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Advantages of using graph databases to explore chromatin conformation capture experiments.
D'Agostino, Daniele; Liò, Pietro; Aldinucci, Marco; Merelli, Ivan.
Afiliación
  • D'Agostino D; Institute of Electronics, Computer and Telecommunication Engineering, National Research Council of Italy, Genoa, Italy. dagostino@ieiit.cnr.it.
  • Liò P; Computer Laboratory, University of Cambridge, Cambridge, UK.
  • Aldinucci M; Computer Science Department, University of Turin, Turin, Italy.
  • Merelli I; Institute for Biomedical Technologies, National Research Council of Italy, Segrate, MI, Italy.
BMC Bioinformatics ; 22(Suppl 2): 43, 2021 Apr 26.
Article en En | MEDLINE | ID: mdl-33902433
BACKGROUND: High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. METHODS: Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. RESULTS: These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). CONCLUSION: With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Cromatina / Cromosomas Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Cromatina / Cromosomas Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2021 Tipo del documento: Article País de afiliación: Italia