Ribosomal database project ii chimera check program

Maidak , a James R. Cole , Timothy G. Lilburn , Charles T. Parker Jr , Paul R. Saxman , Jason M. Stredwick , George M. Garrity , 1 Bing Li , Gary J. Olsen , 2 Sakti Pramanik , 3 Thomas M. Schmidt , 1 and James M. Tiedje 1. George M. Gary J. Thomas M. James M. Author information Article notes Copyright and License information Disclaimer. Received Oct 4; Accepted Oct 6. To our knowledge, this is the first report that archaea has been identified as endophytes associated with rice by the culture-independent approach.

The results suggest that the diversity of endophytic bacteria is abundant in rice roots. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. Microbiol Rev — Appl Environ Microbiol — Bevivino A, Sarrocco S, Dalmastri C, Tabacchioni S, Cantale C, Chiarini L Characterization of a free-living maize-rhizosphere population of Burkholderia cepacia : effect of seed treatment on disease suppression and growth promotion of maize.

Microb Ecol — Gene —8. Microbiology — Nucl Acids Res — Felsenstein J Confidence limits on phylogenies: an approach using the bootstrap. Evolution — Article Google Scholar. J Appl Microbiol — Good IL The population frequencies of species and the estimation of population parameters. Biometrika — Google Scholar. Studies conducted in our laboratory have revealed microbial communities within two U mines Jaduguda and Banduhurang along with potential of inhabitant bacteria in U and other metal resistance and sequestration and impact of U ore contamination on soil microbial diversity Sar et al.

With vast genetic and metabolic diversity, these microorganisms were found to interact with metals and radionuclides directly or indirectly by redox transfer, biosorption, bioaccumulation or bioprecipitation affecting their environmental mobility and toxicity Suzuki and Banfield ; Tabak et al. Considering the significance of geomicrobiology of contaminated sites, it is therefore imperative to decipher phylogenetic diversity of indigenous microbial populations in sites having high risk of contamination or already contaminated to illuminate community resilience, their potential role in affecting metal biogeochemistry and in designing appropriate bioremediation strategies Tabak et al.

Uranium mines at Jaduguda, Bagjata, and Turamdih are all located in highly mineral-rich areas of East Singhbhum district, Jharkhand, India. Jaduguda mine is the oldest U mine in India operating since , while the other two mines are commissioned relatively recently — Gupta and Sarangi Present work was undertaken to ascertain diversity and structure of bacterial communities within sites in and around these U mines as a means to obtain the baseline data on microbial diversity.

Samples collected from all these sites were analyzed for their physicochemical properties pH, conductivity, total organic carbon TOC , total nitrogen TN , total phosphorus TP and heavy metal content.

Culturable bacterial counts were recorded and finally the compositions of microbial communities were determined at molecular level with the determination of diversity indices and identification of dominant ribotypes. Samples B, B, B, B, B and T, T, T, T were collected from agriculture field; pond and small streams located outside the boundary of Bagjata and Turamdih mines, respectively, and are designated as non-contaminated samples.

Samples CW1, CW2 and CW3 were obtained from sites within the mines and contaminated with mine wastes and ores and are designated as contaminated samples. All samples were collected aseptically and stored immediately in ice till further analysis. Measured quantities of samples were dispersed in sterile saline 0.

For each sample, positive clones were selected for clone library construction. Detailed description of methodology was same as described previously Islam and Sar a. Colony PCR products were purified and digested with restriction enzymes in separate reactions. About first — nucleotides of representative clones from each dominant OTU were sequenced.

All samples were analyzed for their physicochemical and microbiological properties Figs. As evident from the Fig. Heavy metal content as analyzed in non-contaminated samples indicated that for the elements tested the concentrations were within the limits for background concentrations of trace elements estimated in non-anthropogenic soils Burt et al. Concentrations of metals like Co, Ni and Zn were considerably higher in contaminated samples over the non-contaminated counterparts exceeding the values reported for non-anthropogenic soils or matches well with those reported for anthropogenically contaminated soils Burt et al.

Uranium, though present in several non-contaminated samples as well, was present at elevated level in two contaminated samples CW1 and CW2 Fig. Noticeably, in all three non-contaminated samples simultaneous presence of three or more metals including U at higher concentrations was observed. Presence of U and other metals in non-contaminated samples could be explained considering the fact that this whole region is highly mineral-rich and natural weathering may easily enrich the soil and other parts with metals Sarangi and Singh For analyzing bacterial diversity nearly 50 or clones were used to construct individual clone library for each sample.

Based on ARDRA profiles, statistical analysis was followed to determine the bacterial diversity within these samples. Interestingly, all three contaminated samples showed relatively higher Shannon diversity indices H but the equitability value E H varied considerably among the samples [B 2.

Based on diversity result, we inferred that high nutrient content of the samples possibly make the heavy metal contamination as less stressful event as the inhabitant bacterial members might be nutritionally well supported to withstand metal toxicity. Nevertheless, near neutral pH may allow formation of relatively insoluble metal complexes for most cations thereby reducing their availability as well.

Compositions of bacterial communities within the samples were ascertained by analyzing 16S rRNA gene sequences of major ribotypes from each library Figs. Among the samples collected from Bagjata, bacterial communities in B agriculture field and B river sediment showed representatives of Bacteroidetes , Acidobacteria and Firmicutes.

Additionally, phyla Chloroflexi and Deferribacteres were present in high percentage in B, while Cyanobacteria and Chloroflexi were present in B Members of Proteobacteria were not so abundant in these samples.

Distribution of different bacterial groups within a non-contaminated and b contaminated samples. Members of Actinobacteria, Chloroflexi, Planctomycetes , Cyanobacteria and Gemmmatimonadetes were detected as relatively minor groups. The observed community structure within this sample corroborates well with its relatively higher organic carbon, nitrogen and phosphorous content and CFU counts suggesting that in spite of higher metal contamination bacterial flora can flourish very well possibly by developing appropriate homeostatic mechanisms Akob et al.

Details of 16S rRNA gene sequences retrieved in this study and used for phylogenetic analysis. Phylogenetic dendrogram of the Acidobacterium division based on Neighbor-joining analysis. Subdivisions see the text are indicated in brackets at the right of the tree. Numbers at nodes indicate percent bootstrap values above 80 supported by 1, replicates.

Bar indicates Jukes-Cantor evolutionary distance. Similarity search in NCBI and RDP databases indicated that all the Acidobacterial sequences had strong identity with uncultured members of this phylum. Particularly, sequences from non-contaminated samples showed a strong similarity with uncultured Acidobacteria from undisturbed mixed grass prairie and rice field soils.

In contrast, Acidobacteria sequences retrieved from contaminated samples showed high similarity with uncultured clones mostly obtained from hydrocarbon-contaminated soil and sediment, soil adjacent to silage storage bunker and Altamira cave, etc. In order to determine the distribution of Acidobacteria sequences retrieved from ten clone libraries into different subgroups, a Neighbor-joining tree was constructed using representative Acidobacteria sequences of different subgroups along with our sequences Fig.

Recommended read Q score of 27 for Assembler and base Q score deltaq of 6 for mothur are marked. Uploaded sequences for all genes are checked for orientation and reverse complemented when necessary. Each alignment job result also includes alignment position and length statistics as well as a summary histogram of read alignment start and end positions relative to the alignment model.

The complete linkage clustering tool 29 , 30 allows users to upload aligned sequences to be clustered as the first step in taxonomy-independent analysis.

Sequence files can be clustered together with each file treated as a sample, or files can be clustered separately. The online clustering tool is limited to unique sequences per job. For clustering very large datasets, we provide a modified version of mcClust 31 for download see below. This new version distributes distance calculations among a compute cluster and incorporates algorithmic changes that lower the time complexity and speed up clustering.

The cluster file obtained from Clustering or mcClust can be used to compute five common ecological measures for their samples. Researchers can also assess sequencing depth using the rarefaction tool. For researchers who include a defined community sample in their sequencer run, the Defined Community Analysis Tool calculates the observed error rates based on the known gene sequences of the organisms in the defined community.

A file is returned either containing only the sequences specified, or excluding them, depending on option selected. Hierarchical sequence clustering methods that worked well for thousands of amplicon sequences often fail with the increased output of the latest sequencing technologies.

Exact clustering methods require all pairwise distances for the input sequences and thus scale on the order of O n 2. Many clustering implementations, in addition to requiring O n 2 computational time, also have a memory complexity of O n 2 as they store all distances in memory. Nonetheless, clustering methods remain an important tool in rRNA sequence analyses, and several groups have attempted to solve the scaling issues facing sequence clustering. Another approach proposed by Loewenstein et al.

To utilize disk storage for the pairwise distances, they must be in sorted order. With a general purpose sorting algorithm this increases the time complexity to O n 2 log n 2. Several previously published complete linkage algorithm implementations take advantage of on disk storage of distances to limit the memory requirements for clustering 31 , 36 , These implementations still require all pairwise distances or at least all pairwise distances up to a maximum distance cutoff , and more importantly require sorting of all these distances.

We propose a distance calculation tool with the goal of being efficient in the distance calculations, allowing the distance matrix computation to be parallelized and using an alternative sorting method to reduce the time complexity back to O n 2 see Supplementary material.

The distance calculation tool is implemented in Java 1. Compared to single-stranded Illumina reads, assembled paired-end reads can provide longer sequences with lower error rates. However, newly developed paired-end assembly tools have limitations. Our modified PANDAseq Assembler performs a modified statistical analysis using the sequencer supplied quality Q scores to find the most likely overlap, computes assembled Q scores for the read overlap region and handles more complex overlap layouts see Supplementary material for details.

We have tested Assembler using two defined community samples from two different MiSeq runs. Both runs passed the Illumina MiSeq quality standards but the basic per base error rates of these two samples are quite different 0. Both are within the reported error rate range for paired-end MiSeq amplicon data 0. Using an overall read Q score quality filter to remove low quality sequences, we tested the Assembler against the paired-end assembler and quality filter built into mothur 39 , another amplicon analysis program.

Assembler slightly outperformed mothur on the high-quality dataset Figure 4 A , and significantly outperformed on the average-quality dataset Figure 4 B. In both datasets, Assembler outperformed the original PANDAseq when scored in a similar manner although such Q score based filtering was not a goal of that implementation.

Using a read Q score of 27 decreases the error rates to 0. All three programs can be run with multiple threads but were limited to a single thread in our testing. On an AMD Opteron quad-core 2. RDP online tools are each supplied with a help page as a quick reference for its functionality, algorithm and how-tos. An RDP Wiki provides an updated searchable repository for answers to commonly asked questions compiled from previous user communications with RDP staff.

Workflow tutorials guide researchers through common task-oriented processes, provide sample data and introduce researchers to the best practices for NGS data analysis. For command-line tools, step-by-step instructions and sample data files are provided on the RDP GitHub repository. Support questions can be emailed to ude. Funding for open access charge: US Department of Energy.

We thank Gareth W. Sogin and Gary Olsen. We dedicate this publication to Carl Woese, whose insight made this work possible. National Center for Biotechnology Information , U.

Journal List Nucleic Acids Res v. Nucleic Acids Res. Published online Nov James R. Fish , 1, 2 Benli Chai , 1 Donna M. McGarrell , 1 Yanni Sun , 2 C. Kuske , 5 and James M. Tiedje 1, 3. Jordan A. Donna M. Titus Brown. Cheryl R. James M. Author information Article notes Copyright and License information Disclaimer.

Published by Oxford University Press. For commercial re-use, please contact journals. This article has been cited by other articles in PMC. Open in a separate window. Figure 1. Alignments The sequences in the RDP database are aligned using Infernal, a stochastic context-free grammar-based aligner Figure 2.