A De Novo Clustering Method: Snowball for Assigning 16S Operational Taxonomic Units

To analyze complex biodiversity in microbial communities, 16S rRNA marker gene sequences are often assigned to operational taxonomic units (OTUs). The abundance of methods that have been used to assign 16S rRNA marker gene sequences into OTUs brings discussions in which one is better. Suggestions on having clustering methods should be stable in which generated OTU assignments do not change as additional sequences are added to the dataset is contradicting some other researches contend that the methods should properly present the distances of sequences is more important. We add one more de novo clustering algorithm, Rolling Snowball to existing ones including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. We use GreenGenes, RDP, and SILVA 16S rRNA gene databases to show the success of the method. The highest accuracy is obtained with SILVA library

Abnormal gut microbiota and impaired incretin effect as a cause of type 2 diabetes mellitus

It has now been established that the intestinal microbiota (CM) is one of the 11 links in the pathogenesis of type 2 diabetes mellitus (DM2). Th e fact is that when the composition of BM is disrupted and the concentration of its active metabolites changes, the processes of synthesis and secretion of incretin hormones are disrupted, the homeostasis of carbohydrates and fats in the body is disrupted, the processes of central regulation of appetite change, chronic infl ammation and insulin resistance of peripheral tissues develop. Th is review discusses possible ways of impairing the synthesis of incretin hormones and the incretin eff ect in patients with T2DM through the prism of BM and its active metabolites, and discusses possible ways of correcting the altered composition of BM with incretin drugs.A systematic literature search was carried out using the Scopus, PubMed, Web of Science databases

Microbiome Preprocessing Machine Learning Pipeline

Background16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML.MethodsWe checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification.ResultsWe show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results.ConclusionsThe prepossessing of microbiome 16S data is crucial for optimal microbiome based Machine Learning. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand-alone version at: https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac.il/Home Both contain the code, and standard test sets

Dynamics of tongue microbial communities with single-nucleotide resolution using oligotyping

.© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Microbiology 5 (2014): 568, doi:10.3389/fmicb.2014.00568.The human mouth is an excellent system to study the dynamics of microbial communities and their interactions with their host. We employed oligotyping to analyze, with single-nucleotide resolution, oral microbial 16S ribosomal RNA (rRNA) gene sequence data from a time course sampled from the tongue of two individuals, and we interpret our results in the context of oligotypes that we previously identified in the oral data from the Human Microbiome Project. Our previous work established that many of these oligotypes had dramatically different distributions between individuals and across oral habitats, suggesting that they represented functionally different organisms. Here we demonstrate the presence of a consistent tongue microbiome but with rapidly fluctuating proportions of the characteristic taxa. In some cases closely related oligotypes representing strains or variants within a single species displayed fluctuating relative abundances over time, while in other cases an initially dominant oligotype was replaced by another oligotype of the same species. We use this high temporal and taxonomic level of resolution to detect correlated changes in oligotype abundance that could indicate which taxa likely interact synergistically or occupy similar habitats, and which likely interact antagonistically or prefer distinct habitats. For example, we found a strong correlation in abundance over time between two oligotypes from different families of Gamma Proteobacteria, suggesting a close functional or ecological relationship between them. In summary, the tongue is colonized by a microbial community of moderate complexity whose proportional abundance fluctuates widely on time scales of days. The drivers and functional consequences of these community dynamics are not known, but we expect they will prove tractable to future, targeted studies employing taxonomically resolved analysis of high-throughput sequencing data sampled at appropriate temporal intervals and spatial scales.Supported by National Institutes of Health (NIH) National Institute of Dental and Craniofacial Research Grant DE022586 (to Gary G. Borisy). Daniel R. Utter was supported by the Woods Hole Partnership Education Program; A. Murat Eren was supported by a G. Unger Vetlesen Foundation grant to the Marine Biological Laboratory; David B. Mark Welch was supported by NSF DBI-126259

Vegetation Effects on Rhizosphere Microbial Communities in Coastal Wetlands of South Mississippi

The Mississippi vegetated coastal wetlands consist of many salt and brackish marshes. In those marshes, there are two plant species Spartina alterniflora and Juncus roemerianus that thrive in those environments. This would not be possible without the benefits of microbial communities that live in the portion of the plant's soil called the rhizosphere. The rhizosphere is crucial for plant nutrition, health, and quality. It supports the biomass and activity of microorganisms for carbon sequestration, ecosystem functioning, and nutrient cycling in natural ecosystems. To investigate the vegetation effects on rhizosphere microbial communities in coastal wetlands, plant samples and their rhizosphere soils were collected from two brackish transects and two saltwater transects at Graveline Bayou, Gautier, MS. A number of biotic and abiotic factors were measured, and their impacts on bacterial community composition and diversity were determined via Illumina MiSeq 16S rRNA gene sequence. Overall, the composition of rhizosphere bacterial community in coastal wetlands were dominated by Proteobacteria and Planctomycetes. The effects of seasonal patterns and plant developmental stages had no impacts on rhizosphere microbial communities due to similar pH level, soil moisture, and organic matter content in soil between winter and summer seasons of 2015. Salinity increased bacterial community diversity especially Proteobacteria and Bacteroidetes. There are several contrasting reports that portrayed the dominant factor in determining the diversity of rhizosphere microbial communities as either the plant species itself or the soil type of the site. In this study, the soil type was the major driving force in bacterial community diversity

Application of Machine Learning in Microbiology

Microorganisms are ubiquitous and closely related to people’s daily lives. Since they were first discovered in the 19th century, researchers have shown great interest in microorganisms. People studied microorganisms through cultivation, but this method is expensive and time consuming. However, the cultivation method cannot keep a pace with the development of high-throughput sequencing technology. To deal with this problem, machine learning (ML) methods have been widely applied to the field of microbiology. Literature reviews have shown that ML can be used in many aspects of microbiology research, especially classification problems, and for exploring the interaction between microorganisms and the surrounding environment. In this study, we summarize the application of ML in microbiology

Ecological consistency of SSU rRNA-based operational taxonomic units at a global scale

Operational Taxonomic Units (OTUs), usually defined as clusters of similar 16S/18S rRNA sequences, are the most widely used basic diversity units in large-scale characterizations of microbial communities. However, it remains unclear how well the various proposed OTU clustering algorithms approximate 'true' microbial taxa. Here, we explore the ecological consistency of OTUs--based on the assumption that, like true microbial taxa, they should show measurable habitat preferences (niche conservatism). In a global and comprehensive survey of available microbial sequence data, we systematically parse sequence annotations to obtain broad ecological descriptions of sampling sites. Based on these, we observe that sequence-based microbial OTUs generally show high levels of ecological consistency. However, different OTU clustering methods result in marked differences in the strength of this signal. Assuming that ecological consistency can serve as an objective external benchmark for cluster quality, we conclude that hierarchical complete linkage clustering, which provided the most ecologically consistent partitions, should be the default choice for OTU clustering. To our knowledge, this is the first approach to assess cluster quality using an external, biologically meaningful parameter as a benchmark, on a global scale