PhyloToAST: Bioinformatics Tools for Species-Level Analysis and Visualization of Complex Microbial Datasets

The 16S rRNA gene is widely used for taxonomic profiling of microbial ecosystems; and recent advances in sequencing chemistry have allowed extremely large numbers of sequences to be generated from minimal amounts of biological samples. Analysis speed and resolution of data to species-level taxa are two important factors in large-scale explorations of complex microbiomes using 16S sequencing. We present here new software, Phylogenetic Tools for Analysis of Species-level Taxa (PhyloToAST), that completely integrates with the QIIME pipeline to improve analysis speed, reduce primer bias (requiring two sequencing primers), enhance species-level analysis, and add new visualization tools. The code is free and open source, and can be accessed at http://phylotoast.org

iPHoP: An integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses

FIND: A new software tool and development platform for enhanced multicolor flow analysis

<p>Abstract</p> <p>Background</p> <p>Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use.</p> <p>Results</p> <p>We have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms.</p> <p>Conclusions</p> <p>The <b>FIND </b>(Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.</p

Community dynamics during de novo colonization of the nascent peri-implant sulcus

Abstract Dental implants have restored masticatory function to over 100 000 000 individuals, yet almost 1 000 000 implants fail each year due to peri-implantitis, a disease triggered by peri-implant microbial dysbiosis. Our ability to prevent and treat peri-implantitis is hampered by a paucity of knowledge of how these biomes are acquired and the factors that engender normobiosis. Therefore, we combined a 3-month interventional study of 15 systemically and periodontally healthy adults with whole genome sequencing, fine-scale enumeration and graph theoretics to interrogate colonization dynamics in the pristine peri-implant sulcus. We discovered that colonization trajectories of implants differ substantially from adjoining teeth in acquisition of new members and development of functional synergies. Source-tracking algorithms revealed that this niche is initially seeded by bacteria trapped within the coverscrew chamber during implant placement. These pioneer species stably colonize the microbiome and exert a sustained influence on the ecosystem by serving as anchors of influential hubs and by providing functions that enable cell replication and biofilm maturation. Unlike the periodontal microbiome, recruitment of new members to the peri-implant community occurs on nepotistic principles. Maturation is accompanied by a progressive increase in anaerobiosis, however, the predominant functionalities are oxygen-dependent over the 12-weeks. The peri-implant community is easily perturbed following crown placement, but demonstrates remarkable resilience; returning to pre-perturbation states within three weeks. This study highlights important differences in the development of the periodontal and peri-implant ecosystems, and signposts the importance of placing implants in periodontally healthy individuals or following the successful resolution of periodontal disease

Characterizing oral microbial communities across dentition states and colonization niches

Abstract Methods  The present study aimed to identify patterns and processes in acquisition of oral bacteria and to characterize the microbiota of different dentition states and habitats. Mucosal, salivary, supragingival, and subgingival biofilm samples were collected from orally and systemically healthy children and mother-child dyads in predentate, primary, mixed, and permanent dentitions. 16S rRNA gene sequences were compared to the Human Oral Microbiome Database (HOMD). Functional potential was inferred using PICRUSt. Results Unweighted and weighted UniFrac distances were significantly smaller between each mother-predentate dyad than infant-unrelated female dyads. Predentate children shared a median of 85% of species-level operational taxonomic units (s-OTUs) and 100% of core s-OTUs with their mothers. Maternal smoking, but not gender, mode of delivery, feeding habits, or type of food discriminated between predentate microbial profiles. The primary dentition demonstrated expanded community membership, structure, and function when compared to the predentate stage, as well as significantly lower similarity between mother-child dyads. The primary dentition also included 85% of predentate core s-OTUs. Subsequent dentitions exhibited over 90% similarity to the primary dentition in phylogenetic and functional structure. Species from the predentate mucosa as well as new microbial assemblages were identified in the primary supragingival and subgingival microbiomes. All individuals shared 65% of species between supragingival and subgingival habitats; however, the salivary microbiome exhibited less than 35% similarity to either habitat. Conclusions Within the limitations of a cross-sectional study design, we identified two definitive stages in oral bacterial colonization: an early predentate imprinting and a second wave with the eruption of primary teeth. Bacterial acquisition in the oral microbiome is influenced by the maternal microbiome. Personalization begins with the eruption of primary teeth; however, this is limited to phylogeny; functionally, individuals exhibit few differences, suggesting that microbial assembly may follow a defined schematic that is driven by the functional requirements of the ecosystem. This early microbiome forms the foundation upon which newer communities develop as more colonization niches emerge, and expansion of biodiversity is attributable to both introduction of new species and increase in abundance of predentate organisms