Integrating multi-omic features exploiting Chromosome Conformation Capture data

Abstract

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.

The Genome Conformation As an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer

“…The second layer models the genome conformation, since it represents Hi-C data processed with NuChart (Merelli et al, 2013b, 2015), on which epigenetic profiles are mapped as features of the vertices. The choice of mapping epigenetic patterns on nuclear maps is oriented at studying specific chromatin profiles that influence the final confirmation of the DNA in the nucleus, highlighting regulatory patterns.…”

“…As introduced, this method combines Next-Generation Sequencing (NGS) and 3C, a technique in which DNA (together with the proteins that coordinate the chromatin conformation) is cross-linked with formaldehyde, enzymatically fragmented, and re-ligated relying on its physical proximity in the nucleus. From the bioinformatics point of view, chromatin conformation data have been analyzed using NuChart (Merelli et al, 2013b, 2015), a complete suite of tools for the analysis of Hi-C experiments using a gene-centric point of view, to provide a map on which other omic information can be mapped.…”

“…In a previous work we developed NuChart (Merelli et al, 2013b, 2015), an R package that elaborates Hi-C information to provide a systems biology oriented, gene-centric view of the three-dimensional organization of the DNA in the nucleus. In this paper, we want to improve the graph representation of Hi-C data discussed in previous works, proposing a multi-level approach able to integrate different omics using multi-level networks.…”

HiCeekR: A Novel Shiny App for Hi-C Data Analysis

“…Therefore, other approaches available in literature could be easily encapsulated in further releases. In this regard, an interesting extension is the one proposed by Merelli et al (2015). In this latter case, by using NuChart tool they build multiple gene-centric graphs starting from Hi-C and transcription data, allowing additional statistical investigations, thanks to the graph-based approach.…”

Editorial: Multi-omic data integration

“…Methodological approaches point with a novel emphasis at the importance of molecules' spatial localization in the omic context. From polysome and ribosome profiling, RNA, and miRNA binding sites annotation and standardization (Dassi and Quattrone, 2014 ), to networks including 3D molecules' proximity thanks to Chromosome Conformation Capture (3C) and its omic version Hi-C (Merelli et al, 2015 ), spatial representation contributes with an important layer of information in this added multi-omic complexity.…”