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Ryan P. Welch

dblp:94/8711 · DBLP profile ↗
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3ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 3 · 1 since 2021

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
3 papers
Bioinformatics and computational biology · 100%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics › genome-wide association study
GWAS visualization
0.622021
LocusZoom.js: interactive and embeddable visualization of genetic association study results · Bioinform. 2021
LocusZoom: regional visualization of genome-wide association scan results · Bioinform. 2010
Bioinformatics and computational biology › epigenomics
ChIP-seq analysis
0.212014
Broad-Enrich: functional interpretation of large sets of broad genomic regions · Bioinform. 2014
Bioinformatics and computational biology › functional genomics
functional enrichment analysis
0.212014
Broad-Enrich: functional interpretation of large sets of broad genomic regions · Bioinform. 2014
Bioinformatics and computational biology › genomics
genome visualization
0.112021
LocusZoom.js: interactive and embeddable visualization of genetic association study results · Bioinform. 2021
Bioinformatics and computational biology › genomics
genome-wide association study
0.112010
LocusZoom: regional visualization of genome-wide association scan results · Bioinform. 2010
Bioinformatics and computational biology
genomics
0.112010
LocusZoom: regional visualization of genome-wide association scan results · Bioinform. 2010
Bioinformatics and computational biology › population genetics
linkage disequilibrium
0.012010
LocusZoom: regional visualization of genome-wide association scan results · Bioinform. 2010

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

web upload service · 0.5javascript library · 0.5gene locus coverage · 0.2false-positive rate calibration · 0.2
YearPublicationVenuePosition
2021 LocusZoom.js: interactive and embeddable visualization of genetic association study results
abstract
SUMMARY: LocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets. AVAILABILITY AND IMPLEMENTATION: LocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages for all versions are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Andrew P. Boughton, Ryan P. Welch, Matthew Flickinger, Peter VandeHaar, Daniel Taliun, Gonçalo R. Abecasis, Michael Boehnke
Bioinform.2
2014 Broad-Enrich: functional interpretation of large sets of broad genomic regions
abstract
MOTIVATION: Functional enrichment testing facilitates the interpretation of Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) data in terms of pathways and other biological contexts. Previous methods developed and used to test for key gene sets affected in ChIP-seq experiments treat peaks as points, and are based on the number of peaks associated with a gene or a binary score for each gene. These approaches work well for transcription factors, but histone modifications often occur over broad domains, and across multiple genes. RESULTS: To incorporate the unique properties of broad domains into functional enrichment testing, we developed Broad-Enrich, a method that uses the proportion of each gene's locus covered by a peak. We show that our method has a well-calibrated false-positive rate, performing well with ChIP-seq data having broad domains compared with alternative approaches. We illustrate Broad-Enrich with 55 ENCODE ChIP-seq datasets using different methods to define gene loci. Broad-Enrich can also be applied to other datasets consisting of broad genomic domains such as copy number variations. AVAILABILITY AND IMPLEMENTATION: http://broad-enrich.med.umich.edu for Web version and R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Raymond G. Cavalcante, Chee Lee, Ryan P. Welch, Snehal Patil, Terry E. Weymouth, Laura J. Scott, Maureen A. Sartor
Bioinform.3
2010 LocusZoom: regional visualization of genome-wide association scan results
abstract
UNLABELLED: Genome-wide association studies (GWAS) have revealed hundreds of loci associated with common human genetic diseases and traits. We have developed a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format. LocusZoom visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns and the positions of genes in the region. AVAILABILITY: LocusZoom can be accessed from a web interface at http://csg.sph.umich.edu/locuszoom. Users may generate a single plot using a web form, or many plots using batch mode. The software utilizes LD information from HapMap Phase II (CEU, YRI and JPT+CHB) or 1000 Genomes (CEU) and gene information from the UCSC browser, and will accept SNP identifiers in dbSNP or 1000 Genomes format. Single plots are generated in approximately 20 s. Source code and associated databases are available for download and local installation, and full documentation is available online.
Randall J. Pruim, Ryan P. Welch, Serena Sanna, Tanya M. Teslovich, Peter S. Chines, Terry P. Gliedt, Michael Boehnke, Gonçalo R. Abecasis, Cristen J. Willer
Bioinform.2