AlleleSeq: analysis of allele‐specific expression and binding in a network framework

Rozowsky, Joel; Abyzov, Alexej; Wang, Jingjing; Alves, Pedro; Raha, Debasish; Harmanci, Arif; Leng, Jing; Bjornson, Robert; Kong, Yong; Kitabayashi, Naoki; Bhardwaj, Nitin; Rubin, Mark A.; Snyder, M; Gerstein, Mark

doi:10.1038/msb.2011.54

Cited by 308 publications

(366 citation statements)

References 41 publications

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“…ChIP-seq provides new opportunities to study allele-specific binding (ASB) and HM (22)(23)(24). ASB detection often suffers from low statistical power because only reads mapped to heterozygote SNPs contain allelic information.…”

Section: Example I: Analysis Of Differential Chromatin Patterns At Tfmentioning

confidence: 99%

“…Using the modified dPCA, we analyzed ASB in 20 ChIP-seq datasets (44 samples) from the ENCODE GM12878 cells (SI Appendix, Table S1). Genotypes for a collection of 5,504 heterozygote SNPs were obtained from a study by Rozowsky et al (23). After removing various read mapping biases (22,24) and applying dPCA to 2,584 bound SNPs (SI Appendix, Text S1 and Fig.…”

Section: Example I: Analysis Of Differential Chromatin Patterns At Tfmentioning

confidence: 99%

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Differential principal component analysis of ChIP-seq

Wang

Yang

2013

Proc. Natl. Acad. Sci. U.S.A.

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We propose differential principal component analysis (dPCA) for analyzing multiple ChIP- sequencing datasets to identify differential protein–DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein–DNA interactions.

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Section: Example I: Analysis Of Differential Chromatin Patterns At Tfmentioning

confidence: 99%

Section: Example I: Analysis Of Differential Chromatin Patterns At Tfmentioning

confidence: 99%

Differential principal component analysis of ChIP-seq

Wang

Yang

2013

Proc. Natl. Acad. Sci. U.S.A.

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“…We obtained the P. maniculatus genome sequence and annotation from the Peromyscus Genome Project (Pman_1.0, GenBank accession: GCA_000500345.1). We created a P. polionotus genome and annotation by incorporating SNPs and indels into the P. maniculatus Pman_1.0 reference using the AlleleSeq package 51 and the UCSC liftover tool. Briefly, P. polionotus gDNA reads (Genbank BioProject accession: PRJNA53593) were aligned to the Pman_1.0 reference using Stampy 38 v1.0.21 with a substitution rate=0.04.…”

Section: Methodsmentioning

confidence: 99%

The genetic basis of parental care evolution in monogamous mice

Bendesky

Kwon

Lassance

et al. 2017

Nature

234

202

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Summary Parental care is essential for the survival of mammals, yet the mechanisms underlying its evolution remain largely unknown. Here we show that two sister species of mice, Peromyscus polionotus and P. maniculatus, have large and heritable differences in parental behaviour. Using quantitative genetics, we identify 12 genomic regions that affect parental care, eight of which have sex-specific effects, suggesting that parental care can evolve independently in males and females. Furthermore, some regions affect parental care broadly, whereas others affect specific behaviours, such as nest building. Of the genes linked to differences in nest-building behaviour, vasopressin is differentially expressed in the hypothalamus of the two species, with increased levels associated with less nest building. Using pharmacology in Peromyscus and chemogenetics in Mus, we show that vasopressin inhibits nest building but not other parental behaviours. Together, our results indicate that variation in an ancient neuropeptide contributes to interspecific differences in parental care.

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“…Similar problems may arise in alignment of ChIP-Seq reads and chromatin state QTL mapping. In RNA-seq analysis, accounting for genotypedependent mapping bias has been important for obtaining more accurate and reliable results from analysis of allelic specific expression (ASE) [8,[11][12][13][14], allelic specific binding (ASB) [12], and DNaseI sensitivity QTLs [15]; nevertheless, similar analyses have not been done for eQTLs.…”

Section: Introductionmentioning

confidence: 99%

Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies

et al. 2014

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Background: RNA sequencing (RNA-seq) is the current gold-standard method to quantify gene expression for expression quantitative trait locus (eQTL) studies. However, a potential caveat in these studies is that RNA-seq reads carrying the non-reference allele of variant loci can have lower probability to map correctly to the reference genome, which could bias gene quantifications and cause false positive eQTL associations. In this study, we analyze the effect of this allelic mapping bias in eQTL discovery.

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AlleleSeq: analysis of allele‐specific expression and binding in a network framework

Abstract: A computational pipeline for constructing a personal diploid genome and determining sites of allele-specific activity is developed. Using a regulatory network framework, allele-specific binding and expression are found to be significantly coordinated across the genome.

Cited by 308 publications

References 41 publications

Differential principal component analysis of ChIP-seq

Differential principal component analysis of ChIP-seq

The genetic basis of parental care evolution in monogamous mice

Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies

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