Counterexample to a claim about the reconstruction of ancestral character states

Evolutionary dynamics of overlapped genes in Salmonella

Presence of overlapping genes (OGs) is a common phenomenon in bacterial genomes. Most frequently, overlapping genes share coding regions with as few as one nucleotide to as many as thousands of nucleotides. Overlapping genes are often co-regulated, transcriptionally and translationally. Overlapping genes are also subject to the whims of evolution, as the gene overlap is known to be disrupted in some species/strains and participating genes are sometimes lost in independent lineages. Therefore, a better understanding of evolutionary patterns and rates of the disruption of overlapping genes is an important component of genome structure and evolution of gene function. In this study, we investigate the fate of ancestrally overlapping genes in complete genomes from 15 contemporary strains of Salmonella species. We find that the fates of overlapping genes inside and outside operons are distinctly different. A larger fraction of overlapping genes inside operons conserves their overlap as compared to gene pairs outside of the operons (average 0.89 vs. 0.83 per genome). However, when overlapping genes in the operons separate, one partner is lost more frequently than in those separated genes outside of operons (average 0.02 vs. 0.01 per genome). We also investigate the fate of a pan set of overlapping genes at the present and ancestral nodes over a phylogenetic tree based on genome sequence data, respectively. We propose that co-regulation plays important roles on the fates of genes. Furthermore, a vast majority of disruptions occurred prior to the common ancestor of all 15 Salmonella strains, which enables us to obtain an estimate of disruptions between Salmonella and E. coli.

Robustness of ancestral sequence reconstruction to phylogenetic uncertainty

Ancestral sequence reconstruction (ASR) is widely used to formulate and test hypotheses about the sequences, functions, and structures of ancient genes. Ancestral sequences are usually inferred from an alignment of extant sequences using a maximum likelihood (ML) phylogenetic algorithm, which calculates the most likely ancestral sequence assuming a probabilistic model of sequence evolution and a specific phylogeny—typically the tree with the ML. The true phylogeny is seldom known with certainty, however. ML methods ignore this uncertainty, whereas Bayesian methods incorporate it by integrating the likelihood of each ancestral state over a distribution of possible trees. It is not known whether Bayesian approaches to phylogenetic uncertainty improve the accuracy of inferred ancestral sequences. Here, we use simulation-based experiments under both simplified and empirically derived conditions to compare the accuracy of ASR carried out using ML and Bayesian approaches. We show that incorporating phylogenetic uncertainty by integrating over topologies very rarely changes the inferred ancestral state and does not improve the accuracy of the reconstructed ancestral sequence. Ancestral state reconstructions are robust to uncertainty about the underlying tree because the conditions that produce phylogenetic uncertainty also make the ancestral state identical across plausible trees; conversely, the conditions under which different phylogenies yield different inferred ancestral states produce little or no ambiguity about the true phylogeny. Our results suggest that ML can produce accurate ASRs, even in the face of phylogenetic uncertainty. Using Bayesian integration to incorporate this uncertainty is neither necessary nor beneficial.

Greedy selection of species for ancestral state reconstruction on phylogenies: elimination is better than insertion

Accurate reconstruction of ancestral character states on a phylogeny is crucial in many genomics studies. We study how to select species to achieve the best reconstruction of ancestral character states on a phylogeny. We first show that the marginal maximum likelihood has the monotonicity property that more taxa give better reconstruction, but the Fitch method does not have it even on an ultrametric phylogeny. We further validate a greedy approach for species selection using simulation. The validation tests indicate that backward greedy selection outperforms forward greedy selection. In addition, by applying our selection strategy, we obtain a set of the ten most informative species for the reconstruction of the genomic sequence of the so-called boreoeutherian ancestor of placental mammals. This study has broad relevance in comparative genomics and paleogenomics since limited research resources do not allow researchers to sequence the large number of descendant species required to reconstruct an ancestral sequence.

Computational reconstruction of ancestral DNA sequences

This chapter introduces the problem of ancestral sequence reconstruction: given a set of extant orthologous DNA genomic sequences (or even whole-genomes), together with a phylogenetic tree relating these sequences, predict the DNA sequence of all ancestral species in the tree. Blanchette et al. (1) have shown that for certain sets of species (in particular, for eutherian mammals), very accurate reconstruction can be obtained. We explain the main steps involved in this process, including multiple sequence alignment, insertion and deletion inference, substitution inference, and gene arrangement inference. We also describe a simulation-based procedure to assess the accuracy of the reconstructed sequences. The whole reconstruction process is illustrated using a set of mammalian sequences from the CFTR region.