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Oliver S. Burren

dblp:93/4045 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2016
0000-0002-3388-5760ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › epigenomics
chromatin interaction analysis
0.212016
CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets · Bioinform. 2016
Bioinformatics and computational biology
genomics
0.212016
CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets · Bioinform. 2016
Bioinformatics and computational biology › genomics
genome-wide association study
0.212014
VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes · Bioinform. 2014
Visualization and visual analytics › biological data visualization
genomic data visualization
0.112016
CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets · Bioinform. 2016
Bioinformatics and computational biology
functional genomics
0.112014
VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes · Bioinform. 2014
Bioinformatics and computational biology › gene regulation › transcription factor analysis
transcription factor target identification
0.112014
VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes · Bioinform. 2014

Methods — techniques the papers use, named apart from their topics

interactive visualization · 0.5non-parametric SNP set enrichment · 0.2
YearPublicationVenuePosition
2016 CHiCP: a web-based tool for the integrative and interactive visualization of promoter capture Hi-C datasets
abstract
UNLABELLED: Promoter capture Hi-C (PCHi-C) allows the genome-wide interrogation of physical interactions between distal DNA regulatory elements and gene promoters in multiple tissue contexts. Visual integration of the resultant chromosome interaction maps with other sources of genomic annotations can provide insight into underlying regulatory mechanisms. We have developed Capture HiC Plotter (CHiCP), a web-based tool that allows interactive exploration of PCHi-C interaction maps and integration with both public and user-defined genomic datasets. AVAILABILITY AND IMPLEMENTATION: CHiCP is freely accessible from www.chicp.org and supports most major HTML5 compliant web browsers. Full source code and installation instructions are available from http://github.com/D-I-L/django-chicp CONTACT: [email protected].
E. C. Schofield, Tim J. Carver, Premanand Achuthan, P. Freire-Pritchett, Mikhail Spivakov, John A. Todd, Oliver S. Burren
Bioinform.7
2014 VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes
abstract
MOTIVATION: Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms. RESULTS: We extended a non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci to a situation where only GWAS P-values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to identify enrichment of type 1 diabetes (T1D) GWAS associations near genes that are targets for the transcription factors IKZF3, BATF and ESRRA. IKZF3 lies in a known T1D susceptibility region, while BATF and ESRRA overlap other immune disease susceptibility regions, validating our approach and suggesting novel avenues of research for T1D. AVAILABILITY AND IMPLEMENTATION: VSEAMS is available for download (http://github.com/ollyburren/vseams).
Oliver S. Burren, Chris Wallace 0002
Bioinform.1