![]() ![]() Users can also display auxiliary information (traits) associated with haplotype sequences, such as geographical locations where samples were collected or phenotypic information. The number of mutations represented by the network's edges can be displayed using either ‘one-step edges’ (edges of length 1 joined through intermediate nodes of degree 2, which represent unsampled sequences), as hatch marks along edges, or as labels giving the number of mutations. The network viewer panel allows users to modify various aspects of the network's appearance, including colours, fonts and positions of nodes and labels. Once a haplotype network has been inferred, it is drawn in popart's network viewer panel using the spring embedded layout described by Tunkelang ( 1999). We have implemented several new methods in popart and describe one of these, integer neighbour-joining (IntNJ), below. 2002), minimum spanning networks and median-joining networks (MSN and MJN, respectively (Bandelt, Forster & Röhl 1999). popart allows users to use three popular haplotype network inference methods: tcs (Clement et al. popart takes sequence data as input and features several methods for inferring relationships designed for the analysis of population genetic data. The primary function of popart is the inference and visualisation of genetic relationships among intraspecific sequences. Haplotype network inference and visualisation We demonstrate the use of these methods and some of the other features available in popart using human mitochondrial sequence data recently published by Knapp et al. We include a brief description of the haplotype inference methods developed by other authors that have been implemented in popart, and introduce a new method, integer neighbour-joining (IntNJ). In addition to haplotype network construction, popart includes amova (Excoffier, Smouse & Quattro 1992), Tajima's D statistic (Tajima 1989), several other basic statistics that are useful for population genetics analyses, as well as tools for visualising the geographical distribution of genetic data using a map-based interface. popart includes implementations of minimum spanning, median-joining and tcs network methods and provides a powerful and user-friendly framework for the implementation and distribution of new methods. Here, we present popart (Population Analysis with Reticulate Trees), a software package for population genetics analysis using haplotype networks that is designed to be user-friendly and feature-rich and runs on Microsoft Windows, LINUX and Mac OS X. Worse still, many users produce figures ‘manually’ using graphics editing software, a process that is both time-consuming and error-prone. hapstar, Teacher & Griffiths 2011) to produce publication-quality figures from these networks. Finally, users are required to produce networks with one software package and then use another (e.g. arlequin, Excoffier, Laval & Schneider 2005 network dnasp, Librado & Rozas 2009) or Mac OS X (e.g. Moreover, most of these methods are only available in software packages that are no longer maintained ( tcs) or are available for either Microsoft Windows (e.g. However, there has been no single software package which implements all of the standard methods. 2002 4937 citations median-joining, Bandelt, Forster & Röhl 1999 3735 citations, according to Google Scholar, as of 15 April 2014). Various of these have been implemented in software packages such as tcs (Clement, Posada & Crandall 2000), network ( ), splitstree (Huson & Bryant 2006) and pegas (Paradis 2010), and these methods have been used extensively in the literature ( tcs, Clement, Posada & Crandall 2000 Clement et al. 2002 Cassens, Mardulyn & Milinkovitch 2005 Manolopoulou et al. Several methods for inferring haplotype networks have been described in the literature (Bandelt, Forster & Röhl 1999 Clement et al. ![]() Haplotype networks are used in the analysis of population genetic data to visualise genealogical relationships at the intraspecific level, as well as to make inference about biogeography and history of populations.
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