Revising transcriptome assemblies with phylogenetic information.

A common transcriptome assembly error is to mistake different transcripts of the same gene as transcripts from multiple closely related genes. This error is difficult to identify during assembly, but in a phylogenetic analysis such errors can be diagnosed from gene phylogenies where they appear as clades of tips from the same species with improbably short branch lengths. treeinform is a method that uses phylogenetic information across species to refine transcriptome assemblies within species. It identifies transcripts of the same gene that were incorrectly assigned to multiple genes and reassign them as transcripts of the same gene. The treeinform method is implemented in Agalma, available at https://bitbucket.org/caseywdunn/agalma, and the general approach is relevant in a variety of other contexts.

Incorporating Within-Host Diversity in Phylogenetic Analyses for Detecting Clusters of New HIV Diagnoses.

BACKGROUND: Phylogenetic analyses of HIV sequences are used to detect clusters and inform public health interventions. Conventional approaches summarize within-host HIV diversity with a single consensus sequence per host of the pol gene, obtained from Sanger or next-generation sequencing (NGS). There is growing recognition that this approach discards potentially important information about within-host sequence variation, which can impact phylogenetic inference. However, whether alternative summary methods that incorporate intra-host variation impact phylogenetic inference of transmission network features is unknown. METHODS: We introduce profile sampling, a method to incorporate within-host NGS sequence diversity into phylogenetic HIV cluster inference. We compare this approach to Sanger- and NGS-derived pol and near-whole-genome consensus sequences and evaluate its potential benefits in identifying molecular clusters among all newly-HIV-diagnosed individuals over six months at the largest HIV center in Rhode Island. RESULTS: Profile sampling cluster inference demonstrated that within-host viral diversity impacts phylogenetic inference across individuals, and that consensus sequence approaches can obscure both magnitude and effect of these impacts. Clustering differed between Sanger- and NGS-derived consensus and profile sampling sequences, and across gene regions. DISCUSSION: Profile sampling can incorporate within-host HIV diversity captured by NGS into phylogenetic analyses. This additional information can improve robustness of cluster detection.

Microbial Metabolism Modulates Antibiotic Susceptibility within the Murine Gut Microbiome.

Although antibiotics disturb the structure of the gut microbiota, factors that modulate these perturbations are poorly understood. Bacterial metabolism is an important regulator of susceptibility in vitro and likely plays a large role within the host. We applied a metagenomic and metatranscriptomic approach to link antibiotic-induced taxonomic and transcriptional responses within the murine microbiome. We found that antibiotics significantly alter the expression of key metabolic pathways at the whole-community and single-species levels. Notably, Bacteroides thetaiotaomicron, which blooms in response to amoxicillin, upregulated polysaccharide utilization. In vitro, we found that the sensitivity of this bacterium to amoxicillin was elevated by glucose and reduced by polysaccharides. Accordingly, we observed that dietary composition affected the abundance and expansion of B. thetaiotaomicron, as well as the extent of microbiome disruption with amoxicillin. Our work indicates that the metabolic environment of the microbiome plays a role in the response of this community to antibiotics.

An Integrated Perspective on Phylogenetic Workflows.

Molecular phylogenetics is the study of evolutionary relationships between biological sequences, often to infer the evolutionary relationships of organisms. These studies require many analysis components, including sequence assembly, identification of homologous sequences, gene tree inference, and species tree inference. At present, each component is usually treated as a single step in a linear analysis, where the output of each component is passed as input to the next as a point estimate. Here we outline a generative model that helps clarify assumptions that are implicit to phylogenetic workflows, focusing on the assumption of low relative entropy. This perspective unifies currently disparate advances, and will help investigators evaluate which steps would benefit the most from additional computation and future methods development.