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Oksana V. Vernygora

dblp:425/3870 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-6444-3888ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 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
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › population genetics
ancestry inference
0.812024
snpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics · Bioinform. 2024
Bioinformatics and computational biology › population genetics
population genomics
0.812024
snpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics · Bioinform. 2024

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

leave-one-out cross-validation · 0.8
YearPublicationVenuePosition
2024 snpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics
abstract
MOTIVATION: Single nucleotide polymorphism (SNP) markers are increasingly popular for population genomics and inferring ancestry for individuals of unknown origin. Because large SNP datasets are impractical for rapid and routine analysis, diagnostics rely on panels of highly informative markers. Strategies exist for selecting these markers, however, resources for efficiently evaluating their performance are limited for non-model systems. RESULTS: snpAIMeR is a user-friendly R package that evaluates the efficacy of genomic markers for the cluster assignment of unknown individuals. It is intended to help minimize panel size and genotyping effort by determining the informativeness of candidate diagnostic markers. Provided genotype data from individuals of known origin, it uses leave-one-out cross-validation to determine population assignment rates for individual markers and marker combinations. AVAILABILITY AND IMPLEMENTATION: snpAIMeR is available on CRAN (https://CRAN.R-project.org/package=snpAIMeR).
Kim L. Vertacnik, Oksana V. Vernygora, Julian R. Dupuis
Bioinform.2