Resumen
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.
| Idioma original | English |
|---|---|
| Número de artículo | btae377 |
| Publicación | Bioinformatics |
| Volumen | 40 |
| N.º | 6 |
| DOI | |
| Estado | Published - jun 1 2024 |
Nota bibliográfica
Publisher Copyright:© 2024 The Author(s). Published by Oxford University Press.
Financiación
This work has been supported by the United States Department of Agriculture-National Institute of Food and Agriculture-Agriculture and Food Research Initiative [2020–67013-30978]; United States Department of Agriculture-Animal and Plant Health Inspection Service-Plant Protection Act [AP20PPQS&T00C154, AP21PPQS&T00C063, AP22PPQS&T00C070]; and United States Department of Agriculture Hatch Grant [KY008091].
| Financiadores | Número del financiador |
|---|---|
| United States Department of Agriculture-Animal and Plant Health Inspection Service-Plant Protection Act | T00C154, T00C063, T00C070 |
| U.S. Department of Agriculture | KY008091 |
| U.S. Department of Agriculture | |
| United States Department of Agriculture National Institute of Food and Agriculture, Agriculture and Food Research Initiative CARE | 2020–67013-30978 |
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics
Huella
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