Dataset Information

Quality-score refinement of SSU rRNA gene pyrosequencing differs across gene region for environmental samples.

ABSTRACT: Due to potential sequencing errors in pyrosequencing data, species richness and diversity indices of microbial systems can be miscalculated. The "traditional" sequence refinement method is not sufficient to account for overestimations (e.g., length, primer errors, ambiguous nucleotides). Recent in silico and single-organism studies have revealed the importance of sequence quality scores in the estimation of ecological indices; however, this is the first study to compare quality-score stringencies across four regions of the SSU rRNA gene sequence (V1V2, V3, V4, and V6) with actual environmental samples compared directly to corresponding clone libraries produced from the same primer sets. The nucleic acid sequences determined via pyrosequencing were subjected to varying quality-score cutoffs that ranged from 25 to 32, and at each quality-score cutoff, either 10 or 15 % of the nucleotides were allowed to be below the cutoff. When species richness estimates were compared for the tested samples, the cutoff values of Q27(15%), Q30(10%), and Q32(15%) for V1V2, V4, and V6, respectively, estimated similar values as obtained with clone libraries and Sanger sequencing. The most stringent Q tested (Q32(10%)) was not enough to account for species richness inflation of the V3 region pyrosequence data. Results indicated that quality-score assessment greatly improved estimates of ecological indices for environmental samples (species richness and ?-diversity) and that the effect of quality-score filtering was region-dependent.

SUBMITTER: Bowen De Leon K

PROVIDER: S-EPMC3391548 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quality-score refinement of SSU rRNA gene pyrosequencing differs across gene region for environmental samples.

Bowen De León Kara K Ramsay Bradley D BD Fields Matthew W MW

Microbial ecology 20120405 2

Due to potential sequencing errors in pyrosequencing data, species richness and diversity indices of microbial systems can be miscalculated. The "traditional" sequence refinement method is not sufficient to account for overestimations (e.g., length, primer errors, ambiguous nucleotides). Recent in silico and single-organism studies have revealed the importance of sequence quality scores in the estimation of ecological indices; however, this is the first study to compare quality-score stringencie ...[more]

PMID: 22476815

Similar Datasets

Project description:BACKGROUND: Sample storage conditions, extraction methods, PCR primers, and parameters are major factors that affect metagenomics analysis based on microbial 16S rRNA gene sequencing. Most published studies were limited to the comparison of only one or two types of these factors. Systematic multi-factor explorations are needed to evaluate the conditions that may impact validity of a microbiome analysis. This study was aimed to improve methodological options to facilitate the best technical approaches in the design of a microbiome study. Three readily available mock bacterial community materials and two commercial extraction techniques, Qiagen DNeasy and MO BIO PowerSoil DNA purification methods, were used to assess procedures for 16S ribosomal DNA amplification and pyrosequencing-based analysis. Primers were chosen for 16S rDNA quantitative PCR and amplification of region V3 to V1. Swabs spiked with mock bacterial community cells and clinical oropharyngeal swabs were incubated at respective temperatures of -80°C, -20°C, 4°C, and 37°C for 4 weeks, then extracted with the two methods, and subjected to pyrosequencing and taxonomic and statistical analyses to investigate microbiome profile stability. RESULTS: The bacterial compositions for the mock community DNA samples determined in this study were consistent with the projected levels and agreed with the literature. The quantitation accuracy of abundances for several genera was improved with changes made to the standard Human Microbiome Project (HMP) procedure. The data for the samples purified with DNeasy and PowerSoil methods were statistically distinct; however, both results were reproducible and in good agreement with each other. The temperature effect on storage stability was investigated by using mock community cells and showed that the microbial community profiles were altered with the increase in incubation temperature. However, this phenomenon was not detected when clinical oropharyngeal swabs were used in the experiment. CONCLUSIONS: Mock community materials originated from the HMP study are valuable controls in developing 16S metagenomics analysis procedures. Long-term exposure to a high temperature may introduce variation into analysis for oropharyngeal swabs, suggestive of storage at 4°C or lower. The observed variations due to sample storage temperature are in a similar range as the intrapersonal variability among different clinical oropharyngeal swab samples.

Project description:Protists have traditionally been identified by cultivation and classified taxonomically based on their cellular morphologies and behavior. In the past decade, however, many novel protist taxa have been identified using cultivation independent ssu rRNA sequence surveys. New rRNA "phylotypes" from uncultivated eukaryotes have no connection to the wealth of prior morphological descriptions of protists. To link phylogenetically informative sequences with taxonomically informative morphological descriptions, we demonstrate several methods for combining whole cell rRNA-targeted fluorescent in situ hybridization (FISH) with cytoskeletal or organellar immunostaining. Either eukaryote or ciliate-specific ssu rRNA probes were combined with an anti-α-tubulin antibody or phalloidin, a common actin stain, to define cytoskeletal features of uncultivated protists in several environmental samples. The eukaryote ssu rRNA probe was also combined with Mitotracker® or a hydrogenosomal-specific anti-Hsp70 antibody to localize mitochondria and hydrogenosomes, respectively, in uncultivated protists from different environments. Using rRNA probes in combination with immunostaining, we linked ssu rRNA phylotypes with microtubule structure to describe flagellate and ciliate morphology in three diverse environments, and linked Naegleria spp. to their amoeboid morphology using actin staining in hay infusion samples. We also linked uncultivated ciliates to morphologically similar Colpoda-like ciliates using tubulin immunostaining with a ciliate-specific rRNA probe. Combining rRNA-targeted FISH with cytoskeletal immunostaining or stains targeting specific organelles provides a fast, efficient, high throughput method for linking genetic sequences with morphological features in uncultivated protists. When linked to phylotype, morphological descriptions of protists can both complement and vet the increasing number of sequences from uncultivated protists, including those of novel lineages, identified in diverse environments.

Project description:High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (108, 107, 106, 105 and 104 microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 106 cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 106 bacteria per sample for robust and reproducible microbiota analysis.

Dataset Information

Quality-score refinement of SSU rRNA gene pyrosequencing differs across gene region for environmental samples.

Publications

Quality-score refinement of SSU rRNA gene pyrosequencing differs across gene region for environmental samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets