Abuse (P30DA018343)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
- Keywords
- allele-specific expression, allele-specific binding, genetic variants, X chromosome inactivation, transcription factor binding, RNA-seq, ChIP-seq, NA12878, 1000 Genomes Project
- MeSH terms
- Alleles, Cell Line, Chromosome Mapping, DNA-Binding Proteins, Gene Expression Regulation, Humans, Transcription Factors, Genome, Human, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, Databases, Genetic, Chromosomes, Human, X, Chromosomes, Human, Y, Gene Regulatory Networks, Molecular Sequence Annotation
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