BayesPeak - Bayesian Analysis of ChIP-seq Data, This package is an implementation of the BayesPeak algorithm for peak-calling in ChIP-seq data.
ChIPpeakAnno – Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges.
Chipseq – A package for analyzing chipseq data. Tools for helping process short read data for chipseq experiments
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.
ChIPsim – A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments.
DESeq – Differential gene expression analysis based on the negative binomial distribution. Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution
DiffBind – Differential Binding Analysis of ChIP-Seq peak data. Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.
edgeR – Empirical analysis of digital gene expression data in R
iASeq – iASeq: integrating multiple sequencing datasets for detecting allele-specific events
MEDIPS – MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-Seq). Nevertheless, functionalities like the quality controls may be applied to other types of sequencing data (e.g. ChIP-Seq). MEDIPS adresses several aspects in the context of MeDIP-Seq data analysis.
Mosaics – MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) This package provides functions for fitting MOSAiCS, a statistical framework to analyze one-sample or two-sample ChIP-seq data.
NarrowPeaks – Functional Principal Component Analysis to Narrow Down Transcription Factor Binding Site Candidates. The package processes data in wiggle track format (WIG) commonly produced by several ChIP-seq data analysis tools by applying functional version of principal component analysis (FPCA) over a set of selected candidate enriched regions. This is done in order to shorten the genomic locations accounting for a given proportion of variation among the enrichment-score profiles. It allows the user to discriminate between binding regions in close proximity to each other and to narrow down the length of the putative transcription factor binding sites while preserving the information present in the variability of the dataset and capturing major sources of variation.
NucleR – Nucleosome positioning for Tiling Arrays and Next Generation Sequencing Experiments package for R
PICS – Probabilistic inference of ChIP-seq. Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach.
Incoming search terms:
- chip-seq data
- chip sequencing
- chip seq
- ChIP-Seq data analysis
- chip-seq motif
- MeDIP peak calling
- chip-seq peak calling
- Galaxy find motif for ChIP-seq
- r package to analyze chipseq data