NuChart-II: The road to a fast and scalable tool for Hi-C data analysis

Abstract

Recent advances in molecular biology and bioinformatic techniques have brought about an explosion of information about the spatial organisation of the DNA in the nucleus of a cell. High-throughput molecular biology techniques provide a genome-wide capture of the spatial organisation of chromosomes at unprecedented scales, which permit one to identify physical interactions between genetic elements located throughout a genome. This important information is, however, hampered by the lack of biologist-friendly analysis and visualisation software: these disciplines are literally caught in a flood of data and are now facing many of the scale-out issues that high-performance computing has been addressing for years. Data must be managed, analysed and integrated, with substantial requirements of speed (in terms of execution time), application scalability and data representation. In this work, we present NuChart-II, an efficient and highly optimised tool for genomic data analysis that provides a gene-centric, graph-based representation of genomic information and which proposes an ex-post normalisation technique for Hi-C data. While designing NuChart-II, we addressed several common issues in the parallelisation of memory-bound algorithms for shared-memory systems.