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Stanley Fields

dblp:85/9357 · DBLP profile ↗
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3ranked-venue papers
0as first author
1since 2021 · last 2021
0000-0001-5504-5925ORCID · corroborated

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics
variant effect prediction
0.512021
MaveRegistry: a collaboration platform for multiplexed assays of variant effect · Bioinform. 2021
Bioinformatics and computational biology › functional genomics
deep mutational scanning
0.112011
Enrich: software for analysis of protein function by enrichment and depletion of variants · Bioinform. 2011
Bioinformatics and computational biology
genomics
0.112011
A Three-Dimensional Model of the Yeast Genome · RECOMB 2011
Bioinformatics and computational biology
protein engineering
0.112011
Enrich: software for analysis of protein function by enrichment and depletion of variants · Bioinform. 2011
Bioinformatics and computational biology › protein sequence analysis
sequence-function relationship
0.112011
Enrich: software for analysis of protein function by enrichment and depletion of variants · Bioinform. 2011

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

sequencing error correction · 0.1enrichment ratio calculation · 0.1
YearPublicationVenuePosition
2021 MaveRegistry: a collaboration platform for multiplexed assays of variant effect
abstract
SUMMARY: Multiplexed assays of variant effect (MAVEs) are capable of experimentally testing all possible single nucleotide or amino acid variants in selected genomic regions, generating 'variant effect maps', which provide biochemical insight and functional evidence to enable more rapid and accurate clinical interpretation of human variation. Because the international community applying MAVE approaches is growing rapidly, we developed the online MaveRegistry platform to catalyze collaboration, reduce redundant efforts, allow stakeholders to nominate targets and enable tracking and sharing of progress on ongoing MAVE projects. AVAILABILITY AND IMPLEMENTATION: MaveRegistry service: https://registry.varianteffect.org. MaveRegistry source code: https://github.com/kvnkuang/maveregistry-front-end.
Da Kuang, Jochen Weile, Nishka Kishore, Maria Nguyen, Alan F. Rubin, Stanley Fields, Douglas M. Fowler, Frederick P. Roth
Bioinform.6
2011 A Three-Dimensional Model of the Yeast Genome
William Stafford Noble, Zhi-jun Duan, Mirela Andronescu, Kevin Schutz, Sean McIlwain, Yoo Jung Kim, Choli Lee, Jay Shendure, Stanley Fields, C. Anthony Blau
RECOMB9
2011 Enrich: software for analysis of protein function by enrichment and depletion of variants
abstract
SUMMARY: Measuring the consequences of mutation in proteins is critical to understanding their function. These measurements are essential in such applications as protein engineering, drug development, protein design and genome sequence analysis. Recently, high-throughput sequencing has been coupled to assays of protein activity, enabling the analysis of large numbers of mutations in parallel. We present Enrich, a tool for analyzing such deep mutational scanning data. Enrich identifies all unique variants (mutants) of a protein in high-throughput sequencing datasets and can correct for sequencing errors using overlapping paired-end reads. Enrich uses the frequency of each variant before and after selection to calculate an enrichment ratio, which is used to estimate fitness. Enrich provides an interactive interface to guide users. It generates user-accessible output for downstream analyses as well as several visualizations of the effects of mutation on function, thereby allowing the user to rapidly quantify and comprehend sequence-function relationships. AVAILABILITY AND IMPLEMENTATION: Enrich is implemented in Python and is available under a FreeBSD license at http://depts.washington.edu/sfields/software/enrich/. Enrich includes detailed documentation as well as a small example dataset. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.
Douglas M. Fowler, Carlos L. Araya, Wayne Gerard, Stanley Fields
Bioinform.4