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

By Joel S. Rozowsky, Alexej Abyzov, Jing Wang, P. Alves, D. Raha, A. Harmanci, Jing Leng, R. Bjornson, Y. Kong, Naoki Kitabayashi and 4 more. Molecular Systems Biology, 2011

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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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d08a46dc53636b6b9027c1497368ba0c8403f96c

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Citations in the corpus

Piercing the dark matter: bioinformatics of long-range sequencing and mapping (2018, methodology): “After creating a phased VCF file of variants, a variety of downstream processing tools are available, including the AlleleSeq pipeline for studying allele-specific expression or binding 94 .”
Annotation of functional variation in personal genomes using RegulomeDB (2012, neutral): “A diploid map of this individual’s genome has recently been generated, and it has been mapped with allele-specific expression and allele-specific binding (Rozowsky et al. 2011).”
Tools and best practices for data processing in allelic expression analysis (2015, methodology): “More exhaustive but computationally intensive approaches include alignment to personalized genomes [18, 32, 33], or use of a variant-aware aligner, such as GSNAP [34].”
RNA Sequencing and Analysis (2015, neutral): “Conventional workflows to detect ASE involve counting reads containing each allele at heterozygous sites and applying a statistical test, such as the binomial test or the Fisher's exact test (Degner et al. 2009; Rozowsky et al. 2011; Wei and Wang 2013).”
Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes (2012, background): “and Medical Phenotypes”
CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription. (2015, result): “Our haplotype analyses showed significant ChrXmaternal-specificity of RNAPII interactions and gene expression (Figure 4C), consistent with the fact that paternal-X in GM12878 is imprinted (Rozowsky et al., 2011).”