What fruits can we get from this tree?:A journey in phylogenetic inference through likelihood modeling

This thesis provides new tools for extracting information on the process of diversification from a phylogenetic tree. To do so the standard approach is to employ likelihood functions in order to estimate the best parameters for the diversification models via likelihood maximization. The parameters for this kind of models usually represent the diversification rates at which the various evolutionary events (e.g., speciations or extinctions) can take place in the process

Quantifying the impact of an inference model in Bayesian phylogenetics

Phylogenetic trees are currently routinely reconstructed from an alignment of character sequences (usually nucleotide sequences). Bayesian tools, such as MrBayes, RevBayes and BEAST2, have gained much popularity over the last decade, as they allow joint estimation of the posterior distribution of the phylogenetic trees and the parameters of the underlying inference model. An important ingredient of these Bayesian approaches is the species tree prior. In principle, the Bayesian framework allows for comparing different tree priors, which may elucidate the macroevolutionary processes underlying the species tree. In practice, however, only macroevolutionary models that allow for fast computation of the prior probability are used. The question is how accurate the tree estimation is when the real macroevolutionary processes are substantially different from those assumed in the tree prior. Here we present pirouette, a free and open-source r package that assesses the inference error made by Bayesian phylogenetics for a given macroevolutionary diversification model. pirouette makes use of BEAST2, but its philosophy applies to any Bayesian phylogenetic inference tool. We describe pirouette's usage providing full examples in which we interrogate a model for its power to describe another. Last, we discuss the results obtained by the examples and their interpretation

Hierarchical folding and reorganization of chromosomes are linked to transcriptional changes in cellular differentiation

Mammalian chromosomes fold into arrays of megabase‐sized topologically associating domains (TADs), which are arranged into compartments spanning multiple megabases of genomic DNA. TADs have internal substructures that are often cell type specific, but their higher‐order organization remains elusive. Here, we investigate TAD higher‐order interactions with Hi‐C through neuronal differentiation and show that they form a hierarchy of domains‐within‐domains (metaTADs) extending across genomic scales up to the range of entire chromosomes. We find that TAD interactions are well captured by tree‐like, hierarchical structures irrespective of cell type. metaTAD tree structures correlate with genetic, epigenomic and expression features, and structural tree rearrangements during differentiation are linked to transcriptional state changes. Using polymer modelling, we demonstrate that hierarchical folding promotes efficient chromatin packaging without the loss of contact specificity, highlighting a role far beyond the simple need for packing efficiency

Additional Analytical Support for a New Method to Compute the Likelihood of Diversification Models

International audienceMolecular phylogenies have been increasingly recognized as an important source of information on species diversification. For many models of macroevolution, analytical likelihood formulas have been derived to infer macroevolutionary parameters from phylogenies. A few years ago, a general framework to numerically compute such likelihood formulas was proposed, which accommodates models that allow speciation and/or extinction rates to depend on diversity. This framework calculates the likelihood as the probability of the diversification process being consistent with the phylogeny from the root to the tips. However, while some readers found the framework presented in Etienne et al. (Proc R Soc Lond B Biol Sci 279(1732):1300–1309, 2012) convincing, others still questioned it (personal communication), despite numerical evidence that for special cases the framework yields the same (i.e., within double precision) numerical value for the likelihood as analytical formulas do that were independently derived for these special cases. Here we prove analytically that the likelihoods calculated in the new framework are correct for all special cases with known analytical likelihood formula. Our results thus add substantial mathematical support for the overall coherence of the general framework

DDD package for R:Diversity-Dependent Diversification

Calculates the likelihood of diversity-dependent diversification models for a given data set of branching times of a phylogenetic tree

Detecting Lineage-Specific Shifts in Diversification: A Proper Likelihood Approach

International audienceThe branching patterns of molecular phylogenies are generally assumed to contain 1 information on rates of the underlying speciation and extinction processes. Simple 2 birth-death models with constant, time-varying, or diversity-dependent rates have been 3 invoked to explain these patterns. They have one assumption in common: all lineages have 4 the same set of diversification rates at a given point in time. It seems likely, however, that 5 there is variability in diversification rates across subclades in a phylogenetic tree. This has 6 inspired the construction of models that allow multiple rate regimes across the phylogeny, 7 with instantaneous shifts between these regimes. Several methods exist for calculating the 8 likelihood of a phylogeny under a specified mapping of diversification regimes and for 9 performing inference on the most likely diversification history that gave rise to a particular 10 phylogenetic tree. Here we show that the likelihood computation of these methods is not 11 correct. We provide a new framework to compute the likelihood correctly and show, with 12 simulations of a single shift, that the correct likelihood indeed leads to parameter estimates 13 that are on average in much better agreement with the generating parameters than the 14

pirouette_examples

Data generated for the manuscript 'Quantifying the impact of an inference modelin Bayesian phylogenetics', Bilderbeek, Laudanno and Etienne, v1.3

The error in Bayesian phylogenetic reconstruction under diversification dynamics with simultaneous speciation events

Dataset for 'The error in Bayesian phylogenetic reconstruction under diversification dynamics with simultaneous speciation events', by Laudanno, Bilderbeek & Etienn

DAISIE: Dynamical Assembly of Islands by Speciation, Immigration and Extinction

Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See e.g. Valente et al. 2015. Ecology Letters 18: 844-852,