A signal–noise model for significance analysis of ChIP-seq with negative control

Protein-coding mutations in clear cell renal cell carcinoma (ccRCC) have been extensively characterized, frequently involving inactivation of the von Hippel-Lindau ( VHL ) tumor suppressor. Roles for noncoding cis -regulatory aberrations in ccRCC tumorigenesis, however, remain unclear. Analyzing 10 primary tumor/normal pairs and 9 cell lines across 79 chromatin profi les, we observed pervasive enhancer malfunction in ccRCC, with cognate enhancer-target genes associated with tissue-specifi c aspects of malignancy. Superenhancer profi ling identifi ed ZNF395 as a ccRCCspecifi c and VHL-regulated master regulator whose depletion causes near-complete tumor elimination in vitro and in vivo . VHL loss predominantly drives enhancer/superenhancer deregulation more so than promoters, with acquisition of active enhancer marks (H3K27ac, H3K4me1) near ccRCC hallmark genes. Mechanistically, VHL loss stabilizes HIF2α-HIF1β heterodimer binding at enhancers, subsequently recruiting histone acetyltransferase p300 without overtly affecting preexisting promoter-enhancer interactions. Subtype-specifi c driver mutations such as VHL may thus propagate unique pathogenic dependencies in ccRCC by modulating epigenomic landscapes and cancer gene expression. SIGnIFICAnCE:Comprehensive epigenomic profi ling of ccRCC establishes a compendium of somatically altered cis -regulatory elements, uncovering new potential targets including ZNF395, a ccRCC master regulator. Loss of VHL , a ccRCC signature event, causes pervasive enhancer malfunction, with binding of enhancer-centric HIF2α and recruitment of histone acetyltransferase p300 at preexisting lineage-specifi c promoter-enhancer complexes. Cancer Discov; 7(11); 1284-305.

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“…Significant peaks were called using CCAT (P < 0.05; ref. 115). The strength and quality of immunoprecipitation were assessed using CHANCE (116).…”

Section: Histone Chip-seq Analysismentioning

confidence: 99%

VHL Deficiency Drives Enhancer Activation of Oncogenes in Clear Cell Renal Cell Carcinoma

et al. 2017

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“…For each mark, we generated 445 million uniquely mapped Illumina sequencing tags and called peak regions using CCAT ( Supplementary Fig. 2, Supplementary Table 2) 27 . Genomic regions in tumour samples exhibiting an abnormal abundance of ChIP input tags, likely reflecting genomic amplification, were excluded from downstream analyses ( Supplementary Fig.…”

mentioning

confidence: 99%

Nanoscale chromatin profiling of gastric adenocarcinoma reveals cancer-associated cryptic promoters and somatically acquired regulatory elements

et al. 2014

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Chromatin alterations are fundamental hallmarks of cancer. To study chromatin alterations in primary gastric adenocarcinomas, we perform nanoscale chromatin immunoprecipitation sequencing of multiple histone modifications in five gastric cancers and matched normal tissues. We identify hundreds of somatically altered promoters and predicted enhancers. Many cancer-associated promoters localize to genomic sites lacking previously annotated transcription start sites (cryptic promoters), driving expression of nearby genes involved in gastrointestinal cancer, embryonic development and tissue specification. Cancer-associated promoters overlap with embryonic stem cell regions targeted by polycomb repressive complex 2, exhibiting promoter bivalency and DNA methylation loss. We identify somatically acquired elements exhibiting germline allelic biases and non-coding somatic mutations creating new promoters. Our findings demonstrate the feasibility of profiling chromatin from solid tumours with limited tissue to identify regulatory elements, transcriptional patterns and regulatory genetic variants associated with cancer.

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“…Xu et al further divided ChIP-Seq signal into signal and noise, and they developed a signal-noise model for significance analysis of ChIP-Seq with negative control [8]. Compared with Zhang et al's model [4,9], in addition to finding the strong protein-DNA binding sites in the genome, Xu et al's model can also find weak signals which Zhang et al's model cannot find [8].…”

Section: Resultsmentioning

confidence: 99%

“…Compared with Zhang et al's model [4,9], in addition to finding the strong protein-DNA binding sites in the genome, Xu et al's model can also find weak signals which Zhang et al's model cannot find [8].…”

Section: Resultsmentioning

confidence: 99%

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A Model-Based Approach for The Demonstration of Fermat’s Last Theorem

Xu¹

2017

PAMJ

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Abstract:Model-based approaches have many applications in bioinformatics studies. Fermat's last theorem is a theorem in number theory. This paper presented a model-based approach for the demonstration of Fermat's Last Theorem (FLT). Through the establishment of mathematical model, Fermat's Last Theorem (FLT) is converted into a problem for feasible solutions in linear programming. By prompting assumptions, and refuting the assumptions, it is demonstrated that Fermat's Last Theorem is right. The model-based approach for the demonstration of Fermat's Last Theorem (FLT) can assist scientific researchers in biological researches.

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A signal–noise model for significance analysis of ChIP-seq with negative control

Abstract: http://cmb.gis.a-star.edu.sg/ChIPSeq/tools.htm.

Cited by 130 publications

References 19 publications

VHL Deficiency Drives Enhancer Activation of Oncogenes in Clear Cell Renal Cell Carcinoma

VHL Deficiency Drives Enhancer Activation of Oncogenes in Clear Cell Renal Cell Carcinoma

Nanoscale chromatin profiling of gastric adenocarcinoma reveals cancer-associated cryptic promoters and somatically acquired regulatory elements

A Model-Based Approach for The Demonstration of Fermat’s Last Theorem

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