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ChIPseqR

ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well.

Installation

Both the development and release version of this R package are available through Bioconductor. Use the BiocInstaller to install the package and its dependencies from within R.

source("http://bioconductor.org/biocLite.R")
biocLite("ChIPseqR")

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Version

Version

1.26.0

License

GPL (>= 2)

Maintainer

Peter Humburg

Last Published

February 15th, 2017

Functions in ChIPseqR (1.26.0)

compress-BindScore

Compress BindScore Objects

RLEBindScore-class

Run-length Encoded Binding Site Scores

Score potential protein binding sites

BindScore-class

Class "BindScore"

Calculate cross-correlation between read counts

plot,ReadCounts,missing-method

Diagnostic Plots for Read Counts

ChIPseqR-package

Identifying Protein Binding Sites in High-Throughput Sequencing Data

Plot read counts within a genomic region

Diagnostic Plots for Binding Site Scores

Estimate length of binding and support region

decompress-methods

Methods for Function decompress in Package `ChIPseqR'

callBindingSites-methods

Predict protein binding sites from high-throughput sequencing data

Extract Read Count and Binding Site Score Representations

Read counts relative to annotated features

Determine significance threshold for binding site scores

ReadCounts-class

Class "ReadCounts"

RLEReadCounts-class

Run-length Encoded Read Counts

Strand specific read counts

Plot compact representation of read counts on a chromosome

Convert genome coordinates into GFF format

Identify peaks above a given threshold

Internal and undocumented functions

compress-ReadCounts

Compress ReadCount Objects

exportBindSequence

Export sequence of predicted binding sites

Access slots of S4 classes

compress-methods

Methods for Function compress in Package `ChIPseqR'

Predict nucleosome positions from high-throughput sequencing data

Summarize read counts in a sliding window