Chromatine immunoprecipitation-exonuclease data analysis software tools

Detects functional signals in tag profiles from different assays such as histone ChIP-seq, TF ChiP-seq, DNase-seq and FAIRE-seq. DFilter is based on a single receiver operating characteristic – area under the curve (ROC-AUC) optimizing algorithm. This software suits for genomic signals of individual cell types diluted in cellular mixture because the proportion of marginal signals can be mistaken for noise by suboptimal algorithms.

A ChIP-exo protocol for map transcription factor (TF) binding genome-wide. This protocol uses an efficient DNA self-circularization step during library preparation. ChIP-nexus protocol combines the standard ChIP-exo protocol with the library preparation protocol from the iCLIP method for mapping RNA-protein interaction and a randomized barcode to the adapter which enables monitoring of over-amplification.

A bioinformatics tool dedicated to analyze ChIP-exo data: 1) Sequencing depth normalization and nucleotide composition bias correction. 2) Signal consolidation and noise reduction. 3) Single base resolution border detection. 4) Border matching.

Estimates protein–DNA binding energies from in vivo binding profiles. TherMos is a position specific energy matrix (PSEM) estimation method. The software infers an additive binding energy model using least-squares fitting to the ChIP-seq tag profile. TherMos fits to the peak shape in binding regions, rather than to absolute peak height. It can also be used on ChIP-exo data, which provides even higher spatial resolution than ChIP-seq.

A method for non-specifically capturing cross-linked chromatin complexes via protein carboxylate groups that allows the DNA to be subjected to all downstream chemical treatments. PAtCh-Cap is designed to be facile and universally applicable to any of the current and future ChIP-based techniques that perform additional chemical and library preparation steps on bead-bound chromatin.

Allows identification of transcription factor binding sites (TFBSs) from ChIP-seq data. Peakzilla exploits the bimodal distribution of sequence reads characteristic of true transcription factor (TF) binding events, to identify closely adjacent TFBSs. The software is not meant for the identification of broad enriched regions. It is able to find peaks at high resolution.

Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function (package 'skellam') is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Irreproducible discovery rate for overlapping peak-pairs across biological replicates is estimated using the package 'idr'.

Uses ChIP-exo tag enrichment patterns and DNA motifs for allowing users to highlight protein-DNA interaction modes in a single ChIP-exo experiment. ChExMix is a standalone software that is able to (i) locate binding event locations, (ii) find out binding event subtypes, (iii) allocate binding events to subtypes without the need of requiring the presence of distinctive sequence features.

A peak caller specifically designed to leverage the increased accuracy of novel experimental methods for studying transcription factor (TF) binding in vivo in an unbiased manner. Our emphasis is on filtering out false binding events by criteria well motivated by the experimental design while avoiding any unnecessary assumptions about the outcome of the experiment. We apply PeakXus to ChIP-Nexus and ChIP-exo experiments performed both in Homo sapiens and in Drosophila melanogaster cell lines. We show that PeakXus consistently finds more peaks overlapping with a TF-specific recognition sequence than published methods.

A method for the estimation of the protected-region width and peak calling that can be applied to ChIP-nexus as well as ChIP-exo data. Q-nexus creates pseudo-controls from the data with which true signal can be differentiated from pseudo-peaks, which allows us to accurately estimate the width of the protected region. Then, Q-nexus performs an analysis of the qfrag distribution to center candidate peaks and then performs a statistical analysis of the read depth distribution to identify peaks.