Black Box Chimera Check (B2C2): a Windows-Based Software for Batch Depletion of Chimeras from Bacterial 16S rRNA Gene Datasets.
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ABSTRACT:
SUBMITTER: Gontcharova V
PROVIDER: S-EPMC3040993 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A new computer program, called Mallard, is presented for screening entire 16S rRNA gene libraries of up to 1,000 sequences for chimeras and other artifacts. Written in the Java computer language and capable of running on all major operating systems, the program provides a novel graphical approach for visualizing phylogenetic relationships among 16S rRNA gene sequences. To illustrate its use, we analyzed most of the large libraries of cloned bacterial 16S rRNA gene sequences submitted to the public repository during 2005. Defining a large library as one containing 100 or more sequences of 1,200 bases or greater, we screened 25 of the 28 libraries and found that all but three contained substantial anomalies. Overall, 543 anomalous sequences were found. The average anomaly content per clone library was 9.0%, 4% higher than that previously estimated for the public repository overall. In addition, 90.8% of anomalies had characteristic chimeric patterns, a rise of 25.4% over that found previously. One library alone was found to contain 54 chimeras, representing 45.8% of its content. These figures far exceed previous estimates of artifacts within public repositories and further highlight the urgent need for all researchers to adequately screen their libraries prior to submission. Mallard is freely available from our website at http://www.cardiff.ac.uk/biosi/research/biosoft/.
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Project description:In ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based and de novo CATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based or de novo mode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units.
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Project description:DECIPHER is a new method for finding 16S rRNA chimeric sequences by the use of a search-based approach. The method is based upon detecting short fragments that are uncommon in the phylogenetic group where a query sequence is classified but frequently found in another phylogenetic group. The algorithm was calibrated for full sequences (fs_DECIPHER) and short sequences (ss_DECIPHER) and benchmarked against WigeoN (Pintail), ChimeraSlayer, and Uchime using artificially generated chimeras. Overall, ss_DECIPHER and Uchime provided the highest chimera detection for sequences 100 to 600 nucleotides long (79% and 81%, respectively), but Uchime's performance deteriorated for longer sequences, while ss_DECIPHER maintained a high detection rate (89%). Both methods had low false-positive rates (1.3% and 1.6%). The more conservative fs_DECIPHER, benchmarked only for sequences longer than 600 nucleotides, had an overall detection rate lower than that of ss_DECIPHER (75%) but higher than those of the other programs. In addition, fs_DECIPHER had the lowest false-positive rate among all the benchmarked programs (<0.20%). DECIPHER was outperformed only by ChimeraSlayer and Uchime when chimeras were formed from closely related parents (less than 10% divergence). Given the differences in the programs, it was possible to detect over 89% of all chimeras with just the combination of ss_DECIPHER and Uchime. Using fs_DECIPHER, we detected between 1% and 2% additional chimeras in the RDP, SILVA, and Greengenes databases from which chimeras had already been removed with Pintail or Bellerophon. DECIPHER was implemented in the R programming language and is directly accessible through a webpage or by downloading the program as an R package (http://DECIPHER.cee.wisc.edu).
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Project description:16S rRNA gene profiling has revolutionized the field of microbial ecology. Many researchers in various fields have embraced this technology to investigate bacterial compositions of samples derived from many different ecosystems. However, it is important to acknowledge the current limitations and drawbacks of 16S rRNA gene profiling. Although sample handling, DNA extraction methods and the choice of universal 16S rRNA gene PCR primers are well known factors that could seriously affect the final results of microbiota profiling studies, inevitable amplification artifacts, such as chimera formation and PCR competition, are seldom appreciated. Here we report on a novel micelle based amplification strategy, which overcomes these limitations via the clonal amplification of targeted DNA molecules. Our results show that micelle PCR drastically reduces chimera formation by a factor of 38 (1.5% vs. 56.9%) compared with traditional PCR, resulting in improved microbial diversity estimates. In addition, compartmentalization during micelle PCR prevents PCR competition due to unequal amplification rates of different 16S template molecules, generating robust and accurate 16S microbiota profiles required for comparative studies (e.g. longitudinal surveys).
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Project description:Specific hybridization assays for intermediates in rRNA synthesis (pre-rRNA) may become useful for monitoring the growth activity of individual microbial species in complex natural systems. This possibility depends upon the assumption that rRNA processing in microbial cells continues after growth and pre-rRNA synthesis cease, resulting in drainage of the pre-rRNA pool. This is not the case in many eukaryotic cells, but less is known about the situation in bacteria. Therefore, we used DNA probes to measure steady-state cellular pre-16S rRNA pools during growth state transitions in Escherichia coli. Pre-16S rRNA became undetectable when cells entered the stationary phase on rich medium and was replenished upon restoration of favorable growth conditions. These fluctuations were of much greater magnitude than concurrent fluctuations in the mature 16S rRNA pool. The extent of pre-16S rRNA depletion depended upon the circumstances limiting growth. It was significantly more pronounced in carbon-energy-starved cells than in nitrogen-starved cells or in cells treated with energy uncouplers. In the presence of the transcriptional inhibitor rifampin, rates of pre-16S rRNA depletion in carbon-energy-starved cells and nitrogen-starved cells were similar, suggesting that the difference between these conditions resides primarily at the level of pre-rRNA synthesis. Chloramphenicol, which inhibits the final steps in rRNA maturation, halted pre-16S rRNA depletion under all conditions. The data show that E. coli cells continue to process pre-rRNA after growth and rrn operon transcription cease, leading to drainage of the pre-rRNA pool. This supports the feasibility of using pre-rRNA-targeted probes to monitor bacterial growth in natural systems, with the caveat that patterns of pre-rRNA depletion vary with the conditions limiting growth.
| S-EPMC179279 | biostudies-other
Project description:The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.
| S-EPMC4315456 | biostudies-literature
Project description:BackgroundSequencing of the PCR-amplified 16S rRNA gene has become a common approach to microbial community investigations in the fields of human health and environmental sciences. This approach, however, is difficult when the amount of DNA is too low to be amplified by standard PCR. Nested PCR can be employed as it can amplify samples with DNA concentration several-fold lower than standard PCR. However, potential biases with nested PCRs that could affect measurement of community structure have received little attention.ResultsIn this study, we used 17 DNAs extracted from vaginal swabs and 12 DNAs extracted from stool samples to study the influence of nested PCR amplification of the 16S rRNA gene on the estimation of microbial community structure using Illumina MiSeq sequencing. Nested and standard PCR methods were compared on alpha- and beta-diversity metrics and relative abundances of bacterial genera. The effects of number of cycles in the first round of PCR (10 vs. 20) and microbial diversity (relatively low in vagina vs. high in stool) were also investigated. Vaginal swab samples showed no significant difference in alpha diversity or community structure between nested PCR and standard PCR (one round of 40 cycles). Stool samples showed significant differences in alpha diversity (except Shannon's index) and relative abundance of 13 genera between nested PCR with 20 cycles in the first round and standard PCR (P<0.01), but not between nested PCR with 10 cycles in the first round and standard PCR. Operational taxonomic units (OTUs) that had low relative abundance (sum of relative abundance <0.167) accounted for most of the distortion (>27% of total OTUs in stool).ConclusionsNested PCR introduced bias in estimated diversity and community structure. The bias was more significant for communities with relatively higher diversity and when more cycles were applied in the first round of PCR. We conclude that nested PCR could be used when standard PCR does not work. However, rare taxa detected by nested PCR should be validated by other technologies.
| S-EPMC4509648 | biostudies-literature
Project description:BackgroundThe low cost of 16S rRNA gene sequencing facilitates population-scale molecular epidemiological studies. Existing computational algorithms can resolve 16S rRNA gene sequences into high-resolution amplicon sequence variants (ASVs), which represent consistent labels comparable across studies. Assigning these ASVs to species-level taxonomy strengthens the ecological and/or clinical relevance of 16S rRNA gene-based microbiota studies and further facilitates data comparison across studies.ResultsTo achieve this, we developed a broadly applicable method for constructing high-resolution training sets based on the phylogenic relationships among microbes found in a habitat of interest. When used with the naïve Bayesian Ribosomal Database Project (RDP) Classifier, this training set achieved species/supraspecies-level taxonomic assignment of 16S rRNA gene-derived ASVs. The key steps for generating such a training set are (1) constructing an accurate and comprehensive phylogenetic-based, habitat-specific database; (2) compiling multiple 16S rRNA gene sequences to represent the natural sequence variability of each taxon in the database; (3) trimming the training set to match the sequenced regions, if necessary; and (4) placing species sharing closely related sequences into a training-set-specific supraspecies taxonomic level to preserve subgenus-level resolution. As proof of principle, we developed a V1-V3 region training set for the bacterial microbiota of the human aerodigestive tract using the full-length 16S rRNA gene reference sequences compiled in our expanded Human Oral Microbiome Database (eHOMD). We also overcame technical limitations to successfully use Illumina sequences for the 16S rRNA gene V1-V3 region, the most informative segment for classifying bacteria native to the human aerodigestive tract. Finally, we generated a full-length eHOMD 16S rRNA gene training set, which we used in conjunction with an independent PacBio single molecule, real-time (SMRT)-sequenced sinonasal dataset to validate the representation of species in our training set. This also established the effectiveness of a full-length training set for assigning taxonomy of long-read 16S rRNA gene datasets.ConclusionHere, we present a systematic approach for constructing a phylogeny-based, high-resolution, habitat-specific training set that permits species/supraspecies-level taxonomic assignment to short- and long-read 16S rRNA gene-derived ASVs. This advancement enhances the ecological and/or clinical relevance of 16S rRNA gene-based microbiota studies. Video Abstract.
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Project description:There is a lack of free software that provides a professional and smooth experience in text editing and markup for qualitative data analysis. Word processing software like Microsoft Word provides a good editing experience, allowing the researcher to effortlessly add comments to text portions. However, organizing the keywords and categories in the comments can become a more difficult task when the amount of data increases. We present WordCommentsAnalyzer, a software tool that is written in C# using .NET Framework and OpenXml, which helps a qualitative researcher to organize codes when using Microsoft Word as the primary text markup software. WordCommentsAnalyzer provides an effective user interface to count codes, to organize codes in a code hierarchy, and to see various data extracts belonging to each code. It also offers basic visualization tools. We illustrate how to use this software by conducting a preliminary content analysis on Tweets with the #successfulaging hashtag. We also demonstrate that the software has satisfactory performance on a large dataset of Iranian journals abstracts. We hope this open-source software will facilitate qualitative data analysis by researchers who are interested in using Word for this purpose.
| S-EPMC6137411 | biostudies-literature
Project description: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.
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