Consensus of All Solutions for Intractable Phylogenetic Tree Inference

The United States Swine Pathogen Database: integrating veterinary diagnostic laboratory sequence data to monitor emerging pathogens of swine

Veterinary diagnostic laboratories derive thousands of nucleotide sequences from clinical samples of swine pathogens such as porcine reproductive and respiratory syndrome virus (PRRSV), Senecavirus A and swine enteric coronaviruses. In addition, next generation sequencing has resulted in the rapid production of full-length genomes. Presently, sequence data are released to diagnostic clients but are not publicly available as data may be associated with sensitive information. However, these data can be used for field-relevant vaccines; determining where and when pathogens are spreading; have relevance to research in molecular and comparative virology; and are a component in pandemic preparedness efforts. We have developed a centralized sequence database that integrates private clinical data using PRRSV data as an exemplar, alongside publicly available genomic information. We implemented the Tripal toolkit, a collection of Drupal modules that are used to manage, visualize and disseminate biological data stored within the Chado database schema. New sequences sourced from diagnostic laboratories contain: genomic information; date of collection; collection location; and a unique identifier. Users can download annotated genomic sequences using a customized search interface that incorporates data mined from published literature; search for similar sequences using BLAST-based tools; and explore annotated reference genomes. Additionally, custom annotation pipelines have determined species, the location of open reading frames and nonstructural proteins and the occurrence of putative frame shifts. Eighteen swine pathogens have been curated. The database provides researchers access to sequences discovered by veterinary diagnosticians, allowing for epidemiological and comparative virology studies. The result will be a better understanding on the emergence of novel swine viruses and how these novel strains are disseminated in the USA and abroad. Database URLhttps://swinepathogendb.org.

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.