Article

Identification and Quantification of Abundant Species from Pyrosequences of 16S rRNA by Consensus Alignment

School of Informatics and Computing, Bloomington, IN 47408, U.S.A.
Proceedings. IEEE International Conference on Bioinformatics and Biomedicine 02/2011; 2010:153-157. DOI: 10.1109/BIBM.2010.5706555
Source: PubMed

ABSTRACT

16S rRNA gene profiling has recently been boosted by the development of pyrosequencing methods. A common analysis is to group pyrosequences into Operational Taxonomic Units (OTUs), such that reads in an OTU are likely sampled from the same species. However, species diversity estimated from error-prone 16S rRNA pyrosequences may be inflated because the reads sampled from the same 16S rRNA gene may appear different, and current OTU inference approaches typically involve time-consuming pairwise/multiple distance calculation and clustering. I propose a novel approach AbundantOTU based on a Consensus Alignment (CA) algorithm, which infers consensus sequences, each representing an OTU, taking advantage of the sequence redundancy for abundant species. Pyrosequencing reads can then be recruited to the consensus sequences to give quantitative information for the corresponding species. As tested on 16S rRNA pyrosequence datasets from mock communities with known species, AbundantOTU rapidly reported identified sequences of the source 16S rRNAs and the abundances of the corresponding species. AbundantOTU was also applied to 16S rRNA pyrosequence datasets derived from real microbial communities and the results are in general agreement with previous studies.

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    • "Sequence reads were aligned with our own custom multiple alignment tool known as the Illinois-Mayo Taxon Operations for RNA Dataset Organization (IM-TORNADO) that merges paired end reads into a single multiple alignment and obtains taxa calls [19]. IM-TORNADO then clusters sequences into operational taxonomic units (OTUs) using AbundantOTU+ [20]. Further processing for visualization was performed using QIIME [21]. "
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    ABSTRACT: Objective To assess the vaginal microbiome throughout full-term uncomplicated pregnancy. Methods Vaginal swabs were obtained from twelve pregnant women at 8-week intervals throughout their uncomplicated pregnancies. Patients with symptoms of vaginal infection or with recent antibiotic use were excluded. Swabs were obtained from the posterior fornix and cervix at 8–12, 17–21, 27–31, and 36–38 weeks of gestation. The microbial community was profiled using hypervariable tag sequencing of the V3–V5 region of the 16S rRNA gene, producing approximately 8 million reads on the Illumina MiSeq. Results Samples were dominated by a single genus, Lactobacillus, and exhibited low species diversity. For a majority of the patients (n = 8), the vaginal microbiome was dominated by Lactobacillus crispatus throughout pregnancy. Two patients showed Lactobacillus iners dominance during the course of pregnancy, and two showed a shift between the first and second trimester from L. crispatus to L. iners dominance. In all of the samples only these two species were identified, and were found at an abundance of higher than 1% in this study. Comparative analyses also showed that the vaginal microbiome during pregnancy is characterized by a marked dominance of Lactobacillus species in both Caucasian and African-American subjects. In addition, our Caucasian subject population clustered by trimester and progressed towards a common attractor while African-American women clustered by subject instead and did not progress towards a common attractor. Conclusion Our analyses indicate normal pregnancy is characterized by a microbiome that has low diversity and high stability. While Lactobacillus species strongly dominate the vaginal environment during pregnancy across the two studied ethnicities, observed differences between the longitudinal dynamics of the analyzed populations may contribute to divergent risk for pregnancy complications. This helps establish a baseline for investigating the role of the microbiome in complications of pregnancy such as preterm labor and preterm delivery.
    Full-text · Article · Jun 2014 · PLoS ONE
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    • "Reads with at least 400 nucleotides (nt) were trimmed and checked for chimerism (Edgar et al., 2011). We obtained consensus OTU clusters and representative sequences using abundant OTU (Ye, 2010). Representative sequences and the OTU table were used for further analysis with the QIIME pipeline as detailed above (Caporaso et al., 2010). "
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    ABSTRACT: Glycoside hydrolases (GHs), the enzymes that breakdown complex carbohydrates, are a highly diversified class of key enzymes associated with the gut microbiota and its metabolic functions. To learn more about the diversity of GHs and their potential role in a variety of gut microbiomes, we used a combination of 16S, metagenomic and targeted amplicon sequencing data to study one of these enzyme families in detail. Specifically, we employed a functional gene-targeted metagenomic approach to the 1-4-α-glucan-branching enzyme (gBE) gene in the gut microbiomes of four host species (human, chicken, cow and pig). The characteristics of operational taxonomic units (OTUs) and operational glucan-branching units (OGBUs) were distinctive in each of hosts. Human and pig were most similar in OTUs profiles while maintaining distinct OGBU profiles. Interestingly, the phylogenetic profiles identified from 16S and gBE gene sequences differed, suggesting the presence of different gBE genes in the same OTU across different vertebrate hosts. Our data suggest that gene-targeted metagenomic analysis is useful for an in-depth understanding of the diversity of a particular gene of interest. Specific carbohydrate metabolic genes appear to be carried by distinct OTUs in different individual hosts and among different vertebrate species' microbiomes, the characteristics of which differ according to host genetic background and/or diet.The ISME Journal advance online publication, 10 October 2013; doi:10.1038/ismej.2013.167.
    Full-text · Article · Oct 2013 · The ISME Journal
    • "Chimeras were detected and removed using the UCHIME algorithm (Edgar et al. 2011) using both reference-based (using the SILVA SSU database;Quast et al. 2013) and de novo chimera detection. Remaining sequences were clustered to 97% similarity using abundant OTU to construct operational taxonomic units (OTUs) (Yuzhen 2010). Student's t-tests were conducted to remove OTUs that were probably matches for contaminant bacteria. "
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    No preview · Conference Paper · Aug 2013
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