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Facilitates the exploration of high-throughput 3C-based data. HiTC allows users to import and export ‘C’ data, to transform, normalize, annotate and visualize interaction maps. It proposes a powerful and extensible framework for visualizing and exploring high-throughput C data and is able to handle both 5C and Hi-C data and offers new functionalities such as standard import, data transformation and integrative visualization methods. The HiTC package is aimed at biologists interested in investigating their data and at biostatisticians involved in the development of new statistical methods which can be applied to C data.
A two-step algorithm and assess its accuracy using both in silico data and human genome-wide 3C (Hi-C) data. This algorithm avoids convergence issues, accommodates sparse and noisy contact maps, and is orders of magnitude faster than existing methods. ShRec3D involves no ad hoc constraints or tunable parameters and is free from convergence issues and misleading transient outcomes. Its speed makes it applicable to both 3C or carbon-copy 3C (5C) data sets, which typically involve tens of loci, and high-resolution Hi-C data sets, comprising sparse contacts between hundreds of thousands of points. Its accurate reconstruction of average distances between genomic loci and visualization of a consensus structure enable a meaningful use of cell-population Hi-C data, especially when extended into 3D genome browsers.
A set of tools for handling HiC and 5C data. HiFive provides efficient data handling and a variety of normalization approaches for easy, fast analysis and method comparison. Integration of MPI-based parallelization allows scalability and rapid processing time. In addition to single-command analysis of an entire experiment from mapped reads to interaction values, HiFive has been integrated into the open-source, web-based platform Galaxy to connect users with computational resources and a graphical interface.
A probabilistic model linking 5C/Hi-C data to physical distances and describe a Markov chain Monte Carlo (MCMC) approach to generate a representative sample from the posterior distribution over structures from interaction frequency data. Structures produced from parallel MCMC runs on the same dataset demonstrate that MCMC5C mixes quickly and is able to sample from the posterior distribution of structures and find subclasses of structures. Structural properties (base looping, condensation, and local density) were defined and their distribution measured across the ensembles of structures generated. We believe that tools like MCMC5C are essential for the reliable analysis of data from the 3C-derived techniques such as 5C and Hi-C. By integrating complex, high-dimensional and noisy datasets into an easy to interpret ensemble of three-dimensional conformations, MCMC5C allows researchers to reliably interpret the result of their assay and contrast conformations under different conditions.
Provides an online 5C tool for the rapid design of 5C primers. my5C allows complete control over extremely complex 5C design schemes. This tool is composed of two modules. The “my5C.primers” module is used to design 5C primers for restriction fragments throughout user-defined genomic regions. In the second module, “my5C.heatmap”, datasets are visualized as two-dimensional heatmaps where each datapoint corresponds to an interaction frequency between two loci. Users can download any data displayed as a heatmap as tables or as lists of pairwise interactions. Furthermore, to ensure confidentiality all data are password protected and users can opt not to store data on the my5C server.
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