Canis STR-seq: A universal approach for non-invasive genetic monitoring of wolves and coyotes

Population genetic studies have traditionally relied on data from short tandem repeat (STR) markers, known as microsatellites, to produce individual genotypes used in population genetics research. However, size fragment analysis from traditional capillary electrophoresis presents scoring challenges and limits data comparisons among labs. Here, we present a new cost-effective, universal microsatellite genotype-by-sequencing assay for Canis species that allows for unambiguous allele calls, flags homoplasy for more accurate assignment tests and estimates of diversity, and improves genotyping output from low-template DNA. We note size homoplasy in 18 of 26 loci, with the number of alleles being 32% higher in the dataset that included sequence mutations (Namut=334) compared to the dataset based on size alone (Nalen=253). Bayesian cluster analysis was similar for both datasets, although 63 of 83 samples had higher assignment values to their primary cluster when mutations were considered. We d..., , # Data from: Canis STR-seq: A universal approach for non-invasive genetic monitoring of wolves and coyotes

Dataset DOI: [10.5061/dryad.02v6wwqgb](10.5061/dryad.02v6wwqgb)

## Description of the data and file structure

We amplified 33 autosomal microsatellite markers and the Amelogenin sex marker in a single PCR followed by indexing of individual Canis tissue/scat samples (in separate runs) with Nextera XT unique dual indexes followed by sequencing on an Illumina MiSeq platform. 

Prior to amplification, tissue samples were standardized to include 5ng in the PCR, and scat samples were screened for sufficient nuclear DNA by amplification at a microsatellite primer and visualization against known standards on an agarose gel.

Five of the 33 markers were removed from the analysis, leaving 26 markers for routine genotyping. The raw data includes all 33 markers.

The data file includes input files to run Seq2Sat / SatAnalyzer with the raw data provided.

### Files and variables

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