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VCF2PopTree: a client-side software to construct population phylogeny from genome-wide SNPs

Authors :
Sankar Subramanian
Umayal Ramasamy
David Chen
Source :
PeerJ, Vol 7, p e8213 (2019)
Publication Year :
2019
Publisher :
PeerJ Inc., 2019.

Abstract

In the past decades a number of software programs have been developed to infer phylogenetic relationships between populations. However, most of these programs typically use alignments of sequences from genes to build phylogeny. 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. 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. 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. 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. For example, it reads a VCF file containing 4 million SNPs and draws a tree in less than 30 seconds. VCF2PopTree accepts genotype data from a local machine, constructs a tree using UPGMA and Neighbour-Joining algorithms and displays it on a web-browser. 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. The software including the source code, a test VCF file and a documentation are available at: https://github.com/sansubs/vcf2pop.

Details

Language :
English
ISSN :
21678359
Volume :
7
Database :
Directory of Open Access Journals
Journal :
PeerJ
Publication Type :
Academic Journal
Accession number :
edsdoj.07e9645c4eff91e3aa224f2346bc
Document Type :
article
Full Text :
https://doi.org/10.7717/peerj.8213