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Published on: April 4, 2018
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Speeding genomic island discovery through systematic design of reference database composition
Steven L Yu1, Catherine M Mageeney1, Fatema Shormin2
1Sandia National Labs, Livermore, California, United States of America.
Plos One
|March 13, 2024
Summary
Optimizing genomic island (GI) identification software using smaller, species-focused databases significantly reduced runtime by 65-fold. This approach minimizes false positives and maintains high accuracy for identifying mobile genetic elements in bacterial genomes.
Area of Science:
- Genomics
- Bioinformatics
- Microbial Ecology
Background:
- Genomic islands (GIs) are mobile genetic elements impacting bacterial phenotypes.
- Comparative genomics enables GI identification, but large databases slow analysis.
- Exploring smaller, species-focused databases is key for efficient GI detection.
Purpose of the Study:
- To optimize comparative software for genomic island identification.
- To investigate the impact of database size on GI detection accuracy and runtime.
- To develop a more efficient database design for comparative genomics.
Main Methods:
- Implemented a SMAll Ranked Tailored (SMART) database design by setting size limits.
- Compared GI identification performance using varying database sizes and taxonomic scopes.
- Simulated database designs for large and small bacterial species.
Main Results:
- Runtime for GI identification was accelerated approximately 65-fold with the SMART database design.
- Overlarge databases increased false positive rates for GI calls.
- Strictly intra-species databases could be enhanced with closely related taxa to improve prophage yield without increasing false positives.
Conclusions:
- The SMART database design dramatically decreased runtime with minimal loss of prophages.
- This optimized approach offers potential utility for various comparative genomics projects.
- Efficient database management is crucial for accurate and rapid genomic island identification.
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