We have shown how to use binomial mixture models to estimate microbial core- and pan-genome size, and the vast literature on capture-recapture methods should be further exploited in microbial pangenomics, as it has been in closely related fields like metagenomics [ 21].
We have described two statistical models based on Binomial mixture models to infer SNVs from aligned NGS data obtained from tumors.
In this perspective, the binomial mixture model will tend to under-estimate the true pan-size for smaller data sets.
Results: We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem.
As an alternative to the binomial mixture model estimate, we have also included the Chao lower-bound estimate [ 11] when fitting to real data.
With the statistical viewpoint, this problem is equivalent to the parameter estimation for an infinite binomial mixture model.
To overcome these limitations, we propose a probabilistic approach based on a Binomial mixture model, called SNVMix1, which computes posterior probabilities, providing a measure of confidence on the SNV predictions.
Analyzing pan-genomics data with binomial mixture models is a way to handle dependencies between genomes, which we find is always present.
The use of a binomial mixture model for estimating the pan-genome size was introduced by [ 7], but the use of mixture models for population size estimation is by no way new, e.g. [ 8, 10, 16].
One way of obtaining this is by using a binomial mixture model.
