A new method for alleviating sequence-specific biases in DNase-seq

Xu, Siwen; Wang, Ying; Liu, Huan; Chen, Duojiao; Bi, Hongyuan; Feng, Weixing

doi:10.1109/eiis.2017.8298582

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…When designing regSNPs-ASB, we considered several potential issues that could arise from technical artifacts. On the one hand, the intrinsic sequence bias of DNase I and Tn5 enzymes has a striking effect on the average cut profile over a specific TF-binding motif, which is a major limitation of current genomic footprinting methods ( Yardımcı et al, 2014 ; Xu et al, 2017 ). regSNPs-ASB effectively eliminates the impact of sequence preference bias, since this algorithm focuses on identifying the differences in the cut-site distribution between two alleles of a single locus, where the technical variability should be the same.…”

Section: Discussionmentioning

confidence: 99%

regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data

Feng

et al. 2020

Front. Bioeng. Biotechnol.

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Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposasecleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of

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Section: Discussionmentioning

confidence: 99%