Sequencing of 16S rRNA gene tags is a popular method for profiling and comparing microbial communities. The protocols and methods used, however, vary considerably with regard to amplification primers, sequencing primers, sequencing technologies; as well as quality filtering and clustering. How results are affected by these choices, and whether data produced with different protocols can be meaningfully compared, is often unknown. Here we compare results obtained using three different amplification primer sets (targeting V4, V6-V8, and V7-V8) and two sequencing technologies (454 pyrosequencing and Illumina MiSeq) using DNA from a mock community containing a known number of species as well as complex environmental samples whose PCR-independent profiles were estimated using shotgun sequencing. We find that paired-end MiSeq reads produce higher quality data and enabled the use of more aggressive quality control parameters over 454, resulting in a higher retention rate of high quality reads for downstream data analysis. While primer choice considerably influences quantitative abundance estimations, sequencing platform has relatively minor effects when matched primers are used. Beta diversity metrics are surprisingly robust to both primer and sequencing platform biases.

Multiple models describe the formation and evolution of distinct microbial phylogenetic groups. These evolutionary models make different predictions regarding how adaptive alleles spread through populations and how genetic diversity is maintained. Processes predicted by competing evolutionary models, for example, genome-wide selective sweeps vs gene-specific sweeps, could be captured in natural populations using time-series metagenomics if the approach were applied over a sufficiently long time frame. Direct observations of either process would help resolve how distinct microbial groups evolve. Here, from a 9-year metagenomic study of a freshwater lake (2005–2013), we explore changes in single-nucleotide polymorphism (SNP) frequencies and patterns of gene gain and loss in 30 bacterial populations. SNP analyses revealed substantial genetic heterogeneity within these populations, although the degree of heterogeneity varied by >1000-fold among populations. SNP allele frequencies also changed dramatically over time within some populations. Interestingly, nearly all SNP variants were slowly purged over several years from one population of green sulfur bacteria, while at the same time multiple genes either swept through or were lost from this population. These patterns were consistent with a genome-wide selective sweep in progress, a process predicted by the ‘ecotype model’ of speciation but not previously observed in nature. In contrast, other populations contained large, SNP-free genomic regions that appear to have swept independently through the populations prior to the study without purging diversity elsewhere in the genome. Evidence for both genome-wide and gene-specific sweeps suggests that different models of bacterial speciation may apply to different populations coexisting in the same environment.

Background: With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing. Findings: Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for high-performance computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA sequencing, chromatin immunoprecipitation sequencing, DNA sequencing, methylation sequencing, Hi-C, capture Hi-C, metagenomics, and Pacific Biosciences long-read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has already been configured on several servers, and a Docker image is also available to facilitate additional installations. Conclusions: GenPipes offers genomics researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

Summary: Pseudomonas aeruginosa presents three types of motilities: swimming, twitching and swarming. The latter is characterized by rapid and coordinated group movement over a semisolid surface resulting from morphological differentiation and intercellular interactions. A striking feature of P. aeruginosa swarming motility is the formation of migrating tendrils producing colonies with complex fractal‐like patterns. Previous studies have shown that normal swarming motility is intimately related to the production of extracellular surface‐active molecules: rhamnolipids (RLs), composed of monorhamnolipids (mono‐RLs) and dirhamnolipids (di‐RLs), and 3‐(3‐hydroxyalkanoyloxy) alkanoic acids (HAAs). Here, we report that (i) di‐RLs attract active swarming cells while HAAs behave as strong repellents, (ii) di‐RLs promote and HAAs inhibit tendril formation and migration, (iii) di‐RLs and HAAs display different diffusion kinetics on a surface as di‐RLs spread faster than HAAs in agar, (iv) di‐RLs and HAAs have no effect on swimming cells, suggesting that swarming cells are different from swimming cells not only in morphology but also at the regulatory level and (v) mono‐RLs act as wetting agents. We propose a model explaining how HAAs and di‐RLs together modulate the behaviour of swarming migrating cells by acting as self‐produced negative and positive chemotactic‐like stimuli.