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HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data
2014
BMC Bioinformatics
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We have developed a highly integrative pipeline, termed HiChIP for systematic analysis of ChIP-Seq data. ...
Using public ChIP-Seq data we demonstrate that HiChIP is a fast and reliable pipeline for processing large amounts of ChIP-Seq data. ...
Acknowledgements We thank Mona Branstad for editing the manuscript. This work was supported by the Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905. ...
doi:10.1186/1471-2105-15-280
pmid:25128017
pmcid:PMC4152589
fatcat:u3woq66u3vaszcfubk3ezkdt7q
HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP
2021
Nature Communications
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Here, we present HiC-DC+, a software tool for Hi-C/HiChIP interaction calling and differential analysis using an efficient implementation of the HiC-DC statistical framework. ...
HiC-DC+ therefore provides a principled statistical analysis tool to empower genome-wide studies of 3D chromatin architecture and function. ...
Still, such analyses depend on statistical analysis of high-throughput 3C-based experiments and are therefore not entirely orthogonal to HiChIP. ...
doi:10.1038/s41467-021-23749-x
pmid:34099725
pmcid:PMC8184932
fatcat:luifzry3kbc6nnd6q64wn747ei
Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model
[article]
2020
bioRxiv
pre-print
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To enrich for functional enhancer-promoter loops over common structural genomic contacts, we trained LoopPredictor with both H3K27ac and YY1 HiChIP data. ...
Furthermore, to explore the cross-species prediction capability of LoopPredictor, we fed mouse multi-omics features into a model trained on human data and found that the predicted enhancer loops outputs ...
A total of 880 super enhancers were found, which accounted for 5.6% of all enhancers.
775 Super enhancer signal derived from H2K27ac ChIP-seq data.
776 ( 776 C) GO analysis for super enhancer anchors ...
doi:10.1101/2020.04.10.036145
fatcat:w5ob7zs7uzet5a5ibngvzhmyhu
ChIA-PIPE: A fully automated pipeline for comprehensive ChIA-PET data analysis and visualization
2020
Science Advances
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Interpretation of ChIA-PET data requires a robust analytic pipeline. ...
Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, visualization, and analysis. ...
Gabdank (Stanford University) for the helpful discussions; Z. Reifsnyder for artistic improvement of the figures; and the legacy ...
doi:10.1126/sciadv.aay2078
pmid:32832596
pmcid:PMC7439456
fatcat:zo2jnuxap5csnlfypsyapnpare
Recent advancement in Next Generation Sequencing techniques and its computational analysis
[article]
2017
arXiv
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This technology results into huge amount of data, which need to be analysed to conclude valuable information. Specific data analysis algorithms are written for specific task to be performed. ...
Analysis of NGS data unravels important clues in quest for the treatment of various life-threatening diseases; improved crop varieties and other related scientific problems related to human welfare. ...
List of Abbreviations ...
arXiv:1606.05254v2
fatcat:ruu3lkfca5fetkzfg5vywzx2su
Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements
2017
Nature Genetics
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We thank Agilent Technologies for generating oligonucleotide pools for cloning of the CRISPRa gRNAs. We thank the UC Berkeley High-Throughput Screening Facility and Flow Cytometry Facility. ...
Miller for assistance interpreting their published data sets. We thank X. Ji and J. Coller at the Stanford Functional Genomics Facility. ...
We asked whether the integration of reference cell line Hi-C data with primary T cell H3K27ac ChIP-seq data could recapitulate HiChIP EIS in primary T cells. ...
doi:10.1038/ng.3963
pmid:28945252
pmcid:PMC5805393
fatcat:t2gutwaenzdejmuxpoysqxe3cy
ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline
2016
BMC Bioinformatics
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Acknowledgements We thank Bo Qin for thoughtful suggestions on the design of ChiLin, Xueqiu Lin for testing ChiLin and Xikun Duan, Qixuan Wang, Yulin Yang, Chengchen Zhao for preparing initial ChiLin QC ...
The project was funded by National Science Foundation of China grant 31329003, National Institute of Health grants U01 CA180980 and R01 HG4069 and support from the Dana-Farber Cancer Institute. ...
Here, we present ChiLin, an integrated command line quality control and analysis pipeline for ChIP-seq or DNase-seq data. ...
doi:10.1186/s12859-016-1274-4
pmid:27716038
pmcid:PMC5048594
fatcat:4wxxsfeiybesvix3ru3riv3bqu
Chromatin architecture reveals cell type-specific target genes for kidney disease risk variants
2021
BMC Biology
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organization, histone modifications distribution and transcriptome with HiChIP, ChIP-seq and RNA-seq. ...
Conclusions Our results provide a valuable multi-omics resource on the chromatin landscape of human kidney tubule cells and establish a bioinformatic pipeline in dissecting functions of kidney disease-associated ...
Acknowledgements We thank Yinghui Lu for preparation of cell culture medium. ...
doi:10.1186/s12915-021-00977-7
pmid:33627123
fatcat:hgsyjasoujeo7nz75topa6rnz4
Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes
2021
PLoS Computational Biology
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We have addressed this issue through the creation of Biomedical Data Commons (BMDC), a knowledge graph that integrates data from multiple sources into a single queryable format. ...
We develop a pipeline using B-lymphocyte multi-dimensional epigenome and connectome data and deploy BMDC to assess genetic variants in the context of Type 1 Diabetes (T1D). ...
Acknowledgments We thank the Google Data Commons team for developing the underlying infrastructure used to build BMDC. We are also grateful for their help in developing the schema for BMDC. ...
doi:10.1371/journal.pcbi.1009382
pmid:34543288
pmcid:PMC8483327
fatcat:76h55gtz3jghlnfn5lx6j4vj2i
Enhancer connectome in primary human cells reveals target genes of disease-associated DNA elements
[article]
2017
bioRxiv
pre-print
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These data provide a principled means of assigning molecular functions to autoimmune and cardiovascular disease risk variants, linking hundreds of noncoding variants to putative gene targets. ...
The majority of disease-associated enhancers contact genes beyond the nearest gene in the linear genome, leading to a four-fold increase of potential target genes for autoimmune and cardiovascular diseases ...
3D, suggesting EIS bias is in part driven by 3D changes (Supplementary 237 Fig. 10b ).
238 We asked whether the integration of reference cell line Hi-C data with primary T 239 cell H3K27ac ChIP-seq ...
doi:10.1101/178269
fatcat:4qhrtma3b5euhlebcac5eitxre
Molecular and computational approaches to map regulatory elements in 3D chromatin structure
2021
Epigenetics & Chromatin
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Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects. ...
AbstractEpigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. ...
Acknowledgements We thank the lab members for helpful discussions.
Authors' contributions BL and SKR wrote the article. All authors read and approved the final manuscript. ...
doi:10.1186/s13072-021-00390-y
pmid:33741028
pmcid:PMC7980343
fatcat:dgtydqgyhjgxdni34kedgzw3wy
diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data
2017
Bioinformatics
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Your story matters Citation Lareau, Caleb A., and Martin J Aryee. 2017. "diffloop: a computational framework for identifying and analyzing differential DNA loops from sequencing data." ...
To systematically assess changes in DNA looping architecture between samples, we introduce diffloop, an R/ Bioconductor package that provides a suite of functions for the quality control, statistical testing ...
Hnisz and members of the R. Young Lab for their useful discussion and insight.
Funding This work has been supported by the NSF (Graduate Fellowship no. ...
doi:10.1093/bioinformatics/btx623
pmid:29028898
pmcid:PMC5860605
fatcat:p5m3u7v3izfbzjyyonql35lwmm
A Mutation in the Transcription Factor Foxp3 Drives T Helper 2 Effector Function in Regulatory T Cells
2019
Immunity
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Genomic analysis of Treg cells by RNA-sequencing, Foxp3 chromatin immunoprecipitation followed by high-throughput DNA sequencing (ChIP-sequencing), and H3K27ac-HiChIP revealed a specific de-repression ...
These findings identify a direct role for Foxp3 in suppressing Th2-like Treg cells and implicate additional pathways that could be targeted to restrain Th2 trans-differentiated Treg cells. ...
Eckalbar of the UCSF Sandler Center Functional Genomics Core for assistance with RNAseq data; Jennifer Bolen and the UCSF immunohistochemistry core facility, M. Lee, V. ...
doi:10.1016/j.immuni.2018.12.016
pmid:30709738
pmcid:PMC6476426
fatcat:nuttb7pts5cvpk3m7ggwsqcive
Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network
[article]
2018
bioRxiv
pre-print
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Recently, a novel high-throughput experimental approach named HiChIP has been developed and generating compre-hensive data on high-resolution interactions between promoters and distal enhancers. ...
We expect to see a wide spectrum of appli-cations using HiChIP data in deciphering the mechanism of gene regulation. ...
Rui Jiang is a RONG professor at the Institute for Data Science, Tsinghua University. ...
doi:10.1101/341214
fatcat:4ta3ar4purgofmt36dvwqlqfca
Model-based analysis of chromatin interactions from dCas9-Based CAPTURE-3C-seq
2020
PLoS ONE
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Here we present the statistical model and a flexible pipeline, C3S, for analysing CAPTURE-3C-seq or similar experimental data from raw sequencing reads to significantly interacting chromatin loci. ...
Furthermore, it supports the analysis of intra- and inter-chromosomal interactions for different mammalian cell types. ...
Carone for the helpful discussions and critical reading. Software: Yunfei Wang.
Supervision: Jian Xu, Michael Q. Zhang. Validation: Yunfei Wang.
Writing -original draft: Yong Chen. ...
doi:10.1371/journal.pone.0236666
pmid:32735574
fatcat:fkt7hun3p5e3zfh6444hlshj4u
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