Pcangsd
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Description
Framework for analyzing low-depth next-generation sequencing (NGS) data in heterogeneous/structured populations using principal component analysis (PCA). Population structure is inferred by estimating individual allele frequencies in an iterative approach using a truncated SVD model. The covariance matrix is estimated using the estimated individual allele frequencies as prior information for the unobserved genotypes in low-depth NGS data.
The estimated individual allele frequencies can further be used to account for population structure in other probabilistic methods. PCAngsd can perform the following analyses:
Covariance matrix Admixture estimation Inbreeding coefficients (both per-individual and per-site) HWE test Genome-wide selection scans Genotype calling Estimate NJ tree of samples
Environment Modules
Run module spider pcangsd to find out what environment modules are available for this application.
System Variables
- HPC_PCANGSD_DIR - installation directory
- HPC_PCANGSD_BIN - executable directory