Species delimitation in the cyanolichen genus Rostania

Background In this study, we investigate species limits in the cyanobacterial lichen genus Rostania (Collemataceae, Peltigerales, Lecanoromycetes). Four molecular markers (mtSSU rDNA, β-tubulin, MCM7, RPB2) were sequenced and analysed with two coalescent-based species delimitation methods: the Generalized Mixed Yule Coalescent model (GMYC) and a Bayesian species delimitation method (BPP) using a multispecies coalescence model (MSC), the latter with or without an a priori defined guide tree. Results Species delimitation analyses indicate the presence of eight strongly supported candidate species. Conclusive correlation between morphological/ecological characters and genetic delimitation could be found for six of these. Of the two additional candidate species, one is represented by a single sterile specimen and the other currently lacks morphological or ecological supporting evidence. Conclusions We conclude that Rostania includes a minimum of six species: R. ceranisca, R. multipunctata, R. occultata 1, R. occultata 2, R. occultata 3, and R. occultata 4,5,6. Three distinct Nostoc morphotypes occur in Rostania, and there is substantial correlation between these morphotypes and Rostania thallus morphology.

Unblended Disjoint Tree Merging using GTM improves species tree estimation

Phylogeny estimation is an important part of much biological research, but large-scale tree estimation is infeasible using standard methods due to computational issues. Recently, an approach to large-scale phylogeny has been proposed that divides a set of species into disjoint subsets, computes trees on the subsets, and then merges the trees together using a computed matrix of pairwise distances between the species. The novel component of these approaches is the last step: Disjoint Tree Merger (DTM) methods. We present GTM (Guide Tree Merger), a polynomial time DTM method that adds edges to connect the subset trees, so as to provably minimize the topological distance to a computed guide tree. Thus, GTM performs unblended mergers, unlike the previous DTM methods. Yet, despite the potential limitation, our study shows that GTM has excellent accuracy, generally matching or improving on two previous DTMs, and is much faster than both. Thus, the GTM approach to the DTM problem is a useful new tool for large-scale phylogenomic analysis, and shows the surprising potential for unblended DTM methods. The software for GTM is available at https://github.com/vlasmirnov/GTM.

StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees

Background Fast, accurate and high-throughput identification of bacterial isolates is in great demand. The present work was conducted to investigate the possibility of identifying isolates from unassembled next-generation sequencing reads using custom-made guide trees. Results A tool named StrainSeeker was developed that constructs a list of specific k-mers for each node of any given Newick-format tree and enables the identification of bacterial isolates in 1–2 min. It uses a novel algorithm, which analyses the observed and expected fractions of node-specific k-mers to test the presence of each node in the sample. This allows StrainSeeker to determine where the isolate branches off the guide tree and assign it to a clade whereas other tools assign each read to a reference genome. Using a dataset of 100 Escherichia coli isolates, we demonstrate that StrainSeeker can predict the clades of E. coli with 92% accuracy and correct tree branch assignment with 98% accuracy. Twenty-five thousand Illumina HiSeq reads are sufficient for identification of the strain. Conclusion StrainSeeker is a software program that identifies bacterial isolates by assigning them to nodes or leaves of a custom-made guide tree. StrainSeeker’s web interface and pre-computed guide trees are available at http://bioinfo.ut.ee/strainseeker. Source code is stored at GitHub: https://github.com/bioinfo-ut/StrainSeeker.

Co-estimation of Phylogeny-aware Alignment and Phylogenetic Tree

The phylogeny-aware alignment algorithm implemented in both PRANK and PAGAN has been found to produce highly accurate alignments for comparative sequence analysis. However, the algorithm’s reliance on a guide tree during the alignment process can bias the resulting alignment rendering it unsuitable for phylogenetic inference. To overcome these issues, we have developed a new tool, Canopy, for parallelized iterative search of optimal alignment. Using Canopy, we studied the impact of the guide tree as well as the number and relative divergence of sequences on the accuracy of the alignment and inferred phylogeny. We find that PAGAN is the more robust of the two phylogeny-aware alignment methods to errors in the guide tree, but Canopy largely resolves the guide tree-related biases in PRANK. We demonstrate that, for all experimental settings tested, Canopy produces the most accurate sequence alignments and, further, that the inferred phylogenetic trees are of comparable accuracy to those obtained with the leading alternative method, SATé. Our analyses also show that, unlike traditional alignment algorithms, the phylogeny-aware algorithm effectively uses the information from denser sequence sampling and produces more accurate alignments when additional closely-related sequences are included. All methods are available for download at http://wasabiapp.org/software.

Using a Bio-Inspired Algorithm to Resolve the Multiple Sequence Alignment Problem

One of the most challenging tasks in bioinformatics is the resolution of Multiple Sequence Alignment (MSA) problem. It consists in comparing a set of protein or DNA sequences, in aim of predicting their structure and function. This paper introduces a new bio-inspired approach to solve such problem. This approach named BA-MSA is based on Bat Algorithm. Bat Algorithm (BA) is a recent evolutionary algorithm inspired from Bats behavior seeking their prey. The proposed approach includes new mechanism to generate initial population. It consists in generating a guide tree for each solution with progressive approach by varying some parameters. The generated guide tree will be enhanced by Hill-Climbing algorithm. In addition, to deal with the premature convergence of BA, a new restart technique is proposed to introduce more diversification when detecting premature convergence. Balibase 2.0 datasets are used for experiments. The comparison with well-known methods as MSA-GA MSA-GA (w\prealign), ClustalW, and SAGA and recent method (BBOMP) shows the effectiveness of the proposed approach.

Uprooting phylogenetic uncertainty in coalescent species delimitation: A meta-analysis of empirical studies

Phylogenetic and phylogeographic studies rely on the accurate quantification of biodiversity. In recent studies of taxonomically ambiguous groups, species boundaries are often determined based on multi-locus sequence data. Bayesian Phylogenetics and Phylogeography (BPP) is a coalescent-based method frequently used to delimit species; however, empirical studies suggest that the requirement of a user-specified guide tree biases the range of possible outcomes. We evaluate fifteen multi-locus datasets using the most recent iteration of BPP, which eliminates the need for a user-specified guide tree and reconstructs the species tree in synchrony with species delimitation (= unguided species delimitation). We found that the number of species recovered with guided versus unguided species delimitation was the same except for two cases, and that posterior probabilities were generally lower for the unguided analyses as a result of searching across species trees in addition to species delimitation models. The guide trees used in previous studies were often discordant with the species tree topologies estimated by BPP. We also compared species trees estimated using BPP and *BEAST and found that when the topologies are the same, BPP tends to give higher posterior probabilities.