Chromatin interaction analysis by paired-end tag software tools
A number of high-throughput methods based on nuclear proximity ligation have been developed to detect genome-wide chromatin interactions, including high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). While Hi-C was developed to capture all chromatin interactions and is effective for mapping large-scale structures such as chromatin compartments and topologically associated domains, ChIA-PET is emerging as an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution.
Allows users to generate 3D structures from 3C data. 3D-GNOME provides a web-based, interactive 3D viewer to visualize and analyze the resulting 3D structure. It includes options for users to upload genomic annotation data to overlay on the structure. In addition to the 3D structure, 3D-GNOME provides a variety of other analysis tools, including 1D arc representations and 2D heatmap representations of the data.
An R package based on a non-parametric Bayesian approach, to infer the set of the most probable protein complexes involved in maintaining chromatin interactions and the regions that they may control, making it a valuable downstream analysis tool in chromatin conformation studies. 3CPET does so by combining data from ChIA-PET, transcription factor binding sites, and protein interactions.
A statistical test for detection of significant interactions between genomic elements in ChIA-PET datasets, applying the non-central hypergeometric (NCHG) distribution. Unlike previous methods, our statistical model incorporates genomic distance in addition to marginal sums, in order to avoid over-estimating the significance of short interactions.
An easy-to-use and complete analysis pipeline to process both bridge-linker and half-linker ChIA-PET data from raw sequencing reads to significant chromatin loop calls. ChIA-PET can detect chromatin loops with a significantly higher sensitivity and reproducibility than the existing pipeline at the same false discovery rate. Mismatches are allowed at the linker trimming step, which rescues a large portion of pair-end tags (PETs). Multi-threading is supported to speed up the processing time. Quality control measures are supported at different steps of the ChIA-PET analysis. When phased genotype data are available, ChIA-PET is also able to detect allele-specific chromatin loops.
A complete ChIA-PET data analysis pipeline that provides statistical confidence estimates for interactions and corrects for major sources of bias including differential peak enrichment and genomic proximity. Comparison to the existing software packages, ChIA-PET Tool and ChiaSig, revealed that Mango interactions exhibit much better agreement with high-resolution Hi-C data. Importantly Mango executes all steps required for processing ChIA-PET data sets whereas ChiaSig only completes 20% of the required steps.
An easy-to-use R package to detect chromatin interactions from ChIA-PET sequencing data. By applying a Bayesian mixture model to systematically remove random ligation and random collision noise, MICC could identify chromatin interactions with a significantly higher sensitivity than existing methods at the same false discovery rate.
Identifies differential DNA looping between samples. diffloop provides a suite of tools to uncover differential loops in DNA with statistical rigor and integrate other bioinformatics data. It aims to subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments or other assays that infer chromatin loops. The analyses show how differences in chromatin accessibility, DNA methylation, histone modifications, and cohesin localization correlate with differences in DNA looping, and how looping relates to differences in gene expression and cellular phenotypes.
Displays 3D structure of genome with diverse genomic features. Delta is a web based 3D genome visualization platform. This tool can be useful for researchers to infer novel and valid hypothesis via visually integrating multiple datasets. It can also be used to understand the principles behind the nuclear architecture from various perspectives.
Predicts whether a pair of convergent CCCTC-binding factor (CTCF) motifs can form a chromatin loop. CTCF-MP is a machine learning algorithm based on word2vec and boosted trees. It permits to evaluate the contributions of sequence-based features already encoded in the genome. It also offers important insights in the sequence-based features underlying loop formation between a pair of CTCF motifs.
Offers functions for the analysis of chromatin interactions using MC_DIST model (one sample problem), Two-Step model and One-Step model. MDM provides: (i) a function MCDIST to detect ture chromatin interactions using a dataset obtained from a chromatin looping experiment, (ii) a function MDTS to peoform the second step of the Two-Step model for detecting chromatin interactions with different intensities in two samples, and (iii) a function MDOS to peoform the One-Step model for detecting chromatin interactions with different intensities in two samples.
A web-based application for visualizing, annotating, and querying chromatin interactions derived from technologies such as ChIA-PET or HiC. QuIN enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.
An R/Bioconductor package for the manipulation, annotation and visualisation of various types of chromatin interaction data, e.g. Hi-C, ChIA-PET. GenomicInteractions allows the easy annotation and summarisation of large genome-wide datasets at both the level of individual interactions and sets of genomic features, and provides several different methods for interrogating and visualising this type of data.
Allows users to process and manage the Paired End diTag (PET) data generated from GIS-PET and ChIP-PET experiments. Pet-Tool is a web application is composed of four components : (i) ProjectManager for handling the data from experiment, (ii) Extractor for uploading the sequence files and distinguish PET sequences, (iii) Examiner provides functions for analyze data and (iv) Mapper for querying the locations of the studied PET sequences.
Uses for interacting with the PGL file standard for paired-genomic loci. Pgltools is a cross platform, pypy compatible python package available both as an easy-to-use UNIX package, and as a python module, for integration into pipelines of paired-genomic-loci analyses. This software performs genomic arithmetic on PGL files such as comparing, merging, and intersecting two sets of paired-genomic-loci, as well as integrates BED files with PGL files.
Serves for discovering protein binding sites (PBSs) using Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET) data. MACPET uses both tags of each self-ligated PET and estimates the PBSs using two-dimensional parametric mixture models. This tool runs a ChIA-PET data analysis including stages for linker trimming, mapping to the reference genome, or PET classification.
Serves for analyzing ChIA-PET data that integrates chromatin interaction discovery with the identification of interaction anchor points. SPROUT allows analysis by modeling the empirical distribution of read positions around interaction anchors. This tool includes features for determining the positions of anchors and assigning pairs of reads to anchors. Moreover, it models read-pair data with a mixture over distributions describing the generation of self-ligation pairs and inter-ligation pairs.
Allows users to detect chromatin interactions from chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) data. CID provides a method which clusters proximal paired-end tags (PETs) into interactions leaning on a density-based clustering technique. This application aims to be more flexible than existing methods by using an approach based on anchors resolving. Besides, it can also be employed for the identification of chromatin interactions from HiChIP data.
Assists in making genome-wide predictions of CCCTC-binding factor (CTCF)-mediated loops. Lollipop is a machine-learning framework based on random forests classifier. It accounts for the complexity of loop structures by integrating genomic and epigenomic features. Moreover, this approach reveals novel determinants of CTCF-mediated chromatin wiring, such as gene expression within the loop.
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