Ecosyste.ms: Awesome
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awesome-HiC
Awesome list of HiC data analysing tools
https://github.com/ningbioinfo/awesome-HiC
- HiC-Pro - An easy configured pipeline that can process raw data/trimmed data all the way to HiC interaction matrix, it is one of the most popular pipeline with a user-friendly documentation and it plays well with HPC.
- Juicer - Comes with a comprehensive Wiki and provides a couple of other tools for downstream analysis like a browser called JuiceBox for visualisation. The performance is very fast and it plays well with HPC, but it is not designed for data with relative low resolution.
- diffHiC - Well documented user's guide and it has multiple functions for filtering, normalisation and identifying differential HiC interactions from multiple datasets. It is a R package so it can work well with lots of other R packages.
- Homer - Recently updated a new HiC workflow, including alignment, quality control, filtering, generating interaction matrix and normalisation, identifying TADs and loops. It can also generate configure file for circos plots.
- TADbit - A python library that contains multiple functions for analysing HiC data with its own TADs calling algorithm, and it has a well documented tutorial.
- HiTC - Developed by the author of HiC-Pro.
- HiCUP - Provides well documented tutorial including YouTube videos, and it can generate a interactive html report as a result.
- HiCNorm - Included in the HiTC R package.
- Iterative Correction and Eigenvector decomposition (ICE) - Included in the HiC-Pro pipeline.
- JuiceBox - A part of the Juicer toolkit and it can also customise multiple types of data tracks, and it can plot gene loops on heatmaps.
- HiCPlotter - Compatible with the HiC-Pro pipeline and easy to use.
- Sushi - A R package and one of the function is to take common HiC matrix as input to plot a heatmap.
- HiGlass - A web-based browser and can also be run locally within a Docker container.
- FitHiC - Provides a two-step spline-fitting procedure and binomial model to identify significant interactions.
- CHiCAGO - This model only works for capture HiC data, it uses negative binomial random model to model read counts and uses poisson random model to model sequencing errors and artefacts.
- rGMAP - A R package including TADs calling functions and plotting functions for visualisation, and it is able to identify sub-TADs.
- HiTAD - A python library and has a comprehensive documentation for all the functions.
- TopDom - An easy to use R package.
- HiC-QC - QC for preliminary HiC libraries.
- Boost-HiC - HiC patterns detection from low resolution HiC data.
- HiCPlus - Resolution Enhancement of HiC interaction heatmap.
- Comprehensive mapping of long-range interactions reveals folding principles of the human genome - The first paper describing HiC-seq.
- Technical review: a Hitchhiker's guide to chromosome conformation capture. In Plant Chromatin Dynamics - Awesome review of introducing all chromosome conformation capture assays.
- Genome-wide mapping and analysis of chromosome architecture - Awesome review of HiC-seq normalisation methods and some of the TADs calling approaches.
- A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping - Highest resolution of HiC-seq data available, and includes an awesome video explaining the 3D genome.
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