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VirusMuT was developed to estimate mutation rate and TMRCA for virus. It has been used to SARS-CoV-2. The software is based on Poisson model and the mutation rate and TMRCA are estimated by maximum likelihood method.

We present a method that jointly analyzes the polymorphism and divergence sites in genomic sequences of multiple species to identify the genes under natural selection and pinpoint the occurrence time of selection to a specific lineage of the species phylogeny. This method integrates population genetics models using a Bayesian Poisson random field framework and combines information over all gene loci to boost the power for detecting selection. The method provides posterior distributions of the fitness effects of each gene along with parameters associated with the evolutionary history, including the species divergence time and effective population size of external species.

STRsensor (v1.0) is a method that can achieve high performance in both whole genome sequencing (WGS) data and multiplex deep sequencing data in STR allele-typing. The benchmark shows that STRsensor can obtain 99.64% detection ratio and 99.9% accuracy in 687 multiplex deep sequencing samples, while 100% detection ratio and 99.37% accuracy in a simulated 30 low-coverage WGS data.

Inferring an individual's ancestry or group membership using a small set of highly informative genetic markers is very useful in forensic and medical genetics. However, given the huge amount of SNP data available from a diverse of populations, it is challenging to develop informative panels by exhaustively searching for all possible SNP combinations. AIM-SNPTag was developed to select an optimal set of SNPs that maximizes the inference accuracy while minimizes the set size.

Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. hmmSweep is a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method.

The allele frequency spectrum (AFS), or site frequency spectrum, is commonly used to summarize the genomic polymorphism pattern of a sample, which is informative for inferring population history and detecting natural selection. In 2013, Chen and Chen developed a method for analytically deriving the AFS for populations with temporally varying size through the coalescence time-scaling function. However, their approach is only applicable to population history scenarios in which the analytical form of the time-scaling function is tractable. AFS-CH is a computational approach to extend the method to populations with arbitrary complex varying size by numerically approximating the time-scaling function.




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