This repository provides analysis code and results produced during evaluation of metagenomic sequencing (MGS) data collected through the Mosaic Standards Challenge. The Mosaic Standards Challenge asked participating laboratories analyze the same set of 7 samples using their own favored MGS laboratory methods. Each lab submitted their raw sequencing results and protocol information. The resulting MGS data was analyzed through a common bioinformatic pipeline and then evaluated to determine the effects of methodological choices.

This repository provides the raw data and analysis code used to demonstrate the implementation of a mathematical framework that uses internal standards to correct for compositionally in MGS datasets. Some of the data contained herein was previously published and is reanalyzed in the context of this new framework, while other datasets represent newly analyzed samples. Please refer to the associated publication for further discussion and a full exploration of the results.

This repository contains raw and intermediate data files as well as analysis code from the manuscript "Evaluating the Analytical Performance of Direct-to-Consumer Gut Microbiome Testing Services". In this analysis a pilot version of the homogenized whole human stool reference material was submitted as a sample to commercial direct to consumer microbiome testing companies. In addition, NIST conducted in house characterization by 16S amplicon sequencing and whole metagenomic sequencing (WMS) on the same sample. Raw sequencing data from the sequencing providers was not obtained for this study; however sequencing files (fastq) both 16S amplicon sequencing analysis and whole metagenomic sequencing (WMS) conducted at NIST are included. In addition, the fastq files from other donors that contributed to the pilot material and included in this analysis to represent biological diversity are also included in this record.

Amplicon sequence variants from enrichment cultures and non-enriched water samples generated with primer sets targeting the V4 region of the 16SrRNA “This research dataset has been reviewed in accordance with U.S. Environmental Protection Agency (U.S. EPA), Office of Research and Development, and approved for release. Mention of brand names or vendors does not constitute an endorsement of products or services by the U.S. EPA.”

Background PCR amplification of bacterial 16S rRNA genes provides the most comprehensive and flexible means of sampling bacterial communities. Sequence analysis of these cloned fragments can provide a qualitative and quantitative insight of the microbial population under scrutiny although this approach is not suited to large-scale screenings. Other methods, such as denaturing gradient gel electrophoresis, heteroduplex or terminal restriction fragment analysis are rapid and therefore amenable to field-scale experiments. A very recent addition to these analytical tools is represented by microarray technology. Results Here we present our results using a Universal DNA Microarray approach as an analytical tool for bacterial discrimination. The proposed procedure is based on the properties of the DNA ligation reaction and requires the design of two probes specific for each target sequence. One oligo carries a fluorescent label and the other a unique sequence (cZipCode or complementary ZipCode) which identifies a ligation product. Ligated fragments, obtained in presence of a proper template (a PCR amplified fragment of the 16s rRNA gene) contain either the fluorescent label or the unique sequence and therefore are addressed to the location on the microarray where the ZipCode sequence has been spotted. Such an array is therefore "Universal" being unrelated to a specific molecular analysis. Here we present the design of probes specific for some groups of bacteria and their application to bacterial diagnostics. Conclusions The combined use of selective probes, ligation reaction and the Universal Array approach yielded an analytical procedure with a good power of discrimination among bacteria.

This repository contains the raw data and analysis scripts supporting the associated publication which introduces a framework to help researchers select fit-for-purpose microbial cell counting methods and optimize protocols for quantification of microbial total cells and viable cells. Escherichia coli cells were enumerated using four methods (colony forming unit assay, impedance flow cytometry - Multisizer 4, impedance flow cytometry - BactoBox, and fluorescent flow cytometry - CytoFLEX LX) and repeated on multiple dates. The experimental design for a single date starts with a cell stock that is divided into 18 sample replicates (3 each for 6 different dilution factors), and each sample is assayed one or two times for a total of 30 observations. Raw data files are provided from the Multisizer 4 (*.#m4) and CytoFLEX LX (*.fcs 3.0). The colony forming unit assay and BactoBox readings are recorded for each date as are the derived results from the Multisizer 4 and CytoFLEX LX. Also provided are an example analysis script for the *.fcs files and the statistical analysis that was performed.

This repository contains the raw data and analysis scripts supporting the associated publication which introduces a framework to help researchers select fit-for-purpose microbial cell counting methods and optimize protocols for quantification of microbial total cells and viable cells. Escherichia coli cells were enumerated using four methods (colony forming unit assay, impedance flow cytometry - Multisizer 4, impedance flow cytometry - BactoBox, and fluorescent flow cytometry - CytoFLEX LX) and repeated on multiple dates. The experimental design for a single date starts with a cell stock that is divided into 18 sample replicates (3 each for 6 different dilution factors), and each sample is assayed one or two times for a total of 30 observations. Raw data files are provided from the Multisizer 4 (*.#m4) and CytoFLEX LX (*.fcs 3.0). The colony forming unit assay and BactoBox readings are recorded for each date as are the derived results from the Multisizer 4 and CytoFLEX LX. Also provided are an example analysis script for the *.fcs files and the statistical analysis that was performed.

The quantitative microbial risk assessment (QMRA) approach applied to the evaluations presented in this publication was described previously in a peer-reviewed published paper. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.

The quantitative microbial risk assessment (QMRA) approach applied to the evaluations presented in this publication was described previously in a peer-reviewed published paper. Citation information for this dataset can be found in the EDG's Metadata Reference Information section and Data.gov's References section.