Statistical inconsistency of the unrooted minimize deep coalescence criterion

Species trees, which describe the evolutionary relationships between species, are often inferred from gene trees, which describe the ancestral relationships between sequences sampled at different loci from the species of interest. A common approach to inferring species trees from gene trees is motivated by supposing that gene tree variation is due to incomplete lineage sorting, also known as deep coalescence. One of the earliest methods motivated by deep coalescence is to find the species tree that minimizes the number of deep coalescent events needed to explain discrepancies between the species tree and input gene trees. This minimize deep coalescence (MDC) criterion can be applied in both rooted and unrooted settings. where either rooted or unrooted gene trees can be used to infer a rooted species tree. Previous work has shown that MDC is statistically inconsistent in the rooted setting, meaning that under a probabilistic model for deep coalescence, the multispecies coalescent, for some species trees, increasing the number of input gene trees does not make the method more likely to return a correct species tree. Here, we obtain analogous results in the unrooted setting, showing conditions leading to inconsistency of the MDC criterion using the multispecies coalescent model with unrooted gene trees for four taxa and five taxa.

Exact median-tree inference for unrooted reconciliation costs

Background Solving median tree problems under tree reconciliation costs is a classic and well-studied approach for inferring species trees from collections of discordant gene trees. These problems are NP-hard, and therefore are, in practice, typically addressed by local search heuristics. So far, however, such heuristics lack any provable correctness or precision. Further, even for small phylogenetic studies, it has been demonstrated that local search heuristics may only provide sub-optimal solutions. Obviating such heuristic uncertainties are exact dynamic programming solutions that allow solving tree reconciliation problems for smaller phylogenetic studies. Despite these promises, such exact solutions are only suitable for credibly rooted input gene trees, which constitute only a tiny fraction of the readily available gene trees. Standard gene tree inference approaches provide only unrooted gene trees and accurately rooting such trees is often difficult, if not impossible. Results Here, we describe complex dynamic programming solutions that represent the first nonnaïve exact solutions for solving the tree reconciliation problems for unrooted input gene trees. Further, we show that the asymptotic runtime of the proposed solutions does not increase when compared to the most time-efficient dynamic programming solutions for rooted input trees. Conclusions In an experimental evaluation, we demonstrate that the described solutions for unrooted gene trees are, like the solutions for rooted input gene trees, suitable for smaller phylogenetic studies. Finally, for the first time, we study the accuracy of classic local search heuristics for unrooted tree reconciliation problems.

Inferring transcriptional regulators through integrative modeling of public chromatin accessibility and ChIP-seq data

We developed Lisa (http://lisa.cistrome.org) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses compendia of public histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Then using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes, and outperformed alternative methods in identifying the perturbed TRs.

Performance of Matrix Representation with Parsimony for Inferring Species from Gene Trees

Phylogenomic datasets often contain sequence alignments on different subsets of taxa for different genes. A major goal of phylogenetics is often to combine estimated gene trees from many loci into an overall estimate of a species tree. When data are missing for some combinations of genes and taxa, supertree methods can be used to combine gene trees on different subsets of taxa into an overall tree. However, studies of the performance of supertree methods when gene tree conflict is due to incomplete lineage sorting are needed to understand their statistical properties in this setting.We find that Matrix Representation with Parsimony (MRP), the most commonly used supertree method, can in many cases infer the species tree in spite of high levels of conflict in the input gene trees. However, for some species trees with short branches, MRP can be increasingly likely to return a tree other than the species tree as the number of loci increases. In some cases, deleting taxa at random or using estimated (rather than known) gene trees can either improve or hinder MRP for recovering the species tree.Although MRP is able to handle large amounts of conflict in the input gene trees, MRP is not statistically consistent for estimating species trees when gene trees arise under the multispecies coalescent model. However, triplet MRP is statistically consistent in this setting.