Abstract
Recent advances in molecular biology and Bioinformatics techniques brought to an explosion of the information about the spatial organisation of the DNA in the nucleus. High-throughput chromosome conformation capture techniques provide a genome-wide capture of chromatin contacts at unprecedented scales, which permit to identify physical interactions between genetic elements located throughout the human genome. These important studies are hampered by the lack of biologists-friendly software. In this work we present NuchaRt, an R package that wraps NuChart-II, an efficient and highly optimized C++ tool for the exploration of Hi-C data. By rising the level of abstraction, NuchaRt proposes a high-performance pipeline that allows users to orchestrate analysis and visualisation of multi-omics data, making optimal use of the computing capabilities offered by modern multi-core architectures, combined with the versatile and well known R environment for statistical analysis and data visualisation.
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Notes
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We actually use data.tables as basic data structures for our datasets: data.table is an enhanced version of data.frame that allows to easily optimise operations for speed and memory usage.
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Acknowledgements
This work has been partially supported by the EC-FP7 STREP project “REPARA” (no. 609666), the Italian Ministry of Education and Research Flagship (PB05) “InterOmics”, and the EC-FP7 innovation project “MIMOMICS”.
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Tordini, F., Merelli, I., Liò, P., Milanesi, L., Aldinucci, M. (2016). NuchaRt: Embedding High-Level Parallel Computing in R for Augmented Hi-C Data Analysis. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_20
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DOI: https://doi.org/10.1007/978-3-319-44332-4_20
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