A genome-wide 3C-method for characterizing the three-dimensional architectures of genomes
ABSTRACT Accumulating evidence demonstrates that the three-dimensional (3D) organization of chromosomes within the eukaryotic nucleus reflects and influences genomic activities, including transcription, DNA replication, recombination and DNA repair. In order to uncover structure-function relationships, it is necessary first to understand the principles underlying the folding and the 3D arrangement of chromosomes. Chromosome conformation capture (3C) provides a powerful tool for detecting interactions within and between chromosomes. A high throughput derivative of 3C, chromosome conformation capture on chip (4C), executes a genome-wide interrogation of interaction partners for a given locus. We recently developed a new method, a derivative of 3C and 4C, which, similar to Hi-C, is capable of comprehensively identifying long-range chromosome interactions throughout a genome in an unbiased fashion. Hence, our method can be applied to decipher the 3D architectures of genomes. Here, we provide a detailed protocol for this method.
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ABSTRACT: In this paper, I examine the role of the idea of synergy in life science research using examples in the fields of pharmacology/toxicology, molecular genetics and development, biochemistry, ecology and metabolic engineering. The research shows that synergy exhibits scale invariance. Small molecules act synergistically in the activation of single receptor molecules. Proteins function synergistically in development, metabolism and signaling. Synergy was found in the interaction between communities of organisms. Synergy manifests itself quantitatively or qualitatively: synergistic effects can be smaller or larger or they can be entirely different from what was expected. There is no single mathematical model that can be used uniformly to detect and quantify synergy. Synergy provides benefits for human health, wellbeing and economy. Synergy has explanatory and heuristic value in our quest to understand the function of and in designing complex biological systems.09/2014; 1:30-42. DOI:10.1016/j.synres.2014.07.004
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ABSTRACT: 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 expression and important links have been highlighted with other genomic features involved in DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements per- formed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between colocalization and coregulation of genes, but these important re- searches 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 ge- nomic 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 package developed in R, in which the graph-based representation of Hi-C data was tested, but that also showed inevitable problems of scalability while working genome-wide on large datasets. Particular attention has been paid at optimizing the data structures employed for the management of the information describing the neighbourhood graph, in order to facilitate the parallel implementation of the software. More- over, this novel implementation allows to solve some intrinsic problem in the normalization of Hi-C data, which now is performed directly on the graph, and also allowed a more reliable vi- sualization of the achieved graph. Thanks to this novel implementation, NuChart-II can scale to full genome representation, providing an invaluable tool for Hi-C data analysis.11th Intl. meeting on Computational Intelligence methods for Bionformatics and Biostatistics (CIBB), Cambridge, UK; 06/2014
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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.Frontiers in Genetics 02/2015; 6(40). DOI:10.3389/fgene.2015.00040