Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12864-015-2140-x
DC FieldValue
dc.titleGenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data
dc.contributor.authorHarmston, N
dc.contributor.authorIng-Simmons, E
dc.contributor.authorPerry, M
dc.contributor.authorBareši, A
dc.contributor.authorLenhard, B.
dc.date.accessioned2020-09-04T02:07:41Z
dc.date.available2020-09-04T02:07:41Z
dc.date.issued2015
dc.identifier.citationHarmston, N, Ing-Simmons, E, Perry, M, Bareši, A, Lenhard, B. (2015). GenomicInteractions: An R/Bioconductor package for manipulating and investigating chromatin interaction data. BMC Genomics 16 (1) : 963. ScholarBank@NUS Repository. https://doi.org/10.1186/s12864-015-2140-x
dc.identifier.issn14712164
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/174274
dc.description.abstractBackground: Precise quantitative and spatiotemporal control of gene expression is necessary to ensure proper cellular differentiation and the maintenance of homeostasis. The relationship between gene expression and the spatial organisation of chromatin is highly complex, interdependent and not completely understood. The development of experimental techniques to interrogate both the higher-order structure of chromatin and the interactions between regulatory elements has recently lead to important insights on how gene expression is controlled. The ability to gain these and future insights is critically dependent on computational tools for the analysis and visualisation of data produced by these techniques. Results and conclusion: We have developed GenomicInteractions, a freely available R/Bioconductor package designed for processing, analysis and visualisation of data generated from various types of chromosome conformation capture experiments. The package allows the easy annotation and summarisation of large genome-wide datasets at both the level of individual interactions and sets of genomic features, and provides several different methods for interrogating and visualising this type of data. We demonstrate this package's utility by showing example analyses performed on interaction datasets generated using Hi-C and ChIA-PET. © 2015 Harmston et al.
dc.publisherBioMed Central Ltd.
dc.sourceUnpaywall 20200831
dc.subjectchromatin
dc.subjectchromosome
dc.subjectgenome
dc.subjectanimal
dc.subjectchromatin
dc.subjectcomputer graphics
dc.subjectgenetic database
dc.subjectgenetics
dc.subjectgenomics
dc.subjecthuman
dc.subjectK-562 cell line
dc.subjectmetabolism
dc.subjectmolecular genetics
dc.subjectmouse
dc.subjectprocedures
dc.subjectsoftware
dc.subjectthymocyte
dc.subjectchromatin
dc.subjectAnimals
dc.subjectChromatin
dc.subjectComputer Graphics
dc.subjectDatabases, Genetic
dc.subjectGenomics
dc.subjectHumans
dc.subjectK562 Cells
dc.subjectMice
dc.subjectMolecular Sequence Annotation
dc.subjectSoftware
dc.subjectThymocytes
dc.typeArticle
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.description.doi10.1186/s12864-015-2140-x
dc.description.sourcetitleBMC Genomics
dc.description.volume16
dc.description.issue1
dc.description.page963
dc.published.statePublished
Appears in Collections:Elements
Staff Publications

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_s12864-015-2140-x.pdf926.67 kBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

18
checked on Jun 29, 2022

Page view(s)

142
checked on Jun 23, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.