PhyInformR: phylogenetic experimental design and phylogenomic data exploration in R

Abstract

BackgroundAnalyses of phylogenetic informativeness represent an important step in screening potential or existing datasets for their proclivity toward convergent or parallel evolution of molecular sites. However, while new theory has been developed from which to predict the utility of sequence data, adoption of these advances have been stymied by a lack of software enabling application of advances in theory, especially for large next-generation sequence data sets. Moreover, there are no theoretical barriers to application of the phylogenetic informativeness or the calculation of quartet internode resolution probabilities in a Bayesian setting that more robustly accounts for uncertainty, yet there is no software with which a computationally intensive Bayesian approach to experimental design could be implemented.ResultsWe introduce PhyInformR, an open source software package that performs rapid calculation of phylogenetic information content using the latest advances in phylogenetic informativeness based theory. These advances include modifications that incorporate uneven branch lengths and any model of nucleotide substitution to provide assessments of the phylogenetic utility of any given dataset or dataset partition. PhyInformR provides new tools for data visualization and routines optimized for rapid statistical calculations, including approaches making use of Bayesian posterior distributions and parallel processing. By implementing the computation on user hardware, PhyInformR increases the potential power users can apply toward screening datasets for phylogenetic/genomic information content by orders of magnitude.ConclusionsPhyInformR provides a means to implement diverse substitution models and specify uneven branch lengths for phylogenetic informativeness or calculations providing quartet based probabilities of resolution, produce novel visualizations, and facilitate analyses of next-generation sequence datasets while incorporating phylogenetic uncertainty through the use parallel processing. As an open source program, PhyInformR is fully customizable and expandable, thereby allowing for advanced methodologies to be readily integrated into local bioinformatics pipelines.Software is available through CRAN and a package containing the software, a detailed manual, and additional sample data is also provided freely through github: https://github.com/carolinafishes/PhyInformR.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-016-0837-3) contains supplementary material, which is available to authorized users.

An integrated pathway for building regional phylogenies for ecological studies

“…These are all common problems in super‐matrix approaches, and they can be exacerbated in studies that focus on a regional species pool (Betancur‐R, Li, et al, ; Heath et al, ; Phillippe et al, ). Although current developments in phylogenetics and phylogenomics do attempt to address these issues (Betancur‐R, Li, et al, ; Dornburg, Fisk, Tamagnan, & Townsend, ; Dornburg et al, ; Simion et al, ), there is still no consensus approach (Phillippe et al, ), especially when attempting to resolve both recent and deep phylogenetic relationships. Our novel approach facilitates using topological constraints based on previous phylogenetic studies, yet still allows unconstrained taxa to vary according to their molecular affinities and helps position taxa without sequence information within defined taxonomic constraints.…”

“…These are all common problems in supermatrix approaches, and they can be exacerbated in studies that focus on a regional species pool Heath et al, 2008;Phillippe et al, 2011). Although current developments in phylogenetics and phylogenomics do attempt to address these issues Dornburg, Fisk, Tamagnan, & (Rangel et al, 2015). Phylogenies represent hypothetical evolutionary relationships among taxa that ultimately depend on taxonomic sampling, the nature and extent of available genetic information, a host of particular model assumptions, and the strength of genuine underlying biological signals (Heath et al, 2008;Phillipe et al, 2011;Rangel et al, 2015).…”

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