DChIPRep, an R/Bioconductor package for differential enrichment analysis in chromatin studies

被引:2
作者
Chabbert, Christophe D. [1 ,2 ]
Steinmetz, Lars M. [1 ,3 ,4 ]
Klaus, Bernd [1 ]
机构
[1] European Mol Biol Lab, Genome Biol Unit, Heidelberg, Germany
[2] Astra Zeneca, CRUK Cambridge Inst, Oncol iMed, Cambridge, England
[3] Stanford Univ, Stanford Genome Technol Ctr, Palo Alto, CA 94304 USA
[4] Stanford Univ, Dept Genet, Sch Med, Stanford, CA 94305 USA
来源
PEERJ | 2016年 / 4卷
关键词
Bioinformatics; Computational Biology; Genomics; ChiP-seq; Chromatin; Histone-modifications; Differential enrichment; Statistics; CHIP-SEQ; BINDING; VISUALIZATION;
D O I
10.7717/peerj.1981
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The genome-wide study of epigenetic states requires the integrative analysis of histone modification ChIP-seq data. Here, we introduce an easy-to-use analytic framework to compare profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). Our framework is available via the user-friendly R/Bioconductor package DChIPRep. DChIPRep uses biological replicate information as well as chromatin Input data to allow fora rigorous assessment of differential enrichment. DChIPRep is available for download through the Bioconductor project at http://bioconductor.org/packages/DChIPRep. Contact. DChIPRep@gmail.com.
引用
收藏
页数:12
相关论文
共 32 条
  • [1] GeneTrack - a genomic data processing and visualization framework
    Albert, Istvan
    Wachi, Shinichiro
    Jiang, Cizhong
    Pugh, Franklin
    [J]. BIOINFORMATICS, 2008, 24 (10) : 1305 - 1306
  • [2] HTSeq-a Python']Python framework to work with high-throughput sequencing data
    Anders, Simon
    Pyl, Paul Theodor
    Huber, Wolfgang
    [J]. BIOINFORMATICS, 2015, 31 (02) : 166 - 169
  • [3] Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data
    Bailey, Timothy
    Krajewski, Pawel
    Ladunga, Istvan
    Lefebvre, Celine
    Li, Qunhua
    Liu, Tao
    Madrigal, Pedro
    Taslim, Cenny
    Zhang, Jie
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2013, 9 (11)
  • [4] NucleoFinder: a statistical approach for the detection of nucleosome positions
    Becker, Jeremie
    Yau, Christopher
    Hancock, John M.
    Holmes, Christopher C.
    [J]. BIOINFORMATICS, 2013, 29 (06) : 711 - 716
  • [5] Broad-Institute, 2016, PIC TOOLS BY BROAD I
  • [6] A high-throughput ChIP-Seq for large-scale chromatin studies
    Christophe D Chabbert
    Sophie H Adjalley
    Bernd Klaus
    Emilie S Fritsch
    Ishaan Gupta
    Vicent Pelechano
    Lars M Steinmetz
    [J]. Molecular Systems Biology, 11 (1)
  • [7] Dharmalingam G., 2021, soGGi: visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals
  • [8] Identifying ChIP-seq enrichment using MACS
    Feng, Jianxing
    Liu, Tao
    Qin, Bo
    Zhang, Yong
    Liu, Xiaole Shirley
    [J]. NATURE PROTOCOLS, 2012, 7 (09) : 1728 - 1740
  • [9] nucleR: a package for non-parametric nucleosome positioning
    Flores, Oscar
    Orozco, Modesto
    [J]. BIOINFORMATICS, 2011, 27 (15) : 2149 - 2150
  • [10] Ground State Conditions Induce Rapid Reorganization of Core Pluripotency Factor Binding before Global Epigenetic Reprogramming
    Galonska, Christina
    Ziller, Michael J.
    Karnik, Rahul
    Meissner, Alexander
    [J]. CELL STEM CELL, 2015, 17 (04) : 462 - 470