A likelihood framework to analyse phyletic patterns

Abstract

Probabilistic evolutionary models revolutionized our capability to extract biological insights from sequence data. While these models accurately describe the stochastic processes of site-specific substitutions, single-base substitutions represent only a fraction of all the events that shape genomes. Specifically, in microbes, events in which entire genes are gained (e.g. via horizontal gene transfer) and lost play a pivotal evolutionary role. In this research, we present a novel likelihood-based evolutionary model for gene gains and losses, and use it to analyse genome-wide patterns of the presence and absence of gene families. The model assumes a Markovian stochastic process, where gains and losses are represented by the transition between presence and absence, respectively, given an underlying phylogenetic tree. To account for differences in the rates of gain and loss of different gene families, we assume among-gene family rate variability, thus allowing for more accurate description of the data. Using the Bayesian approach, we estimated an evolutionary rate for each gene family. Simulation studies demonstrated that our methodology accurately infers these rates. Our methodology was applied to analyse a large corpus of data, consisting of 4873 gene families spanning 63 species and revealed novel insights regarding the evolutionary nature of genome-wide gain and loss dynamics.

Inferring Bacterial Genome Flux While Considering Truncated Genes

“…and the model is equivalent to that used in Hao andGolding (2006, 2008b) and Cohen et al (2008). This model is labeled M 00 in Table 3.…”

“…which has been used in previous studies (Cohen et al 2008;Spencer and Sangaralingam 2009;Cohen and Pupko 2010). When p a ¼ p p ¼ 0.5, the matrix entries Q(a, p) and Q(p, a) ¼ 1 (see Equation 3 below for detail), or…”

“…This is called model M 0 . In the case of only two characters (a, p), model M 0 is when p a and p p are the frequencies of each character state in the data, and another model, in which p a and p p ¼ 0.5, was introduced (called model M 00 ) since it has been used in previous studies (Hao andGolding 2006, 2008b;Cohen et al 2008). Then p a , p f , and p p were treated as parameters to be optimized and hence called model M 0 1 p. Finally, a discrete G-distribution was incorporated (model M 0 1 G 1 p).…”

“…Various methods have been employed to study the variation of gene content in the form of gene insertions/deletions (or gene gains/losses); there are studies of population dynamics (Nielsen and Townsend 2004), birth-anddeath evolutionary models (Berg and Kurland 2002;Novozhilov et al 2005), phylogeny-dependent studies including parsimony methods (Mirkin et al 2003;Daubin et al 2003a,b;Hao and Golding 2004), and maximum-likelihood methods (Hao andGolding 2006, 2008b;Cohen et al 2008;Cohen and Pupko 2010;Spencer and Sangaralingam 2009). The pattern of gene presence/absence also contains phylogenetic signals (Fitz-Gibbon and House 1999;Snel et al 1999;Tekaia et al 1999) and has been used for phylogenetic reconstruction (Dutilh et al 2004;Gu and Zhang 2004;Huson and Steel 2004;Zhang and Gu 2004;Spencer et al 2007a,b).…”

The evolution of an ancient metazoan biomineralisation strategy was supported by a horizontal gene transfer

“…This indeed was the case for the Spherulin gene within the A. willeyana genome. Other approaches, such as identifying features unique to eukaryotic genes (such as the presence of spliceosomal introns and polyadenylation signals), and the phylogenetic distribution of the gene, 33,34 can overcome these difficulties. The Spherulin HGT is a clear example of the transfer of genetic material from a prokaryote into a metazoan genome.…”