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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
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GAUGE-Annotated Microbial Transcriptomic Data Facilitate Parallel Mining and High-Throughput Reanalysis To Form

Zhongyou Li1, Katja Koeppen1, Victoria I Holden1

  • 1Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA.

Msystems
|March 24, 2021
PubMed
Summary
This summary is machine-generated.

The GAUGE algorithm automatically annotates microbial transcriptomic data in NCBI GEO, making 33% of datasets available for reanalysis. This enables new scientific insights, like identifying a gene crucial for biofilm formation in Pseudomonas aeruginosa.

Keywords:
Pseudomonas aeruginosabiofilmsbioinformaticsgene expressiongenomics

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • NCBI Gene Expression Omnibus (GEO) archives vast microbial transcriptomic data.
  • Less than 4% of microbial datasets in GEO have essential sample group annotations for reanalysis.
  • This lack of annotation hinders high-throughput reanalysis and data reuse, especially for studies post-2014.

Purpose of the Study:

  • To develop an automated algorithm (GAUGE) for annotating microbial GEO datasets.
  • To increase the accessibility of microbial transcriptomic data for differential gene expression analysis.
  • To create a user-friendly interface (GAPE) for analyzing annotated microbial datasets.

Main Methods:

  • Developed GAUGE (general annotation using text/data group ensembles) algorithm for automated annotation of GEO microbial datasets (microarray and RNA sequencing).
  • Created GAPE (GAUGE-annotated Pseudomonas aeruginosa and Escherichia coli transcriptomic compendia for reanalysis), a Shiny Web interface.
  • Validated GAUGE annotations against human curators and performed wet-bench experiments to confirm bioinformatic predictions.

Main Results:

  • GAUGE increased the percentage of analyzable microbial datasets from 4% to 33%.
  • Eighty-nine percent of GAUGE annotations matched human curator assignments.
  • GAPE analysis identified a previously uncharacterized gene (PA3923) in P. aeruginosa frequently differentially expressed and coregulated with biofilm formation genes; wet-bench experiments confirmed its role in biofilm defect.

Conclusions:

  • GAUGE significantly enhances the reusability of public microbial transcriptomic data.
  • GAPE facilitates data-driven hypothesis generation and discovery through accessible analysis of annotated compendia.
  • The developed tools (GAUGE and GAPE) are freely available and can be adapted for other bacterial species.