V. 1.0 MIVEGEC-IRD / MAB-LIRMM-CNRS
Overview

Phylogenetic trees are often combined with extrinsic traits to reconstruct evolutionary scenarios in various research fields, including phylogeography, molecular epidemiology, and ecology. Analysis starts with the computation of ancestral annotations from extrinsic traits (discrete variables such as geographic origin, resistance to a treatment, and life history traits), associated with the sampled sequences used to build the phylogenetic tree. Analysis continues with the study of the evolutionary changes from the tree root to its tips to characterize evolutionary scenarios such as the spread of a disease, the dynamic of a drug resistance, or shifts in ecological habitats. Difficulties may arise when interpreting the output. First, there is a diversity of optimization criteria for computing trees and ancestral annotations (parsimony, maximum likelihood, Bayesian) and each of them involves various models of evolution; these methods may all yield different results that need to be compared. Second, the tree size and complexity of the annotations can be an inconvenience with respect to computation time or tedious graphical analysis. Third, although probabilistic models give more accurate results than other methods, if some ancestral characters do not stand out clearly from others (i.e. they are much more likely), they may produce a combinatorial explosion of potential evolutionary scenarios. To the best of our knowledge, there are no tools to help with the comparison of different sets of ancestral annotations and find common patterns across multiple evolutionary scenarios from a beam of transitions. We thus present PastView, a new editor with a user-friendly interface that includes numerical and graphical features to help users in this context. PastView is available publicly as a standalone software. Contact : francois.chevenet@ird.fr


News


Sep. 2019 PastView release (V46) (Bugs corrected for the Windows version)
Jul. 2019 PastView release (V45) & User's Manual 1.1 & Tutorial
May. 2019 PastView release (V43, new compression level for transitions maps) & PastView web interface
Nov. 2018 PastView release (V40, transitions matrices revisited)
Sep. 2018 PastView release (V39, transitions query revisited)
Jul. 2018 PastView first release


PastView web interface

A PastView web interface is available here. It is still under construction and it will be updated ASAP.


PastView screenshots

How to load & run PastView ?

1) Download & Install Tcl/Tk: OSX Linux Windows
2) Download PastView package (source code, multi-platforms)
3) under Linux or OSX: from a terminal, goto to the downloaded PastView folder, then command "wish", then from the wish prompt, command "source pastview.tcl". Under Windows: double click the pastview.tcl file.


Documentation


Citation

F. Chevenet*, G. Castel, E. Jousselin and O. Gascuel
PastView: a user-friendly interface to explore evolutionary scenarios
BMC Evolutionary Biology submitted

(*) corresponding author


Funding

IRD - MIVEGEC (IBC)
Institut de Biologie Computationnelle (IBC)
PALADIN project The PALADIN project was publicly funded through the French National Research Agency under the "Investissements d’Avenir" program with the reference ANR-10-LABX-04-01 Labex CEMEB and coordinated by the University of Montpellier

The PALADIN project aims to better understand the evolution and current diversity of a zoonotic virus (Puumala, PUUV) circulating in France in bank vole populations and responsible in humans for nephropathy epidemic (NE). Puumala virus (PUUV) is a species of hantavirus. Humans infected with the virus may develop a haemorrhagic fever with renal syndrome (HFRS) known as nephropathia epidemica. A recent study has shown the presence of distinct French PUUV "sublineages" in endemic and non-endemic regions. However the evolutionary relationships between these sublineages and the evolutionary history of the virus in areas located between these regions remains unknown. We propose to combine rodent sampling, serological analyses and virus sequencing to investigate viral diversity at the population and intra-host (quasi-species) levels. Phylogeographic analyses will be conducted using classical Bayesian approaches and a new method, developed in this project, to infer ancestral characters and to reconstruct evolutionary scenario through graphic and decision-making tools (PastView). The project will enable to specify the epidemiological situation of these areas, analyze PUUV strains diversity and understand the links between PUUV epidemiology and evolutionary history