GAUGE-Annotated Microbial Transcriptomic Data Facilitate Parallel Mining and High-Throughput Reanalysis To Form Data-Driven Hypotheses

GAUGE-Annotated Microbial Transcriptomic Data Facilitate Parallel Mining and High-Throughput Reanalysis To Form Data-Driven Hypotheses

GEO archives transcriptomic data from over 5,800 microbial experiments and allows researchers to answer questions not directly addressed in published papers. However, less than 4% of the microbial data sets include the sample group annotations required for high-throughput reanalysis.

Bibliographic Details
Main Authors: Zhongyou Li, Katja Koeppen, Victoria I. Holden, Samuel L. Neff, Liviu Cengher, Elora G. Demers, Dallas L. Mould, Bruce A. Stanton, Thomas H. Hampton
Format: Article
Language:English
Published: American Society for Microbiology 2021-04-01
Series:mSystems
Online Access:https://journals.asm.org/doi/10.1128/mSystems.01305-20

Internet

https://journals.asm.org/doi/10.1128/mSystems.01305-20

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