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ICE / Iterative Correction and Eigenvector decomposition

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A computational pipeline that integrates a strategy to map sequencing reads with a data-driven method for iterative correction of biases, yielding genome-wide maps of relative contact probabilities. Iterative correction leverages the unique pairwise and genome-wide structure of Hi-C data to decompose contact maps into a set of biases and a map of relative contact probabilities between any two genomic loci, achieving equal visibility across all genomic regions.


Allows a comprehensive and reproducible analysis of Hi-C sequencing data. HiC-bench performs complete Hi-C analysis starting with the alignment of reads (fastq files) and ending with the annotation of specific interactions, their visualization and enrichment analysis. Hi-C pipeline integrates Anchoring Topological Domain (TAD) calling HiC-bench using published methods and your own algorithm and performs calculation of boundary scores using your own methods and existing ones. Every pipeline step is followed by summary statistics (when applicable) and visualization of the results. This allows quality control and facilitates troubleshooting. Furthermore, HiC-bench allows parameter exploration and comparison of different methods in a combinatorial fashion. This feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple parameter/tool choices in each step.

HIPPIE / High-throughput Identification Pipeline for Promoter Interacting Enhancer elements

A high-throughput identification pipeline for promoter interacting enhancer element to streamline the workflow from mapping raw Hi-C reads, identifying DNA-DNA interacting fragments with high confidence and quality control, detecting histone modifications and DNase hypersensitive enrichments in putative enhancer elements, to ultimately extracting possible intra- and inter-chromosomal enhancer-target gene relationships.