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MACS / Model-based Analysis for ChIP-Seq

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A software to analyze data generated by short read sequencers. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. It offers four important utilities for predicting protein-DNA interaction sites from ChIP-Seq. First, it improves the spatial resolution of the predicted sites by empirically modeling the distance d and shifting tags by d/2. Second, MACS uses a dynamic λ local parameter to capture local biases in the genome and improves the robustness and specificity of the prediction. Third, MACS can be applied to ChIP-Seq experiments without controls, and to those with controls with improved performance. Last but not least, MACS is easy to use and provides detailed information for each peak.

HOMER / Hypergeometric Optimization of Motif EnRichment

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Performs peak finding and downstream data analysis for next-generation sequencing analysis. HOMER affords several tools and methods to make use of ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and other types of functional genomics sequencing data sets. This software offers support to UCSC visualization, peaks annotation, quantification of transcripts and repeats or differential features, enrichment and expression.

SICER / spatial clustering approach for the identification of ChIP-enriched regions

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Recognizes ChIP-enriched regions in histone modification data. SCIER is based on a mathematical theory for the score distribution in a genomic background model of random reads. It finds spatial clusters, large and small, unlikely to appear by chance. This tool is able to deal with the enrichment context of a local window in determining its significance. It assists users to reduce the sampling fluctuations in the control library.


Examines epigenomic and transcriptomic next generation sequencing (NGS) data. Octopus-toolkit can be used for antibody- or enzyme-mediated experiments and studies for the quantification of gene expression. It can accelerate the data mining of public epigenomic and transcriptomic NGS data for basic biomedical research. This tool provides a private and a public mode: one to process the user’s own data, and the other to analyze public NGS data by retrieving raw files from the GEO database.


A probabilistic method for jointly analyzing multiple ChIP-seq datasets. jMOSAiCS (joint model-based one- and two-sample analysis and inference for ChIP-seq) is a probabilistic model for integrating multiple ChIP-seq datasets to identify combinatorial patterns of enrichment. The key components of jMOSAiCS are base models for the sequencing reads of each individual ChIP-seq experiment and a model that governs the relationship of enrichment among different samples. It facilitates joint analysis of multiple ChIP-seq datasets for both identifying enrichment patterns of a single TF across multiple conditions and characterizing enrichment patterns of multiple epigenomic marks in one or more conditions.


Provides a flexible implementation of the BayesPeak algorithm and is compatible with downstream BioConductor packages. The BayesPeak package introduces a new method for summarizing posterior probability output, along with methods for handling overfitting and support for parallel processing. It provides a Bayesian analysis, with advantages including allowance for overdispersion in read counts and a competitive genome-wide specificity and sensitivity. By anticipating peak structure, BayesPeak does not call peaks based on sheer numbers of reads without appropriate read formation.

EDD / Enriched Domain Detector

Allows discovery of broad genomic enrichment areas from ChIP-seq data. EDD is a genomic domain caller designed to detect megabase-size domains. The software enables quantitative analysis of ChIP-seq data for proteins widely distributed and with low-level enrichment on chromatin. It can discover genomic domains enriched in lamin A (LMNA) using new ChIP-seq data for LMNA. The main advantages of EDD are its sensitivity to the size of domains rather than the strength of enrichment at a particular site and its robustness against local variations.

CSAR / ChIP-Seq Analysis in R

An R package for the statistical analysis of ChIP-seq experiments. CSAR calculates single-nucleotide read-enrichment values, taking the average size of DNA fragments subjected to sequencing into account. After normalization, sample and control are compared using a test based on the ratio test or the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutations. Computational efficiency is achieved by implementing the most time-consuming functions in C++ and integrating these in the R package.

PICS / Probabilistic Inference for ChIP-Seq

A package for identifying regions bound by transcription factors from aligned reads. PICS identifies binding event locations by modeling local concentrations of directional reads, and uses DNA fragment length prior information to discriminate closely adjacent binding events via a Bayesian hierarchical t-mixture model. It uses precalculated, whole-genome read mappability profiles and a truncated t-distribution to adjust binding event models for reads that are missing due to local genome repetitiveness. PICS estimates uncertainties in model parameters that can be used to define confidence regions on binding event locations and to filter estimates. Finally, it calculates a per-event enrichment score relative to a control sample, and can use a control sample to estimate a false discovery rate. Because it is based on mixture models and accounts for missing reads, PICS is computationally intensive.

DROMPA / DRaw and Observe Multiple enrichment Profiles and Annotation

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Allows peak calling, visualization, quality check and Polymerase Chain Reaction (PCR) bias filtering of ChIP-seq data. DROMPA calls peaks by comparing the read distribution of the ChIP sample with that of the corresponding input sample. The software identifies peaks as bar graph protein-binding sites when the peaks are sharp (approximately 1 kbp) and when they are broad (approximately 1 Mbp). It can accept multiple mapped reads (reads mapped on multiple loci of the reference genome).


A peak-caller package that works equally well on punctate and broad sites. PeakRanger can resolve closely-spaced peaks, has excellent performance, and is easily customized. It can be run in a parallel cloud computing environment to obtain extremely high performance on very large data sets. PeakRanger has above-average spatial accuracy in terms of identifying the precise location of binding events. PeakRanger also has excellent sensitivity and specificity in all benchmarks evaluated.


Computes the annotation error of peak calls. PeakError allows, after constructing a database of annotated regions that represent your visual interpretation of the peak locations in a ChIP-seq experiment, to compute the error of a peak calling model. PeakError proposes to create labels that encode an experienced scientist’s judgment about which regions contain or do not contain peaks. The labels can then be used as a gold standard to quantitatively train and test peak detection algorithms on specific data sets.

BRACIL / Binding Resolution Amplifier and Cooperative Interaction Locator

Improves binding site resolution and predicts cooperative interactions. BRACIL can study physical properties of DNA shearing from the ChIP-seq coverage. It incorporates motif discovery and is able to detect multiple sites in an enriched region with single nucleotide resolution, high sensitivity, and high specificity. The tool improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate <11% for a set of 47 experimentally validated prokaryotic sites.


A peak-calling algorithm. OccuPeak uses the abundant low frequency tags present in each ChIP-seq dataset to model the background, thereby avoiding the need for additional datasets. Analysis of the performance of OccuPeak showed robust model parameters. Its measure of peak significance, the excess ratio, is only dependent on the tag density of a peak and the global noise levels. Compared to the commonly used peak-calling applications MACS and CisGenome, OccuPeak had the highest sensitivity in an enhancer identification benchmark test, and performed similar in an overlap tests of transcription factor occupation with DNase I hypersensitive sites and H3K27ac sites. Moreover, peaks called by OccuPeak were significantly enriched with cardiac disease-associated SNPs.


A ChIP-seq discretizer with built-in quality control. Zerone is powered by a hidden Markov model with zero-inflated negative multinomial emissions, which allows it to merge several replicates into a single discretized profile. To identify low quality or irreproducible data, we trained a support vector machine and integrated it as part of the discretization process. The result is a classifier reaching 95% accuracy in detecting low quality profiles. We also introduce a graphical representation to compare discretization quality and we show that Zerone achieves outstanding accuracy. Zerone is designed for large volume pipelines aiming to combine many ChIP-seq profiles with little human intervention. To this end, it is compatible with the standard BED, SAM/BAM, and GEM formats, it produces congruent window-based outputs, and it can process hundreds of experiments per day on average hardware.

Pasha / Preprocessing of Aligned Sequences from HTS Analyses

An R package designed for processing aligned reads from chromatin-oriented high-throughput sequencing experiments. Pasha allows easy manipulation of aligned reads from short-read sequencing technologies (ChIP-seq, FAIRE-seq, MNase-seq...) and offers innovative approaches such as ChIP-seq reads elongation, nucleosome midpoint piling strategy for positioning analyses, or the ability to subset paired-end reads by groups of insert size that can contain biologically relevant information. It integrates several options allowing a seamless adaptation to various experimental setups. Additionally, the R package provides several tools for programmers that need to develop or integrate additional features.


A general methodological framework to rigorously combine the evidence of enriched regions in ChIP-seq replicates, with the option to set a significance threshold on the repeated evidence and a minimum number of samples bearing this evidence. Given a set of peaks from (biological or technical) replicates, the method combines the p-values of overlapping enriched regions: users can choose a threshold on the combined significance of overlapping peaks and set a minimum number of replicates where the overlapping peaks should be present. The method allows the "rescue" of weak peaks occuring in more than one replicate and outputs a new set of enriched regions for each replicate.


Uses a hidden Markov model, to identify both enriched peaks and domains simultaneously. It is unique in that it does not need to be tuned to one type of enrichment prior to analysis and does not make assumptions about how reads should be distributed around transcription factor binding sites. The output from the programs was compared to qPCR-validated enriched and depleted sites, predicted transcription factor binding sites, and highly-transcribed gene bodies. With every method, hiddenDomains, performed as well as, if not better than algorithms dedicated to a specific type of analysis. hiddenDomains performs as well as the best domain and peak calling algorithms, making it ideal for analyzing ChIP-seq datasets, especially those that contain a mixture of peaks and domains.

BiSA / Binding Sites Analyser

Identifies genes located near binding regions of interest, genomic features near a gene or locus of interest and statistical significance of overlapping regions can also be reported. BiSA is a transcription factor DNA binding site analyser software for archiving of binding regions and easy identification of overlap with or proximity to other regions of interest. It is capable of reporting overlapping regions that share common base pairs; regions that are nearby; regions that are not overlapping; and average region sizes.