MetaSMC: a coalescent-based shotgun sequence simulator for evolving microbial populations.

MOTIVATION: High-throughput sequencing technology has revolutionized the study of metagenomics and cancer evolution. In a relatively simple environment, a metagenomics sequencing data is dominated by a few species. By analyzing the alignment of reads from microbial species, single nucleotide polymorphisms can be discovered and the evolutionary history of the populations can be reconstructed. The ever-increasing read length will allow more detailed analysis about the evolutionary history of microbial or tumor cell population. A simulator of shotgun sequences from such populations will be helpful in the development or evaluation of analysis algorithms. RESULTS: Here, we described an efficient algorithm, MetaSMC, which simulates reads from evolving microbial populations. Based on the coalescent theory, our simulator supports all evolutionary scenarios supported by other coalescent simulators. In addition, the simulator supports various substitution models, including Jukes-Cantor, HKY85 and generalized time-reversible models. The simulator also supports mutator phenotypes by allowing different mutation rates and substitution models in different subpopulations. Our algorithm ignores unnecessary chromosomal segments and thus is more efficient than standard coalescent when recombination is frequent. We showed that the process behind our algorithm is equivalent to Sequentially Markov Coalescent with an incomplete sample. The accuracy of our algorithm was evaluated by summary statistics and likelihood curves derived from Monte Carlo integration over large number of random genealogies. AVAILABILITY AND IMPLEMENTATION: MetaSMC is written in C. The source code is available at https://github.com/tarjxvf/metasmc. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application of 16S rRNA metagenomics to analyze bacterial communities at a respiratory care centre in Taiwan.

In this study, we applied a 16S ribosomal RNA (rRNA) metagenomics approach to survey inanimate hospital environments (IHEs) in a respiratory care center (RCC). A total of 16 samples, including 9 from medical devices and 7 from workstations, were analyzed. Besides, clinical isolates were retrospectively analyzed during the sampling period in the RCC. A high amount of microbial diversity was detected, with an average of 1,836 phylotypes per sample. In addition to Acinetobacter, more than 60 % of the bacterial communities present among the top 25 abundant genera were dominated by skin-associated bacteria. Differences in bacterial profiles were restricted to individual samples. Furthermore, compliance with hand hygiene guidelines may be unsatisfactory among hospital staff according to a principal coordinate analysis that indicated clustering of bacterial communities between devices and workstations for most of the sampling sites. Compared to the high incidence of clinical isolates in the RCC, only Staphylococcus and Acinetobacter were highly abundant in the IHEs. Despite Acinetobacter was the most abundant genus present in IHEs of the RCC, potential pathogens, e.g., Acinetobacter baumannii, might remain susceptible to carbapenem. This study is the first in Taiwan to demonstrate a high diversity of human-associated bacteria in the RCC via 16S rRNA metagenomics, which allows for new assessment of potential health risks in RCCs, aids in the evaluation of existing sanitation protocols, and furthers our understanding of the development of healthcare-associated infections.

Subgingival microbiota in individuals with severe chronic periodontitis.

BACKGROUND/PURPOSE: Subgingival microorganisms are potentially associated with periodontal diseases. However, the correlation between the variance in the periodontal microbiome and the prevalence and severity of periodontitis remains unclear. The aim of this study was to determine the subgingival microbiota in Taiwanese individuals with severe chronic periodontitis (SP). METHODS: The composition of the subgingival microbiota in healthy and diseased individuals was compared using a 16S rRNA metagenomic approach and quantitative polymerase chain reaction (qPCR). A total of 20 samples, including 10 from healthy individuals and 10 from SP patients, were analyzed. RESULTS: We found high microbial diversity, with an average of 774 classified phylotypes per sample and a total of six bacterial phyla across all samples. Cluster analysis by principal component analysis and heat map showed that the bacterial communities were different in the two groups. Streptococcus dominated across all the healthy samples, whereas Prevotella, Porphyromonas, and Treponema were highly abundant across all diseased samples. At least 13 bacterial genera were conserved among all the samples. Only eight genera, including Lautropia, Parvimonas, Actinomyces, Capnocytophaga, Paludibacter, Streptococcus, Haemophilus, and Corynebacterium, were significantly enriched in the healthy group, and six genera, including Porphyromonas, Treponema, Tannerella, Aggregatibacter, Peptostreptococcus, and Filifactor, were significantly enriched in the diseased group. Furthermore, a trend of abundance of bacteria at the species level measured by qPCR in all samples was consistent with the 16S rRNA metagenomics results. CONCLUSION: This study is the first in Taiwan to provide a picture of the microbiome in SP via 16S rRNA metagenomics.