Dataset Information

Black Box Chimera Check (B2C2): a Windows-Based Software for Batch Depletion of Chimeras from Bacterial 16S rRNA Gene Datasets.

ABSTRACT: The existing chimera detection programs are not specifically designed for "next generation" sequence data. Technologies like Roche 454 FLX and Titanium have been adapted over the past years especially with the introduction of bacterial tag-encoded FLX/Titanium amplicon pyrosequencing methodologies to produce over one million 250-600 bp 16S rRNA gene reads that need to be depleted of chimeras prior to downstream analysis. Meeting the needs of basic scientists who are venturing into high-throughput microbial diversity studies such as those based upon pyrosequencing and specifically providing a solution for Windows users, the B2C2 software is designed to be able to accept files containing large multi-FASTA formatted sequences and screen for possible chimeras in a high throughput fashion. The graphical user interface (GUI) is also able to batch process multiple files. When compared to popular chimera screening software the B2C2 performed as well or better while dramatically decreasing the amount of time required generating and screening results. Even average computer users are able to interact with the Windows .Net GUI-based application and define the stringency to which the analysis should be done. B2C2 may be downloaded from http://www.researchandtesting.com/B2C2.

SUBMITTER: Gontcharova V

PROVIDER: S-EPMC3040993 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Black Box Chimera Check (B2C2): a Windows-Based Software for Batch Depletion of Chimeras from Bacterial 16S rRNA Gene Datasets.

Gontcharova Viktoria V Youn Eunseog E Wolcott Randall D RD Hollister Emily B EB Gentry Terry J TJ Dowd Scot E SE

The open microbiology journal 20100811

The existing chimera detection programs are not specifically designed for "next generation" sequence data. Technologies like Roche 454 FLX and Titanium have been adapted over the past years especially with the introduction of bacterial tag-encoded FLX/Titanium amplicon pyrosequencing methodologies to produce over one million 250-600 bp 16S rRNA gene reads that need to be depleted of chimeras prior to downstream analysis. Meeting the needs of basic scientists who are venturing into high-throughpu ...[more]

PMID: 21339894

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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.

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: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.

Dataset Information

Black Box Chimera Check (B2C2): a Windows-Based Software for Batch Depletion of Chimeras from Bacterial 16S rRNA Gene Datasets.

Publications

Black Box Chimera Check (B2C2): a Windows-Based Software for Batch Depletion of Chimeras from Bacterial 16S rRNA Gene Datasets.

Similar Datasets

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