scholarly journals DISSECT: an assignment-free Bayesian discovery method for species delimitation under the multispecies coalescent

2014 ◽  
Author(s):  
Graham Jones ◽  
Bengt Oxelman
Keyword(s):  
Simulated Data ◽  
Degree Of Approximation ◽  
Species Tree ◽  
Supplementary Information ◽  
Data Set ◽  
Multispecies Coalescent ◽  
Formal Framework ◽  
Beast Analysis ◽  
Tree Topologies ◽  
Discovery Method

Motivation: The multispecies coalescent model provides a formal framework for the assignment of individual organisms to species, where the species are modeled as the branches of the species tree. None of the available approaches so far have simultaneously co-estimated all the relevant parameters in the model, without restricting the parameter space by requiring a guide tree and/or prior assignment of individuals to clusters or species. Results: We present DISSECT, which explores the full space of possible clusterings of individuals and species tree topologies in a Bayesian framework. It uses an approximation to avoid the need for reversible-jump MCMC, in the form of a prior that is a modification of the birth-death prior for the species tree. It incorporates a spike near zero in the density for node heights. The model has two extra parameters: one controls the degree of approximation, and the second controls the prior distribution on the numbers of species. It is implemented as part of BEAST and requires only a few changes from a standard *BEAST analysis. The method is evaluated on simulated data and demonstrated on an empirical data set. The method is shown to be insensitive to the degree of approximation, but quite sensitive to the second parameter, suggesting that large numbers of sequences are needed to draw firm conclusions. Availability:http://code.google.com/p/beast-mcmc/, http://www.indriid.com/dissectinbeast.html Contact:[email protected], www.indriid.com Supplementary information: Supplementary material is available.

scholarly journals Divergence estimation in the presence of incomplete lineage sorting and migration

2017 ◽  
Author(s):  
Graham Jones
Keyword(s):  
Incomplete Lineage Sorting ◽  
Simulated Data ◽  
Species Tree ◽  
Lineage Sorting ◽  
Data Set ◽  
Migration Rates ◽  
Isolation With Migration ◽  
Multispecies Coalescent ◽  
Tree Inference ◽  
And Migration

AbstractThis paper focuses on the problem of estimating a species tree from multilocus data in the presence of incomplete lineage sorting and migration. We develop a mathematical model similar to IMa2 (Hey 2010) for the relevant evolutionary processes which allows both the the population size parameters and the migration rates between pairs of species tree branches to be integrated out. We then describe a BEAST2 package DENIM which based on this model, and which uses an approximation to sample from the posterior. The approximation is based on the assumption that migrations are rare, and it only samples from certain regions of the posterior which seem likely given this assumption. The method breaks down if there is a lot of migration. Using simulations, Leaché et al 2014 showed migration causes problems for species tree inference using the multispecies coalescent when migration is present but ignored. We re-analyze this simulated data to explore DENIM’s performance, and demonstrate substantial improvements over *BEAST. We also re-analyze an empirical data set. [isolation-with-migration; incomplete lineage sorting; multispecies coalescent; species tree; phylogenetic analysis; Bayesian; Markov chain Monte Carlo]

scholarly journals PRANC: ML species tree estimation from the ranked gene trees under coalescence

2020 ◽  
Vol 36 (18) ◽  
pp. 4819-4821
Author(s):  
Anastasiia Kim ◽  
James H Degnan
Keyword(s):  
Maximum Likelihood ◽  
Gene Tree ◽  
Species Tree ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Gene Trees ◽  
Multispecies Coalescent ◽  
Branch Lengths ◽  
Tree Topologies ◽  
Tree Estimation

Abstract Summary PRANC computes the Probabilities of RANked gene tree topologies under the multispecies coalescent. A ranked gene tree is a gene tree accounting for the temporal ordering of internal nodes. PRANC can also estimate the maximum likelihood (ML) species tree from a sample of ranked or unranked gene tree topologies. It estimates the ML tree with estimated branch lengths in coalescent units. Availability and implementation PRANC is written in C++ and freely available at github.com/anastasiiakim/PRANC. Supplementary information Supplementary data are available at Bioinformatics online.

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

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 ◽  
Supplementary Information ◽  
Species Trees ◽  
Lineage Sorting ◽  
Coalescent Model ◽  
Multispecies Coalescent ◽  
Tree Inference

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.

scholarly journals Probabilities of Unranked and Ranked Anomaly Zones under Birth–Death Models

2019 ◽  
Vol 37 (5) ◽  
pp. 1480-1494 ◽  
Author(s):  
Anastasiia Kim ◽  
Noah A Rosenberg ◽  
James H Degnan
Keyword(s):  
Gene Tree ◽  
Species Tree ◽  
Tree Topology ◽  
Death Process ◽  
Gene Trees ◽  
Species Trees ◽  
Multispecies Coalescent ◽  
Speciation Rates ◽  
Tree Topologies ◽  
Birth Death

Abstract A labeled gene tree topology that is more probable than the labeled gene tree topology matching a species tree is called “anomalous.” Species trees that can generate such anomalous gene trees are said to be in the “anomaly zone.” Here, probabilities of “unranked” and “ranked” gene tree topologies under the multispecies coalescent are considered. A ranked tree depicts not only the topological relationship among gene lineages, as an unranked tree does, but also the sequence in which the lineages coalesce. In this article, we study how the parameters of a species tree simulated under a constant-rate birth–death process can affect the probability that the species tree lies in the anomaly zone. We find that with more than five taxa, it is possible for species trees to have both anomalous unranked and ranked gene trees. The probability of being in either type of anomaly zone increases with more taxa. The probability of anomalous gene trees also increases with higher speciation rates. We observe that the probabilities of unranked anomaly zones are higher and grow much faster than those of ranked anomaly zones as the speciation rate increases. Our simulation shows that the most probable ranked gene tree is likely to have the same unranked topology as the species tree. We design the software PRANC, which computes probabilities of ranked gene tree topologies given a species tree under the coalescent model.

scholarly journals Consistency and identifiability of the polymorphism-aware phylogenetic models

2019 ◽  
Author(s):  
Rui Borges ◽  
Carolin Kosiol
Keyword(s):  
Incomplete Lineage Sorting ◽  
Simulated Data ◽  
Population Level ◽  
Species Tree ◽  
Population Variation ◽  
Necessary Condition ◽  
Lineage Sorting ◽  
Data Set ◽  
Genome Wide ◽  
Phylogenetic Models

AbstractPolymorphism-aware phylogenetic models (PoMo) constitute an alternative approach for species tree estimation from genome-wide data. PoMo builds on the standard substitution models of DNA evolution but expands the classic alphabet of the four nucleotide bases to include polymorphic states. By doing so, PoMo accounts for ancestral and current intra-population variation, while also accommodating population-level processes ruling the substitution process (e.g. genetic drift, mutations, allelic selection). PoMo has shown to be a valuable tool in several phylogenetic applications but a proof of statistical consistency (and identifiability, a necessary condition for consistency) is lacking. Here, we prove that PoMo is identifiable and, using this result, we further show that the maximum a posteriori (MAP) tree estimator of PoMo is a consistent estimator of the species tree. We complement our theoretical results with a simulated data set mimicking the diversity observed in natural populations exhibiting incomplete lineage sorting. We implemented PoMo in a Bayesian framework and show that the MAP tree easily recovers the true tree for typical numbers of sites that are sampled in genome-wide analyses.

Evaluation of gene–drug common module identification methods using pharmacogenomics data

2020 ◽  
Author(s):  
Jie Huang ◽  
Jiazhou Chen ◽  
Bin Zhang ◽  
Lei Zhu ◽  
Hongmin Cai
Keyword(s):  
Drug Interactions ◽  
Drug Repositioning ◽  
Simulated Data ◽  
Supplementary Information ◽  
Supplementary File ◽  
Cancer Drugs ◽  
Real World Data ◽  
Data Set ◽  
Module Identification ◽  
Network Analyses

Abstract Accurately identifying the interactions between genomic factors and the response of cancer drugs plays important roles in drug discovery, drug repositioning and cancer treatment. A number of studies revealed that interactions between genes and drugs were ‘many-genes-to-many drugs’ interactions, i.e. common modules, opposed to ‘one-gene-to-one-drug’ interactions. Such modules fully explain the interactions between complex biological regulatory mechanisms and cancer drugs. However, strategies for effectively and robustly identifying the underlying common modules among pharmacogenomics data remain to be improved. In this paper, we aim to provide a detailed evaluation of three categories of state-of-the-art common module identification techniques from a machine learning perspective, including non-negative matrix factorization (NMF), partial least squares (PLS) and network analyses. We first evaluate the performance of six methods, namely SNMNMF, NetNMF, SNPLS, O2PLS, NSBM and HOGMMNC, using two series of simulated data sets with different noise levels and outlier ratios. Then, we conduct experiments using a real world data set of 2091 genes and 101 drugs in 392 cancer cell lines and compare the real experimental results from the aspect of biological process term enrichment, gene–drug and drug–drug interactions. Finally, we present interesting findings from our evaluation study and discuss the advantages and drawbacks of each method. Supplementary information: Supplementary file is available at Briefings in Bioinformatics online.

scholarly journals Species delimitation and phylogeny estimation under the multispecies coalescent

2014 ◽  
Author(s):  
Graham R Jones
Keyword(s):  
Dna Sequences ◽  
Species Delimitation ◽  
A Priori ◽  
Simulated Data ◽  
Species Trees ◽  
Data Set ◽  
Multispecies Coalescent ◽  
Multiple Loci ◽  
Population Sizes ◽  
Phylogeny Estimation

This article describes a Bayesian method for inferring both species delimitations and species trees under the multispecies coalescent model using DNA sequences from multiple loci. The focus here is on species delimitation with no a priori assignment of individuals to species, and no guide tree. The method uses a new model for the population sizes along the branches of the species tree, and three new operators for sampling from the posterior using the Markov chain Monte Carlo (MCMC) algorithm. The correctness of the moves is demonstrated both by proofs and by tests of the implementation. Current practice, using a pipeline approach to species delimitation under the multispecies coalescent, has been shown to have major problems on simulated data (Olave et al, 2014). The same simulated data set is used to demonstrate the accuracy and efficiency of the present method. The method is implemented in a package called STACEY for BEAST2.

scholarly journals Effects of management decisions on genetic evaluation of simulated calving records using random regression

2021 ◽  
Author(s):  
M D MacNeil ◽  
J W Buchanan ◽  
M L Spangler ◽  
E Hay
Keyword(s):  
Reproductive Success ◽  
Simulated Data ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Management Decisions ◽  
Third Order ◽  
Data Set ◽  
Binary Phenotype ◽  
Random Regression Model ◽  
Missing Observation

Abstract The objective of this study was to evaluate the effects of various data structures on the genetic evaluation for the binary phenotype of reproductive success. The data were simulated based on an existing pedigree and an underlying fertility phenotype with a heritability of 0.10. A data set of complete observations was generated for all cows. This data set was then modified mimicking the culling of cows when they first failed to reproduce, cows having a missing observation at either their second or fifth opportunity to reproduce as if they had been selected as donors for embryo transfer, and censoring records following the sixth opportunity to reproduce as in a cull-for-age strategy. The data were analyzed using a third order polynomial random regression model. The EBV of interest for each animal was the sum of the age-specific EBV over the first 10 observations (reproductive success at ages 2-11). Thus, the EBV might be interpreted as the genetic expectation of number of calves produced when a female is given ten opportunities to calve. Culling open cows resulted in the EBV for 3 year-old cows being reduced from 8.27 ± 0.03 when open cows were retained to 7.60 ± 0.02 when they were culled. The magnitude of this effect decreased as cows grew older when they first failed to reproduce and were subsequently culled. Cows that did not fail over the 11 years of simulated data had an EBV of 9.43 ± 0.01 and 9.35 ± 0.01 based on analyses of the complete data and the data in which cows that failed to reproduce were culled, respectively. Cows that had a missing observation for their second record had a significantly reduced EBV, but the corresponding effect at the fifth record was negligible. The current study illustrates that culling and management decisions, and particularly those that impact the beginning of the trajectory of sustained reproductive success, can influence both the magnitude and accuracy of resulting EBV.

scholarly journals A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592095492
Author(s):  
Marco Del Giudice ◽  
Steven W. Gangestad
Keyword(s):  
Degrees Of Freedom ◽  
A Priori ◽  
Simulated Data ◽  
Style Analysis ◽  
Data Set ◽  
Scant Attention ◽  
Equivalence Type ◽  
The Impact ◽  
Biased Estimates

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.

scholarly journals TreeMerge: a new method for improving the scalability of species tree estimation methods

2019 ◽  
Vol 35 (14) ◽  
pp. i417-i426 ◽  
Author(s):  
Erin K Molloy ◽  
Tandy Warnow
Keyword(s):  
Large Scale ◽  
Species Tree ◽  
New Method ◽  
Divide And Conquer ◽  
Supplementary Information ◽  
Estimation Methods ◽  
Running Time ◽  
Tree Estimation ◽  
Computationally Intensive ◽  
A Minor

Abstract Motivation At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species. Results Here we present a new method called ‘TreeMerge’ that improves on NJMerge in two ways: it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework—only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. Availability and implementation TreeMerge is publicly available on Github (http://github.com/ekmolloy/treemerge). Supplementary information Supplementary data are available at Bioinformatics online.

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