P2C2M.GMYC: An R package for assessing the utility of the Generalized Mixed Yule Coalescent model

Abstract Summary MSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting and its network analog. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package implements the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model. Availability MSCquartets is available through the Comprehensive R Archive Network: https://CRAN.R-project.org/package=MSCquartets. Supplementary information Supplementary materials, including example data and analyses, are incorporated into the package.

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The bounded coalescent model: conditioning a genealogy on a minimum root date

10.1101/2022.01.04.474909 ◽

2022 ◽

Author(s):

Jake Carson ◽

Alice Ledda ◽

Luca Ferretti ◽

Matt Keeling ◽

Xavier Didelot

Keyword(s):

Horizontal Gene Transfer ◽

Common Ancestor ◽

R Package ◽

Last Common Ancestor ◽

Simulation Algorithm ◽

Direct Simulation ◽

Rejection Sampling ◽

Computationally Efficient ◽

Coalescent Model ◽

Sampling Approach

The coalescent model represents how individuals sampled from a population may have originated from a last common ancestor. The bounded coalescent model is obtained by conditioning the coalescent model such that the last common ancestor must have existed after a certain date. This conditioned model arises in a variety of applications, such as speciation, horizontal gene transfer or transmission analysis, and yet the bounded coalescent model has not been previously analysed in detail. Here we describe a new algorithm to simulate from this model directly, without resorting to rejection sampling. We show that this direct simulation algorithm is more computationally efficient than the rejection sampling approach. We also show how to calculate the probability of the last common ancestor occurring after a given date, which is required to compute the probability of realisations under the bounded coalescent model. Our results are applicable in both the isochronous (when all samples have the same date) and heterochronous (where samples can have different dates) settings. We explore the effect of setting a bound on the date of the last common ancestor, and show that it affects a number of properties of the resulting phylogenies. All our methods are implemented in a new R package called BoundedCoalescent which is freely available online.

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Identifying model violations under the multispecies coalescent model using P2C2M.SNAPP

PeerJ ◽

10.7717/peerj.8271 ◽

2020 ◽

Vol 8 ◽

pp. e8271

Author(s):

Drew J. Duckett ◽

Tara A. Pelletier ◽

Bryan C. Carstens

Keyword(s):

Posterior Distribution ◽

Incomplete Lineage Sorting ◽

R Package ◽

Predictive Distribution ◽

Species Trees ◽

Lineage Sorting ◽

Posterior Predictive Distribution ◽

Coalescent Model ◽

Multispecies Coalescent ◽

Phylogenetic Estimation

Phylogenetic estimation under the multispecies coalescent model (MSCM) assumes all incongruence among loci is caused by incomplete lineage sorting. Therefore, applying the MSCM to datasets that contain incongruence that is caused by other processes, such as gene flow, can lead to biased phylogeny estimates. To identify possible bias when using the MSCM, we present P2C2M.SNAPP. P2C2M.SNAPP is an R package that identifies model violations using posterior predictive simulation. P2C2M.SNAPP uses the posterior distribution of species trees output by the software package SNAPP to simulate posterior predictive datasets under the MSCM, and then uses summary statistics to compare either the empirical data or the posterior distribution to the posterior predictive distribution to identify model violations. In simulation testing, P2C2M.SNAPP correctly classified up to 83% of datasets (depending on the summary statistic used) as to whether or not they violated the MSCM model. P2C2M.SNAPP represents a user-friendly way for researchers to perform posterior predictive model checks when using the popular SNAPP phylogenetic estimation program. It is freely available as an R package, along with additional program details and tutorials.

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MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

10.1101/2020.05.01.073361 ◽

2020 ◽

Author(s):

John A. Rhodes ◽

Hector Baños ◽

Jonathan D. Mitchell ◽

Elizabeth S. Allman

Keyword(s):

Network Inference ◽

Incomplete Lineage Sorting ◽

R Package ◽

Species Tree ◽

Species Trees ◽

Lineage Sorting ◽

Coalescent Model ◽

Multispecies Coalescent ◽

Tree Inference ◽

Species Tree Inference

AbstractMSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package includes the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model.

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Marine halogenated compound analysis: from an R package to the isolation of new griseophenone derivatives

Planta Medica ◽

10.1055/s-0036-1596648 ◽

2016 ◽

Vol 81 (S 01) ◽

pp. S1-S381

Author(s):

C Roullier ◽

Y Guitton ◽

S Prado ◽

O Grovel ◽

YF Pouchus

Keyword(s):

R Package ◽

Halogenated Compound

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The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

10.32942/osf.io/epqa7 ◽

2019 ◽

Author(s):

Shinichi Nakagawa ◽

Malgorzata Lagisz ◽

Rose E O'Dea ◽

Joanna Rutkowska ◽

Yefeng Yang ◽

...

Keyword(s):

Meta Analysis ◽

R Package ◽

Effect Sizes ◽

Forest Plot ◽

Point Estimates ◽

Aggregate Effect ◽

The Individual ◽

Meta Analyses ◽

Heterogeneous Effect ◽

Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

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Faculty Opinions recommendation of TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725335383.793503769 ◽

2015 ◽

Author(s):

Zdeněk Valenta

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

R Package ◽

Cancer Cell Lines ◽

Drug Combinations ◽

Targeted Drug

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Systematics, Biogeography, and Morphological Character Evolution of the Hemiepiphytic Subfamily Monsteroideae (Araceae)

Annals of the Missouri Botanical Garden ◽

10.3417/2018269 ◽

2019 ◽

Vol 104 (1) ◽

pp. 33-48 ◽

Cited By ~ 4

Author(s):

Alejandro Zuluaga ◽

Martin Llano ◽

Ken Cameron

Keyword(s):

Dna Sequences ◽

Pacific Islands ◽

R Package ◽

Morphological Characters ◽

Ancestral State ◽

Seed Shape ◽

Long Distance ◽

The Pacific ◽

And Migration ◽

The Tropics

The subfamily Monsteroideae (Araceae) is the third richest clade in the family, with ca. 369 described species and ca. 700 estimated. It comprises mostly hemiepiphytic or epiphytic plants restricted to the tropics, with three intercontinental disjunctions. Using a dataset representing all 12 genera in Monsteroideae (126 taxa), and five plastid and two nuclear markers, we studied the systematics and historical biogeography of the group. We found high support for the monophyly of the three major clades (Spathiphylleae sister to Heteropsis Kunth and Rhaphidophora Hassk. clades), and for six of the genera within Monsteroideae. However, we found low rates of variation in the DNA sequences used and a lack of molecular markers suitable for species-level phylogenies in the group. We also performed ancestral state reconstruction of some morphological characters traditionally used for genera delimitation. Only seed shape and size, number of seeds, number of locules, and presence of endosperm showed utility in the classification of genera in Monsteroideae. We estimated ancestral ranges using a dispersal-extinction-cladogenesis model as implemented in the R package BioGeoBEARS and found evidence for a Gondwanan origin of the clade. One tropical disjunction (Monstera Adans. sister to Amydrium Schott–Epipremnum Schott) was found to be the product of a previous Boreotropical distribution. Two other disjunctions are more recent and likely due to long-distance dispersal: Spathiphyllum Schott (with Holochlamys Engl. nested within) represents a dispersal from South America to the Pacific Islands in Southeast Asia, and Rhaphidophora represents a dispersal from Asia to Africa. Future studies based on stronger phylogenetic reconstructions and complete morphological datasets are needed to explore the details of speciation and migration within and among areas in Asia.

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