standardizing population genetic simulations

we are a member of the PopSIm Consortium, aiming to standardize population genetic simulations for frequently studied model organisms and species, including humans.
As a result, the software stdpopsim allows to a) easily re-simulate published population models for many species and b) compare the results of new inference methods against a standardized benchmark.

Have a look at the stdpopsim documentation here.

Blockwise Passing Bablok Regression  for method comparison studies with repeated measurements

Passing-Bablok regression (PBR) is a non-parametric method widely used in clinical biochemistry and laboratory medicine to compare two measurement methods. It estimates the slope β and intercept α of the linear relationship y = α + βx by taking the median of all pairwise slopes between measurements.

When the data contain repeated measurements (multiple observations from the same sample or group), the within-group slopes have an expected value of 0 rather than β, and including them biases the slope estimate toward zero. Block-PBR corrects this by excluding within-group slopes from the median calculation and provides a corresponding asymptotic confidence interval that accounts for group sizes and inter-group overlap.

 

An example script on how to use our method is available at  github

(formerly known as IMaGe)

 Panicmage is a shortcut for "pangenome analyzer for infinitely - which means considerably - many genes".

The name changed from IMaGe to panicmage, since "image" is possibly one of the most stupid words to search for on google. In addition, the new name emphasizes that in our model, while there are infinitely many possibly existing genes, at any time a finite number of genes exists in the population.

 

Given

  • a genealogy
  • the gene frequency spectrum
  • the number of generations to the most recent common ancestor (optional),

panicmage estimates

  • the parameters of a neutral Infinitely Many Genes Model (gene gain and gene loss rates)
  • the number of core genes of the whole population
  • the expected number of new genes found in the next sequenced strain
  • the size of the persistent pangenome
  • the size of the total pangenome.

 In addition, panicmage computes the p-value of gene frequency spectra for a given genealogy under neutral evolution and can simulate distributed genomes. So far p-values for neutral evolution and for existing sampling bias can be computed.

 To install panicmage please visit the panicmage GitHub repository.

view panicmage manual