Advantages of using graph databases to explore chromatin conformation capture experiments

Abstract

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.

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

“…Neo4j ( Neo4j) is a top-ranked graph database based on various metrics (DB-Engines Ranking of Graph DBMS, Hoksza & Jelínek, 2015), and has been useful in bioinformatics applications towards the research and analysis of complex biological pathways (Balaur et al, 2017, Casaní et al, 2020, human metabolism data (Fabregat et al, 2018) and DNA interaction studies (D'Agostino et al, 2021), to name a few. We found Neo4j to be useful in ingesting data from diverse databases independently and then integrating them based on our current use case.…”

Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

“…For a detailed programmatic workflow of the integration of the individual databases, the fields extracted from each, and the final construction of the data set, see Section S1 of Appendix S1. Neo4j (Van Bruggen, 2014) is a top-ranked graph database based on various metrics (DB-Engines Ranking of Graph DBMS; Hoksza & Jelínek, 2015), and has been useful in bioinformatics applications towards the research and analysis of complex biological pathways (Balaur et al, 2017;Casaní-Galdón et al, 2020), human metabolism data (Fabregat et al, 2018) and DNA interaction studies (D'Agostino et al, 2021), to name a few. We found Neo4j to be useful in ingesting data from diverse databases independently and then integrating them based on our current use case.…”