NuChart-II: A Graph-Based Approach for Analysis and Interpretation of Hi-C Data
Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expressions and DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing techniques (Hi-C) and molecular dynamics studies show that there is a large correlation between co-localization and co-regulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we present NuChart-II, a software that allows the user to annotate and visualize a list of input genes with information relying on Hi-C data, integrating knowledge data about genomic features that are involved in the chromosome spatial organization. This software works directly with sequenced reads to identify related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. NuChart-II is a highly optimized implementation of a previous prototype developed in R, in which the graph-based representation of Hi-C data was tested. The prototype showed inevitable problems of scalability while working genome-wide on large datasets: particular attention has been paid in order to obtain an efficient parallel implementation of the software. The normalization of Hi-C data has been modified and improved, in order to provide a reliable estimation of proximity likelihood for the genes.
KeywordsSystems Biology Parallel Computing Hi-C data Neighbourhood Graph Chromosome Conformation Capture
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