Please cite the paper below for the ChIPpeakAnno package.

Lihua J Zhu, Claude Gazin, Nathan D Lawson, Herve Pages, Simon M Lin, David S Lapointe and Michael R Green, ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics. 2010, 11:237

Zhu LJ. Integrative analysis of ChIP-chip and ChIP-seq dataset. Methods Mol Biol. 2013;1067:105-24.

Corresponding BibTeX entries:

  @Article{,
    title = {ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq
      and ChIP-chip data},
    author = {Lihua Zhu and Claude Gazin and Nathan Lawson and Hervé
      Pagès and Simon Lin and David Lapointe and Michael Green},
    journal = {BMC Bioinformatics},
    volume = {11},
    year = {2010},
    number = {1},
    pages = {237},
    url = {http://www.biomedcentral.com/1471-2105/11/237},
    doi = {10.1186/1471-2105-11-237},
    pubmedid = {20459804},
    issn = {1471-2105},
    abstract = {BACKGROUND:Chromatin immunoprecipitation (ChIP)
      followed by high-throughput sequencing (ChIP-seq) or ChIP
      followed by genome tiling array analysis (ChIP-chip) have become
      standard technologies for genome-wide identification of
      DNA-binding protein target sites. A number of algorithms have
      been developed in parallel that allow identification of binding
      sites from ChIP-seq or ChIP-chip datasets and subsequent
      visualization in the University of California Santa Cruz (UCSC)
      Genome Browser as custom annotation tracks. However, summarizing
      these tracks can be a daunting task, particularly if there are a
      large number of binding sites or the binding sites are
      distributed widely across the genome.RESULTS:We have developed
      ChIPpeakAnno as a Bioconductor package within the statistical
      programming environment R to facilitate batch annotation of
      enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis
      of gene expression (CAGE) or any experiments resulting in a large
      number of enriched genomic regions. The binding sites annotated
      with ChIPpeakAnno can be viewed easily as a table, a pie chart or
      plotted in histogram form, i.e., the distribution of distances to
      the nearest genes for each set of peaks. In addition, we have
      implemented functionalities for determining the significance of
      overlap between replicates or binding sites among transcription
      factors within a complex, and for drawing Venn diagrams to
      visualize the extent of the overlap between replicates.
      Furthermore, the package includes functionalities to retrieve
      sequences flanking putative binding sites for PCR amplification,
      cloning, or motif discovery, and to identify Gene Ontology (GO)
      terms associated with adjacent genes.CONCLUSIONS:ChIPpeakAnno
      enables batch annotation of the binding sites identified from
      ChIP-seq, ChIP-chip, CAGE or any technology that results in a
      large number of enriched genomic regions within the statistical
      programming environment R. Allowing users to pass their own
      annotation data such as a different Chromatin immunoprecipitation
      (ChIP) preparation and a dataset from literature, or existing
      annotation packages, such as GenomicFeatures and BSgenome,
      provides flexibility. Tight integration to the biomaRt package
      enables up-to-date annotation retrieval from the BioMart
      database.},
  }

  @InBook{,
    booktitle = {Tilling Arrays},
    title = {Integrative analysis of ChIP-chip and ChIP-seq dataset},
    author = {Lihua Zhu},
    chapter = {4},
    editor = {Tin-Lap Lee and Alfred Chun Shui Luk},
    publisher = {Humana Press},
    journal = {Methods in Molecular Biology},
    volume = {1067},
    year = {2013},
    pages = {-19},
    url =
      {http://link.springer.com/protocol/10.1007%2F978-1-62703-607-8_8},
    doi = {10.1007/978-1-62703-607-8_8},
    pubmedid = {23975789},
    issn = {1064-3745},
    abstract = {Epigenetic regulation and interactions between
      transcription factors and regulatory genomic regions play crucial
      roles in controlling transcriptional regulatory networks that
      drive development, environmental responses, and disease.
      Chromatin immunoprecipitation (ChIP) followed by high-throughput
      sequencing (ChIP-seq) and ChIP followed by genomic tiling
      microarray hybridization (ChIP-chip) are the two of the most
      widely used technologies for genome-wide identification of DNA
      protein interactions and histone modification in vivo. Many
      algorithms and tools have been developed and evaluated that allow
      identification of transcription factor binding sites from
      ChIP-seq or ChIP-chip datasets. However, binding site
      identification is only the first step; the ultimate goal is to
      discover the regulatory network of the transcription factor (TF).
      Here, we present a common workflow for downstream analysis of
      ChIP-chip and ChIP-seq with an emphasis on annotating binding
      sites and integration with gene expression data to identify
      direct and indirect targets of the TF. These tools will help with
      the overall goal of unraveling transcriptional regulatory
      networks using datasets publicly available in GEO.},
  }