Data provided by National Institute of Standards and Technology
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.
About this Dataset
Updated: 2025-04-06
Metadata Last Updated:
2023-02-03 00:00:00
Date Created:
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Data Provided by:
Dataset Owner:
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| Title | Variability and Bias in Microbiome Metagenomic Sequencing: an Interlaboratory Study Comparing Experimental Protocols |
|---|---|
| Description | 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. |
| Modified | 2023-02-03 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | microbiome; methodology; metagenomic sequencing |
{
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"fn": "Samuel P. Forry"
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"title": "Variability and Bias in Microbiome Metagenomic Sequencing: an Interlaboratory Study Comparing Experimental Protocols",
"description": "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. ",
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"theme": [
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