Publicly available multi-omic databases, in particular if associated with medical annotations, are rich resources with the potential to lead a rapid transition from high-throughput molecular biology experiments to better clinical outcomes for patients. In this work, we propose a model for multi-omic data integration (i.e., genetic variations, gene expression, genome conformation, and epigenetic patterns), which exploits a multi-layer network approach to analyse, visualize, and obtain insights from such biological information, in order to use achieved results at a macroscopic level. Using this representation, we can describe how driver and passenger mutations accumulate during the development of diseases providing, for example, a tool able to characterize the evolution of cancer. Indeed, our test case concerns the MCF-7 breast cancer cell line, before and after the stimulation with estrogen, since many datasets are available for this case study. In particular, the integration of data about cancer mutations, gene functional annotations, genome conformation, epigenetic patterns, gene expression, and metabolic pathways in our multi-layer representation will allow a better interpretation of the mechanisms behind a complex disease such as cancer. Thanks to this multi-layer approach, we focus on the interplay of chromatin conformation and cancer mutations in different pathways, such as metabolic processes, that are very important for tumor development. Working on this model, a variance analysis can be implemented to identify normal variations within each omics and to characterize, by contrast, variations that can be accounted to pathological samples compared to normal ones. This integrative model can be used to identify novel biomarkers and to provide innovative omic-based guidelines for treating many diseases, improving the efficacy of decision trees currently used in clinic.

Bilirubin neurotoxicity has been studied for decades and has been shown to affect various mechanisms via significant modulation of gene expression. This suggests that vital regulatory mechanisms of gene expression, such as epigenetic mechanisms, could play a role in bilirubin neurotoxicity. Histone acetylation has recently received attention in the CNS due to its role in gene modulation for numerous biological processes, such as synaptic plasticity, learning, memory, development and differentiation. Aberrant epigenetic regulation of gene expression in psychiatric and neurodegenerative disorders has also been described. In this work, we followed the levels of histone 3 lysine 14 acetylation (H3K14Ac) in the cerebellum (Cll) of the developing (2, 9, 17 days after the birth) and adult Gunn rat, the natural model for neonatal hyperbilirubinemia and kernicterus. We observed an age-specific alteration of the H3K14Ac in the hyperbilirubinemic animals. The GeneOntology analysis of the H3K14Ac linked chromatin revealed that almost 45% of H3K14Ac ChiP-Seq TSS-promoter genes were involved in CNS development including maturation and differentiation, morphogenesis, dendritogenesis, and migration. These data suggest that the hallmark Cll hypoplasia in the Gunn rat occurs also via epigenetically controlled mechanisms during the maturation of this brain structure, unraveling a novel aspect of the bilirubin-induced neurotoxicity.

Genomic instability is a hallmark of cancer. Whether it also occurs in Cancer Associated Fibroblasts (CAFs) remains to be carefully investigated. Loss of CSL/RBP-Jκ, the effector of canonical NOTCH signaling with intrinsic transcription repressive function, causes conversion of dermal fibroblasts into CAFs. Here, we find that CSL down-modulation triggers DNA damage, telomere loss and chromosome end fusions that also occur in skin Squamous Cell Carcinoma (SCC)-associated CAFs, in which CSL is decreased. Separately from its role in transcription, we show that CSL is part of a multiprotein telomere protective complex, binding directly and with high affinity to telomeric DNA as well as to UPF1 and Ku70/Ku80 proteins and being required for their telomere association. Taken together, the findings point to a central role of CSL in telomere homeostasis with important implications for genomic instability of cancer stromal cells and beyond.

The representation, integration, and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture allows the analysis of the chromosome organization in the cell’s natural state. While performed genome wide, this technique is usually called Hi–C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi–C data to describe the chromosomal neighborhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.

Abstract-High-throughput molecular biology techniques are widely used to identify physical interactions between genetic elements located throughout the human genome. Chromosome Conformation Capture (3C) and other related techniques allow to investigate the spatial organisation of chromosomes in the cell's natural state. Recent results have shown that there is a large correlation between co-localization and co-regulation of genes, but these important information are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we introduce NuChart-II, a tool for Hi-C data analysis that provides a gene-centric view of the chromosomal neighbourhood in a graph-based manner. NuChart-II is an efficient and highly optimized C++ re-implementation of a previous prototype package developed in R. Representing Hi-C data using a graphbased approach overcomes the common view relying on genomic coordinates and permits the use of graph analysis techniques to explore the spatial conformation of a gene neighbourhood.

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.

Abstract-This paper presents the optimisation efforts on the creation of a graph-based mapping representation of gene adjacency. The method is based on the Hi-C process, starting from Next Generation Sequencing data, and it analyses a huge amount of static data in order to produce maps for one or more genes. Straightforward parallelisation of this scheme does not yield acceptable performance on multicore architectures since the scalability is rather limited due to the memory bound nature of the problem. This work focuses on the memory optimisations that can be applied to the graph construction algorithm and its (complex) data structures to derive a cache-oblivious algorithm and eventually to improve the memory bandwidth utilisation. We used as running example NuChart-II, a tool for annotation and statistic analysis of Hi-C data that creates a gene-centric neighborhood graph. The proposed approach, which is exemplified for Hi-C, addresses several common issue in the parallelisation of memory bound algorithms for multicore. Results show that the proposed approach is able to increase the parallel speedup from 7x to 22x (on a 32-core platform). Finally, the proposed C++ implementation outperforms the first R NuChart prototype, by which it was not possible to complete the graph generation because of strong memory-saturation problems.

Availability: This is the author's manuscriptAbstract 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 of a cell. High-throughput molecular biology techniques provide a genome-wide capture of the spatial organization of chromosomes at unprecedented scales, which permit to identify physical interactions between genetic elements located throughout a genome. This important information is however hampered by the lack of biologists-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 (HPC) has been addressing for years. Data must be managed, analysed and integrated, with substantial requirements in speed (in terms of execution time), application scalability and data representation. In this work we present NuChart-II, an efficient and highly optimized tool for genomic data analysis that provides a gene-centric, graph-based representation of genomic information, and 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.