Chromatin interaction analysis with updated ChIA-PET Tool (V3)

Li, Guoliang; Sun, Tongkai; Chang, Huidan; Cai, Liuyang; Pan, Hong; Zhou, Qiangwei

doi:10.1101/627257

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…The enhancers used were typically hundreds of base pairs, while the chromatin interaction anchors were much larger in size. For example, Hi-C anchors are normally 5 to 100 kb long (while only in rare case with extremely deep sequencing are the anchors down to 1 kb size) [ 29 , 30 ], and ChIA-PET is normally several kilobase pairs long [ 31 , 32 ]. The resolution discrepancy could lead to the introduction of a lot of noises to the training datasets (Fig.…”

Section: Resultsmentioning

confidence: 99%

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

et al. 2021

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Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

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Section: Resultsmentioning

confidence: 99%

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

et al. 2021

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The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

Hernández‐Lemus

Reyes-Gopar

Espinal-Enríquez

et al. 2019

Genes

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Cancer is a complex disease at many different levels. The molecular phenomenology of cancer is also quite rich. The mutational and genomic origins of cancer and their downstream effects on processes such as the reprogramming of the gene regulatory control and the molecular pathways depending on such control have been recognized as central to the characterization of the disease. More important though is the understanding of their causes, prognosis, and therapeutics. There is a multitude of factors associated with anomalous control of gene expression in cancer. Many of these factors are now amenable to be studied comprehensively by means of experiments based on diverse omic technologies. However, characterizing each dimension of the phenomenon individually has proven to fall short in presenting a clear picture of expression regulation as a whole. In this review article, we discuss some of the more relevant factors affecting gene expression control both, under normal conditions and in tumor settings. We describe the different omic approaches that we can use as well as the computational genomic analysis needed to track down these factors. Then we present theoretical and computational frameworks developed to integrate the amount of diverse information provided by such single-omic analyses. We contextualize this within a systems biology-based multi-omic regulation setting, aimed at better understanding the complex interplay of gene expression deregulation in cancer.

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The DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis

Pan

Jiang

et al. 2020

Genes

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It is becoming increasingly important to understand the mechanism of regulatory elements on target genes in long-range genomic distance. 3C (chromosome conformation capture) and its derived methods are now widely applied to investigate three-dimensional (3D) genome organizations and gene regulation. Digestion-ligation-only Hi-C (DLO Hi-C) is a new technology with high efficiency and cost-effectiveness for whole-genome chromosome conformation capture. Here, we introduce the DLO Hi-C tool, a flexible and versatile pipeline for processing DLO Hi-C data from raw sequencing reads to normalized contact maps and for providing quality controls for different steps. It includes more efficient iterative mapping and linker filtering. We applied the DLO Hi-C tool to different DLO Hi-C datasets and demonstrated its ability in processing large data with multithreading. The DLO Hi-C tool is suitable for processing DLO Hi-C and in situ DLO Hi-C datasets. It is convenient and efficient for DLO Hi-C data processing.

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Chromatin interaction analysis with updated ChIA-PET Tool (V3)

Cited by 3 publications

References 25 publications

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

The Many Faces of Gene Regulation in Cancer: A Computational Oncogenomics Outlook

The DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis

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