However, to use these software programs, the genome data need to be processed in specific formats such as alignments or allele frequencies. A number of sophisticated tree-building software such as TreeMix ( Pickrell & Pritchard, 2012) and QPgraph ( Patterson et al., 2012) have also been developed to accommodate the number of potential admixture events while inferring the phylogeny. In the recent past, a series of programs such as RaxML ( Stamatakis, 2006), ExaML ( Kozlov, Aberer & Stamatakis, 2015) and MP-EST ( Liu, Yu & Edwards, 2010) have been developed to infer phylogenetic relationship using whole genome data.
However, these software such as MEGA ( Kumar, Stecher & Tamura, 2016), PHYLIP ( Felsenstein, 2005), PAUP ( Wilgenbusch & Swofford, 2003) and BEAST ( Drummond et al., 2012) are suited only for gene-based sequence data. To implement these algorithms, computationally efficient software programs were developed. For this purpose, a number of mathematical and statistical algorithms have been developed. One of the major tasks in genetics and evolutionary biology is to infer the ancestral relationship between populations and species. The software including the source code, a test VCF file and a documentation are available at. It also produces pairwise-diversity matrix in MEGA and PHYLIP file formats as well as trees in the Newick format which could be directly used by other popular phylogenetic software programs. VCF2PopTree accepts genotype data from a local machine, constructs a tree using UPGMA and Neighbour-Joining algorithms and displays it on a web-browser. For example, it reads a VCF file containing 4 million SNPs and draws a tree in less than 30 seconds. To address this limitation, we have developed a user-friendly software, VCF2PopTree that uses genome-wide SNPs to construct and display phylogenetic trees in seconds to minutes. Such genotype data is typically presented in the Variant Call Format (VCF) and there is no simple software available that directly uses this data format to construct the phylogeny of populations in a short time. In the post-genomic era, researchers are able to obtain bioinformatically processed high-quality publication-ready whole genome data for many individuals in a population from next generation sequencing companies due to the reduction in the cost of sequencing and analysis. Recently, many standalone or web applications have been developed to handle large-scale whole genome data, but they are either computationally intensive, dependent on third party software or required significant time and resource of a web server.
However, most of these programs typically use alignments of sequences from genes to build phylogeny. In the past decades a number of software programs have been developed to infer phylogenetic relationships between populations.
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