Clustering 16S rRNA Research Articles

An Innovative Approach for Biocontrol of Meloidogyne incognita in Ginger Using Potential Bacteria Isolated from Indian Himalayas.

The prevalence of Meloidogyne incognita, a severe root-knot nematode, is alarmingly high in the production of ginger-a main cash crop of Himachal Pradesh, a Himalayan state of India. In order to control this through natural means, the nematicidal potential of plant growth-promoting rhizobacteria (PGPR) against M. incognita was analyzed. This is an effective alternative solution to manage nematode incidence as compared to hazardous chemicals under protected and field cultivation of ginger. In the present study an attempt has been made to isolate, characterize, and identify potential rhizobacteria associated with ginger rhizosphere and endosphere. In total, 169 bacterial isolates were isolated from ginger (Zingiber officinale) rhizosphere and endosphere of 4 different sites of Sirmaur district, screened out for multifarious PGP traits showing positive results. The combined cluster analysis and 16S rRNA genotypic analysis of selected bacterial isolates revealed that Serratia marcescens FS-23, Pseudochrobacter sp. GS-15, Stonotrophomonas pavanii HER-9, Pseudomonas brassicacearum HER-20 and Serratia marcescens IS-2 exhibited highest PGP traits. All tested bacterial isolates were capable of exerting a significant effect on mortality of juvenile M. incognita ranging upto 40-90% in laboratory experiments. Further a consortium of these screened isolates showed 86.67% reduction in gall formation by M. incognita in lab conditions. The remarkable increase to 93.24% with 138.73q/ha with application of charcoal based bio-formulation of consortium without adding any chemical fertilizer was observed in field trials of Nohradhar of Sirmaur district. An alternative choice as a biocontrol agent as well as for PGP activities, the novel and most robust isolate Serratia marcescens IS-2 had revealed to have a variety of bioactive metabolic products with abilities against nematodes, bacteria, and fungi.

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences.

With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.

Clustering 16S rRNA for OTU prediction: A similarity based method

To study the phylogeny and taxonomy of samples from complex environments Next-generation sequencing (NGS)-based 16S rRNA sequencing , which has been successfully used jointly with the PCR amplification and NGS technology. First step for many downstream analyses is clustering 16S rRNA sequences into operational taxonomic units (OTUs). Heuristic clustering is one of the most widely employed approaches for generating OTUs in which one or more seed sequences to represent each cluster are selected. In this work we chose five random seeds for each cluster from a genes library, and we present a novel distance measure to cluster bacteria in the sample. Artificially created sets of 16S rRNA genes selected from databases are successfully clustered with more than %98 accuracy, sensitivity, and specificity.

DMSC: A Dynamic Multi-Seeds Method for Clustering 16S rRNA Sequences Into OTUs.

Next-generation sequencing (NGS)-based 16S rRNA sequencing by jointly using the PCR amplification and NGS technology is a cost-effective technique, which has been successfully used to study the phylogeny and taxonomy of samples from complex microbiomes or environments. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is often the first step for many downstream analyses. Heuristic clustering is one of the most widely employed approaches for generating OTUs. However, most heuristic OTUs clustering methods just select one single seed sequence to represent each cluster, resulting in their outcomes suffer from either overestimation of OTUs number or sensitivity to sequencing errors. In this paper, we present a novel dynamic multi-seeds clustering method (namely DMSC) to pick OTUs. DMSC first heuristically generates clusters according to the distance threshold. When the size of a cluster reaches the pre-defined minimum size, then DMSC selects the multi-core sequences (MCS) as the seeds that are defined as the n-core sequences (n ≥ 3), in which the distance between any two sequences is less than the distance threshold. A new sequence is assigned to the corresponding cluster depending on the average distance to MCS and the distance standard deviation within the MCS. If a new sequence is added to the cluster, dynamically update the MCS until no sequence is merged into the cluster. The new method DMSC was tested on several simulated and real-life sequence datasets and also compared with the traditional heuristic methods such as CD-HIT, UCLUST, and DBH. Experimental results in terms of the inferred OTUs number, normalized mutual information (NMI) and Matthew correlation coefficient (MCC) metrics demonstrate that DMSC can produce higher quality clusters with low memory usage and reduce OTU overestimation. Additionally, DMSC is also robust to the sequencing errors. The DMSC software can be freely downloaded from https://github.com/NWPU-903PR/DMSC.

DMclust, a Density-based Modularity Method for Accurate OTU Picking of 16S rRNA Sequences.

Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is a crucial step in analyzing metagenomic data. Although many methods have been developed, how to obtain an appropriate balance between clustering accuracy and computational efficiency is still a major challenge. A novel density-based modularity clustering method, called DMclust, is proposed in this paper to bin 16S rRNA sequences into OTUs with high clustering accuracy. The DMclust algorithm consists of four main phases. It first searches for the sequence dense group defined as n-sequence community, in which the distance between any two sequences is less than a threshold. Then these dense groups are used to construct a weighted network, where dense groups are viewed as nodes, each pair of dense groups is connected by an edge, and the distance of pairwise groups represents the weight of the edge. Then, a modularity-based community detection method is employed to generate the preclusters. Finally, the remaining sequences are assigned to their nearest preclusters to form OTUs. Compared with existing widely used methods, the experimental results on several metagenomic datasets show that DMclust has higher accurate clustering performance with acceptable memory usage.

A heritability-based comparison of methods used to cluster 16S rRNA gene sequences into operational taxonomic units.

A variety of methods are available to collapse 16S rRNA gene sequencing reads to the operational taxonomic units (OTUs) used in microbiome analyses. A number of studies have aimed to compare the quality of the resulting OTUs. However, in the absence of a standard method to define and enumerate the different taxa within a microbial community, existing comparisons have been unable to compare the ability of clustering methods to generate units that accurately represent functional taxonomic segregation. We have previously demonstrated heritability of the microbiome and we propose this as a measure of each methods’ ability to generate OTUs representing biologically relevant units. Our approach assumes that OTUs that best represent the functional units interacting with the hosts’ properties will produce the highest heritability estimates. Using 1,750 unselected individuals from the TwinsUK cohort, we compared 11 approaches to OTU clustering in heritability analyses. We find that de novo clustering methods produce more heritable OTUs than reference based approaches, with VSEARCH and SUMACLUST performing well. We also show that differences resulting from each clustering method are minimal once reads are collapsed by taxonomic assignment, although sample diversity estimates are clearly influenced by OTU clustering approach. These results should help the selection of sequence clustering methods in future microbiome studies, particularly for studies of human host-microbiome interactions.

Molecular and antimicrobial susceptibility profiling of atypical Streptococcus species from porcine clinical specimens

The Streptococcus species present broad phenotypic variation, making identification difficult using only traditional microbiological methods. Even though Streptococcus suis is the most important species for the worldwide swine industry, other Streptococcus species appear to be able to cause disease in swine and could represent a higher underestimated risk for porcine health. The aim of this study was to identify Streptococcus-like isolates by MALDI-TOF MS and 16S rRNA sequencing and further molecular and antibiotic susceptibility characterization of the atypical Streptococcus species capable of causing disease in swine. Fifty presumptive Streptococcus isolates from diseased pigs isolated from different Brazilian States between 2002 and 2014 were evaluated. Among the studied isolates, 26% were identified as Streptococcus hyovaginalis, 24% as Streptococcus plurianimalium, 12% as Streptococcus alactolyticus, 10% as Streptococcus hyointestinalis, and the remaining isolates belonged to Streptococcus henryi (6%), Streptococcus thoraltensis (6%), Streptococcus gallolyticus (6%), Streptococcus gallinaceus (4%), Streptococcus sanguinis (4%), and Streptococcus mitis (2%). The Streptococcus isolates were successfully identified by spectral cluster analysis and 16S rRNA sequencing with 96% of concordance between the techniques. The SE-AFLP analysis also supported Streptococcus species distinction and enabled further observation of higher genetic heterogeneity intra-species. The identified Streptococcus species presented variable MIC values to β-lactams, enrofloxacin and florfenicol, and high resistance rates to tetracyclines and macrolides, which appear to be directly related to the industry's antimicrobial usage and resistance selection.

CLUSTOM-CLOUD: In-Memory Data Grid-Based Software for Clustering 16S rRNA Sequence Data in the Cloud Environment.

High-throughput sequencing can produce hundreds of thousands of 16S rRNA sequence reads corresponding to different organisms present in the environmental samples. Typically, analysis of microbial diversity in bioinformatics starts from pre-processing followed by clustering 16S rRNA reads into relatively fewer operational taxonomic units (OTUs). The OTUs are reliable indicators of microbial diversity and greatly accelerate the downstream analysis time. However, existing hierarchical clustering algorithms that are generally more accurate than greedy heuristic algorithms struggle with large sequence datasets. To keep pace with the rapid rise in sequencing data, we present CLUSTOM-CLOUD, which is the first distributed sequence clustering program based on In-Memory Data Grid (IMDG) technology–a distributed data structure to store all data in the main memory of multiple computing nodes. The IMDG technology helps CLUSTOM-CLOUD to enhance both its capability of handling larger datasets and its computational scalability better than its ancestor, CLUSTOM, while maintaining high accuracy. Clustering speed of CLUSTOM-CLOUD was evaluated on published 16S rRNA human microbiome sequence datasets using the small laboratory cluster (10 nodes) and under the Amazon EC2 cloud-computing environments. Under the laboratory environment, it required only ~3 hours to process dataset of size 200 K reads regardless of the complexity of the human microbiome data. In turn, one million reads were processed in approximately 20, 14, and 11 hours when utilizing 20, 30, and 40 nodes on the Amazon EC2 cloud-computing environment. The running time evaluation indicates that CLUSTOM-CLOUD can handle much larger sequence datasets than CLUSTOM and is also a scalable distributed processing system. The comparative accuracy test using 16S rRNA pyrosequences of a mock community shows that CLUSTOM-CLOUD achieves higher accuracy than DOTUR, mothur, ESPRIT-Tree, UCLUST and Swarm. CLUSTOM-CLOUD is written in JAVA and is freely available at http://clustomcloud.kopri.re.kr.

A Comparison of Methods for Clustering 16S rRNA Sequences into OTUs

Recent studies of 16S rRNA sequences through next-generation sequencing have revolutionized our understanding of the microbial community composition and structure. One common approach in using these data to explore the genetic diversity in a microbial community is to cluster the 16S rRNA sequences into Operational Taxonomic Units (OTUs) based on sequence similarities. The inferred OTUs can then be used to estimate species, diversity, composition, and richness. Although a number of methods have been developed and commonly used to cluster the sequences into OTUs, relatively little guidance is available on their relative performance and the choice of key parameters for each method. In this study, we conducted a comprehensive evaluation of ten existing OTU inference methods. We found that the appropriate dissimilarity value for defining distinct OTUs is not only related with a specific method but also related with the sample complexity. For data sets with low complexity, all the algorithms need a higher dissimilarity threshold to define OTUs. Some methods, such as, CROP and SLP, are more robust to the specific choice of the threshold than other methods, especially for shorter reads. For high-complexity data sets, hierarchical cluster methods need a more strict dissimilarity threshold to define OTUs because the commonly used dissimilarity threshold of 3% often leads to an under-estimation of the number of OTUs. In general, hierarchical clustering methods perform better at lower dissimilarity thresholds. Our results show that sequence abundance plays an important role in OTU inference. We conclude that care is needed to choose both a threshold for dissimilarity and abundance for OTU inference.

Correction: CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization

Author Soon Gyu Hong should have been noted as a Corresponding Author, with the email address polypore@kopri.re.kr

CLUSTOM: A Novel Method for Clustering 16S rRNA Next Generation Sequences by Overlap Minimization

The recent nucleic acid sequencing revolution driven by shotgun and high-throughput technologies has led to a rapid increase in the number of sequences for microbial communities. The availability of 16S ribosomal RNA (rRNA) gene sequences from a multitude of natural environments now offers a unique opportunity to study microbial diversity and community structure. The large volume of sequencing data however makes it time consuming to assign individual sequences to phylotypes by searching them against public databases. Since ribosomal sequences have diverged across prokaryotic species, they can be grouped into clusters that represent operational taxonomic units. However, available clustering programs suffer from overlap of sequence spaces in adjacent clusters. In natural environments, gene sequences are homogenous within species but divergent between species. This evolutionary constraint results in an uneven distribution of genetic distances of genes in sequence space. To cluster 16S rRNA sequences more accurately, it is therefore essential to select core sequences that are located at the centers of the distributions represented by the genetic distance of sequences in taxonomic units. Based on this idea, we here describe a novel sequence clustering algorithm named CLUSTOM that minimizes the overlaps between adjacent clusters. The performance of this algorithm was evaluated in a comparative exercise with existing programs, using the reference sequences of the SILVA database as well as published pyrosequencing datasets. The test revealed that our algorithm achieves higher accuracy than ESPRIT-Tree and mothur, few of the best clustering algorithms. Results indicate that the concept of an uneven distribution of sequence distances can effectively and successfully cluster 16S rRNA gene sequences. The algorithm of CLUSTOM has been implemented both as a web and as a standalone command line application, which are available at http://clustom.kribb.re.kr.

A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets

Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.

Effects of OTU Clustering and PCR Artifacts on Microbial Diversity Estimates

Next-generation sequencing has increased the coverage of microbial diversity surveys by orders of magnitude, but differentiating artifacts from rare environmental sequences remains a challenge. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) organizes sequence data into groups of 97 % identity, helping to reduce data volumes and avoid analyzing sequencing artifacts by grouping them with real sequences. Here, we analyze sequence abundance distributions across environmental samples and show that 16S rRNA sequences of >99 % identity can represent functionally distinct microorganisms, rendering OTU clustering problematic when the goal is an accurate analysis of organism distribution. Strict postsequencing quality control (QC) filters eliminated the most prevalent artifacts without clustering. Further experiments proved that DNA polymerase errors in polymerase chain reaction (PCR) generate a significant number of substitution errors, most of which pass QC filters. Based on our findings, we recommend minimizing the number of PCR cycles in DNA library preparation and applying strict postsequencing QC filters to reduce the most prevalent artifacts while maintaining a high level of accuracy in diversity estimates. We further recommend correlating rare and abundant sequences across environmental samples, rather than clustering into OTUs, to identify remaining sequence artifacts without losing the resolution afforded by high-throughput sequencing.

CloVR-16S: Phylogenetic microbial community composition analysis based on 16S ribosomal RNA amplicon sequencing – standard operating procedure, version 1.0

AbstractThe CloVR-16S pipeline employs several well-known phylogenetic tools and protocols for the analysis of 16S rRNA sequence datasets:A) Mothur – a C++-based software package used for clustering 16S rRNA sequences into operational taxonomic units (OTUs). Mothur creates OTUs using a matrix that describes pairwise distances between representative aligned sequences and subsequently estimates within-sample diversity (alpha diversity);B) The Ribosomal Database (RDP) naïve Bayesian classifier assigns each 16S sequence to a reference taxonomy with associated empirical probabilities based on oligonucleotide frequencies;C) Qiime – a python-based workflow package, allowing for sequence processing and phylogenetic analysis using different methods including phylogenetic distance (UniFrac) for within-(alpha diversity) and between-(beta diversity) sample analysis;D) Metastats and custom R scripts used to generate additional statistical and graphical evaluations.Though some of the different protocols used in CloVR-16S overlap in purpose (e.g. OTU clustering), the end-user benefits from their overall complementary nature as they focus on different aspects of the phylogenetic analysis. CloVR-16S accepts as input raw multiplex 454-pyrosequencer output, i.e. pooled pyro-tagged sequences from multiple samples, or alternatively, pre-processed sequences from multiple samples in separate files. This protocol is available in CloVR beta versions 0.5 and 0.6.

CloVR-16S: Phylogenetic microbial community composition analysis based on 16S ribosomal RNA amplicon sequencing – standard operating procedure, version 1.0

AbstractThe CloVR-16S pipeline employs several well-known phylogenetic tools and protocols for the analysis of 16S rRNA sequence datasets:A) Mothur – a C++-based software package used for clustering 16S rRNA sequences into operational taxonomic units (OTUs). Mothur creates OTUs using a matrix that describes pairwise distances between representative aligned sequences and subsequently estimates within-sample diversity (alpha diversity);B) The Ribosomal Database (RDP) naïve Bayesian classifier assigns each 16S sequence to a reference taxonomy with associated empirical probabilities based on oligonucleotide frequencies;C) Qiime – a python-based workflow package, allowing for sequence processing and phylogenetic analysis using different methods including phylogenetic distance (UniFrac) for within-(alpha diversity) and between-(beta diversity) sample analysis;D) Metastats and custom R scripts used to generate additional statistical and graphical evaluations.Though some of the different protocols used in CloVR-16S overlap in purpose (e.g. OTU clustering), the end-user benefits from their overall complementary nature as they focus on different aspects of the phylogenetic analysis. CloVR-16S accepts as input raw multiplex 454-pyrosequencer output, i.e. pooled pyrotagged sequences from multiple samples, or alternatively, pre-processed sequences from multiple samples in separate files. This protocol is available in CloVR beta versions 0.5 and 0.6.

CloVR-16S: Phylogenetic microbial community composition analysis based on 16S ribosomal RNA amplicon sequencing – standard operating procedure, version1.0

AbstractThe CloVR-16S pipeline employs several well-known phylogenetic tools and protocols for the analysis of 16S rRNA sequence datasets:A) Mothur [1] – a C++ - based software package used for clustering 16SrRNA sequences into operational taxonomic units (OTUs). Mothur creates OTUs using a matrix that describes pairwise distances between representative aligned sequences and subsequently estimates within-sample diversity (alpha diversity);B)The Ribosomal Database (RDP) naive Bayesian classifier [2] assigns each 16S sequence to a reference taxonomy with associated empirical probabilities based on oligonucleotide frequencies;C) Qiime [3] – a python-based workflow package, allowing for sequence processing and phylogenetic analysis using different methods including phylogenetic distance (UniFrac [4]) for within- (alpha diversity) and between- (beta diversity) sample analysis;D) Metastats [5] and custom R scripts used to generate additional statistical and graphical evaluations.Though some of the different protocols used in CloVR-16S overlap in purpose (e.g. OTU clustering), the end-user benefits from their overall complementary nature as they focus on different aspects of the phylogenetic analysis. CloVR-16S accepts as input raw multiplex 454-pyrosequencer output, i.e. pooled pyrotagged sequences from multiple samples, or alternatively, pre-processed sequences from multiple samples in separate files. This protocol is available in CloVR beta versions 0.5 and 0.6.

Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering

Motivation: With the advancements of next-generation sequencing technology, it is now possible to study samples directly obtained from the environment. Particularly, 16S rRNA gene sequences have been frequently used to profile the diversity of organisms in a sample. However, such studies are still taxed to determine both the number of operational taxonomic units (OTUs) and their relative abundance in a sample.Results: To address these challenges, we propose an unsupervised Bayesian clustering method termed Clustering 16S rRNA for OTU Prediction (CROP). CROP can find clusters based on the natural organization of data without setting a hard cut-off threshold (3%/5%) as required by hierarchical clustering methods. By applying our method to several datasets, we demonstrate that CROP is robust against sequencing errors and that it produces more accurate results than conventional hierarchical clustering methods.Availability and Implementation: Source code freely available at the following URL: http://code.google.com/p/crop-tingchenlab/, implemented in C++ and supported on Linux and MS Windows.Contact: tingchen@usc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Characterization ofStreptococcus milleriGroup Isolates from Expectorated Sputum of Adult Patients with Cystic Fibrosis

With the recent insights into the Streptococcus milleri group (SMG) as pulmonary pathogens in patients with cystic fibrosis (CF), we sought to characterize 128 isolates from the sputum of adults with CF, along with 45 isolates from patients with invasive diseases for comparison. The tests performed included Lancefield grouping; tests for hemolysis; tests for the production of hyaluronidase, chondroitin sulfatase, DNase, proteases, and hydrogen peroxide; and PCR for the detection of the intermedilysin gene (ily). We also generated biochemical profiles with the Rapid ID Strep 32 API system and tested cell-free supernatants for the presence of the signal molecule autoinducer-2 (AI-2) using a Vibrio harveyi bioassay with a subset of CF strains. The S. intermedius isolates from both strain collections were similar, while the S. constellatus and S. anginosus isolates yielded several biotypes that differed in prevalence between the two strain collections. Beta-hemolytic, Lancefield group C S. constellatus comprised 74.4% of the S. constellatus isolates from patients with CF but only 13.3% of the corresponding isolates from patients with invasive infections. This was the only S. constellatus biotype associated with pulmonary exacerbations. Hyaluronidase-positive S. anginosus was detected only among the isolates from patients with CF. Strain-to-strain variability in AI-2 expression was evident, with the mean values being the highest for S. anginosus, followed by S. constellatus and then S. intermedius. Cluster analysis and 16S rRNA sequencing revealed that the species of SMG could be accurately determined with a minimum of three phenotypic tests: tests for the Lancefield group, hyaluronidase production, and chondroitin sulfatase production. Furthermore, isolates from patients with invasive infections clustered with isolates from the sputum of patients with CF, suggesting that the respiratory tract isolates were equally pathogenic.

An update of the structure and 16S rRNA gene sequence-based definition of higher ranks of the class Actinobacteria, with the proposal of two new suborders and four new families and emended descriptions of the existing higher taxa

The higher ranks of the class Actinobacteria were proposed and described in 1997. At each rank, the taxa were delineated from each other solely on the basis of 16S rRNA gene sequence phylogenetic clustering and taxon-specific 16S rRNA signature nucleotides. In the past 10 years, many novel members have been assigned to this class while, at the same time, some members have been reclassified. The new 16S rRNA gene sequence information and the changes in phylogenetic positions of some taxa influence decisions about which 16S rRNA nucleotides to define as taxon-specific. As a consequence, the phylogenetic relationships of Actinobacteria at higher levels may need to be reconstructed. Here, we present new 16S rRNA signature nucleotide patterns of taxa above the family level and indicate the affiliation of genera to families. These sets replace the signatures published in 1997. In addition, Actinopolysporineae subord. nov. and Actinopolysporaceae fam. nov. are proposed to accommodate the genus Actinopolyspora, Kineosporiineae subord. nov. and Kineosporiaceae fam. nov. are proposed to accommodate the genera Kineococcus, Kineosporia and Quadrisphaera, Beutenbergiaceae fam. nov. is proposed to accommodate the genera Beutenbergia, Georgenia and Salana and Cryptosporangiaceae fam. nov. is proposed to accommodate the genus Cryptosporangium. The families Nocardiaceae and Gordoniaceae are proposed to be combined in an emended family Nocardiaceae. Emended descriptions are also proposed for most of the other higher taxa.

Diversity and depth-specific distribution of SAR11 cluster rRNA genes from marine planktonic bacteria.

Small-subunit (SSU) ribosomal DNA (rDNA) gene clusters are phylogenetically related sets of SSU rRNA genes, commonly encountered in genes amplified from natural populations. Genetic variability in gene clusters could result from artifacts (polymerase error or PCR chimera formation), microevolution (variation among rrn copies within strains), or macroevolution (genetic divergence correlated with long-term evolutionary divergence). To better understand gene clusters this study assessed genetic diversity and distribution of a single environmental SSU rDNA gene cluster, the SAR11 cluster. SAR11 cluster genes, from an uncultured group of the alpha subclass of the class Proteobacteria, have been recovered from coastal and midoceanic waters of the North Atlantic and Pacific. We cloned and bidirectionally sequenced 23 new SAR11 cluster 16S rRNA genes, from 80 and 250 m in the Sargasso Sea and from surface coastal waters of the Atlantic and Pacific, and analyzed them with previously published sequences. Two SAR11 genes were obviously PCR chimeras, but the biological (nonchimeric) origins of most subgroups within the cluster were confirmed by independent recovery from separate gene libraries. Using group-specific oligonucleotide probes, we analyzed depth profiles of nucleic acids, targeting both amplified rDNAs and bulk RNAs. Two subgroups within the SAR11 cluster showed different highly depth-specific distributions. We conclude that some of the genetic diversity within the SAR11 gene cluster represents macroevolutionary divergence correlated with niche specialization. Furthermore, we demonstrate the utility for marine microbial ecology of oligonucleotide probes based on gene sequences amplified from natural populations and show that a detailed knowledge of sequence variability may be needed to effectively design these probes.