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Open AccessJournal ArticleDOI

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

TLDR
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.
Abstract
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. 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. 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 BSgenom e, provides flexibility. Tight integration to the biomaRt package enables up-to-date annotation retrieval from the BioMart database.

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Citations
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Journal ArticleDOI

ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization

TL;DR: UNLABELLED ChIPseeker is an R package for annotating ChIP-seq data analysis and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions.
Journal ArticleDOI

Nrf2 suppresses macrophage inflammatory response by blocking proinflammatory cytokine transcription

TL;DR: It is demonstrated that Nrf2 interferes with lipopolysaccharide-induced transcriptional upregulation of proinflammatory cytokines, including IL-6 and IL-1β, and establishes a molecular basis for an NRF2-mediated anti-inflammation approach.
Journal ArticleDOI

DNA methylation in Arabidopsis has a genetic basis and shows evidence of local adaptation

TL;DR: Investigation of DNA methylation variation in Swedish Arabidopsis thaliana accessions grown at two different temperatures finds that accessions from colder regions had higher levels of GBM for a significant fraction of the genome, and this was associated with increased transcription for the genes affected.
References
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Journal ArticleDOI

Gene Ontology: tool for the unification of biology

TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Journal ArticleDOI

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Journal ArticleDOI

Model-based Analysis of ChIP-Seq (MACS)

TL;DR: This work presents Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer, and uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions.
Journal ArticleDOI

Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

TL;DR: The hierarchical model of Lonnstedt and Speed (2002) is developed into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples and the moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom.
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