Incorporating indel information into phylogeny estimation for rapidly emerging pathogens

Abstract

Background: Phylogenies of rapidly evolving pathogens can be difficult to resolve because of the small number of substitutions that accumulate in the short times since divergence. To improve resolution of such phylogenies we propose using insertion and deletion (indel) information in addition to substitution information. We accomplish this through joint estimation of alignment and phylogeny in a Bayesian framework, drawing inference using Markov chain Monte Carlo. Joint estimation of alignment and phylogeny sidesteps biases that stem from conditioning on a single alignment by taking into account the ensemble of near-optimal alignments.

Ostreid herpesvirus type 1 genomic diversity in wild populations of Pacific oyster Crassostrea gigas from Italian coasts

“…Given these circumstances, we included the indel as it improved analysis resolution. The use of sophisticated algorithms for alignment and phylogenetical analysis, adapted to take into account DNA-virus evolution, would be necessary in order to obtain accurate results with the correct phylogenetic distances (Nagy et al, 2012;Redelings and Suchard, 2007). Interestingly, the amplification of this region in the specimens from Capoiale never succeeded, suggesting the presence of a mutation, whereas the other markers belonged to the lVar cluster.…”

The Hymenopteran Tree of Life: Evidence from Protein-Coding Genes and Objectively Aligned Ribosomal Data

“…As an alternative approach, we used the program BAli-Phy [19,21] with a model of indel evolution that takes branch lengths into account [27] to obtain MAP (maximum posterior probability) subalignments of subsets of taxa that were later pieced together into a complete alignment. We split our data in four different taxon sets.…”

First report of the pantropical species Diploschistes rampoddensis from Europe

“…In this case, the nrITS dataset was divided into three partitions (ITS1, 5.8S, and ITS2) as suggested in Gaya et al (2011). The analyses were run under the following settings: ITS1 and ITS2 sharing the same model of evolution (HKY+G), indel model (RS07; Redelings & Suchard 2007) and branch length estimation, while the EQU model and separate branch length estimation were applied to the partition 5.8S, which was fixed because the alignment was unambiguous. We ran 6 independent Markov chains of 50,000 iterations each, sampling the Markov chains every 10 iterations.…”