SambaR: An R package for fast, easy and reproducible population‐genetic analyses of biallelic SNP data sets

Abstract

SNP data sets can be used to infer a wealth of information about natural populations, including information about their structure, genetic diversity, and the presence of loci under selection. However, SNP data analysis can be a time‐consuming and challenging process, not in the least because at present many different software packages are needed to execute and depict the wide variety of mainstream population‐genetic analyses. Here, we present SambaR, an integrative and user‐friendly R package which automates and simplifies quality control and population‐genetic analyses of biallelic SNP data sets. SambaR allows users to perform mainstream population‐genetic analyses and to generate a wide variety of ready to publish graphs with a minimum number of commands (less than 10). These wrapper commands call functions of existing packages (including adegenet, ape, LEA, poppr, pcadapt and StAMPP) as well as new tools uniquely implemented in SambaR. We tested SambaR on online available SNP data sets and found that SambaR can process data sets of over 100,000 SNPs and hundreds of individuals within hours, given sufficient computing power. Newly developed tools implemented in SambaR facilitate optimization of filter settings, objective interpretation of ordination analyses, enhance comparability of diversity estimates from reduced representation library SNP data sets, and generate reduced SNP panels and structure‐like plots with Bayesian population assignment probabilities. SambaR facilitates rapid population genetic analyses on biallelic SNP data sets by removing three major time sinks: file handling, software learning, and data plotting. In addition, SambaR provides a convenient platform for SNP data storage and management, as well as several new utilities, including guidance in setting appropriate data filters. The SambaR source script, manual and example data set are distributed through GitHub: https://github.com/mennodejong1986/SambaR.

Genomic Impact of Whaling in North Atlantic Fin Whales

“…Population structure analyses were then performed on the combined fin whale SNP data set that was further thinned randomly (one SNP per 1 kbp) with vcftools v0.1.16 thin ( Danecek et al 2011 ) to reduce the computational load of the following steps. The R-package samba R ( de Jong et al 2021 ) was used to filter out individuals with more than 5% missing data as well as SNPs with more than 10% missing data, heterozygosity excess, and a minor allele count of 1. Population-genetic analyses were performed by using samba R’s main functions “findstructure()” and “calcdistance()”.…”

Population analysis of retrotransposons in giraffe genomes supports RTE decline and widespread LINE1 activity in Giraffidae

“…Population-genetic analyses were performed in R-4.1.0 [ 76 ] using wrapper functions of the R package SambaR [ 77 ]. The data was imported into R and stored in a genlight object provided by the R package adegenet [ 78 , 79 ].…”

“…Pairwise population Weir & Cockerham 1984 Fst estimates were calculated with the function ‘stamppFst’ of the R package StAMPP. Pairwise population Fst-values according to Wright 1943 were calculated with the function ‘runWrightFst’ of the R package SambaR [ 77 ]. Principal component analyses (PCA) was performed using the function ‘snpgdsPCA’ of the R package SNPRelate [ 81 ].…”

snpfiltr: An R package for interactive and reproducible SNP filtering

“…Notably, functions from the snpfiltr package rely on vcfr objects as input, which can be generated by reading vcf files into an R session using the vcfR function read.vcfR(). snpfiltr is also complementary to the growing suite of R packages designed for phylogenetic and population genetic analysis (e.g., ape [Paradis & Schliep, 2019], stampp [Pembleton et al, 2013], snprelate [Zheng et al, 2012], adegenet [Jombart, 2008], sambar [de Jong et al, 2021], and introgress [Gompert & Buerkle, 2010]), as the vcfr objects read and returned by snpfiltr can be easily converted into the appropriate input object class for each of these specialized packages.…”