High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of chromatin interactions and 3D chromosome folding on a larger scale. A graph-based multi-level representation of Hi-C data is essential for proper visualisation of the spatial pattern they represent, in particular for comparing different experiments or for re-mapping omics-data in a space-aware context. The size of the HiC data hampers the straightforward use of currently available graph visualisation tools and libraries. In this paper, we present the first version of NeoHiC, a user-friendly web application for the progressive graph visualisation of Hi-C data based on the use of the Neo4j graph database. The user could select the richness of the environment of the query gene by choosing among a large number of proximity and distance metrics.

NeoHiC: A web application for the analysis of Hi-C data / D'Agostino, D.; Lio, P.; Aldinucci, M.; Merelli, I.. - 12313:(2020), pp. 98-107. (Intervento presentato al convegno 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019 tenutosi a Bergamo; ita) [10.1007/978-3-030-63061-4_10].

NeoHiC: A web application for the analysis of Hi-C data

Lio P.;
2020

Abstract

High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of chromatin interactions and 3D chromosome folding on a larger scale. A graph-based multi-level representation of Hi-C data is essential for proper visualisation of the spatial pattern they represent, in particular for comparing different experiments or for re-mapping omics-data in a space-aware context. The size of the HiC data hampers the straightforward use of currently available graph visualisation tools and libraries. In this paper, we present the first version of NeoHiC, a user-friendly web application for the progressive graph visualisation of Hi-C data based on the use of the Neo4j graph database. The user could select the richness of the environment of the query gene by choosing among a large number of proximity and distance metrics.
2020
16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019
Graph database; Graph visualisation; Hi-C; Web application
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
NeoHiC: A web application for the analysis of Hi-C data / D'Agostino, D.; Lio, P.; Aldinucci, M.; Merelli, I.. - 12313:(2020), pp. 98-107. (Intervento presentato al convegno 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019 tenutosi a Bergamo; ita) [10.1007/978-3-030-63061-4_10].
File allegati a questo prodotto
File Dimensione Formato  
DAgostino_NeoHIC_2020.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 5.07 MB
Formato Adobe PDF
5.07 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1720272
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact