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Updated: Feb 8, 2026
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The ITS2 Database
Published on: March 12, 2012
32.3K
Removing contaminants from databases of draft genomes.
Jennifer Lu1,2, Steven L Salzberg1,2,3
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.
Plos Computational Biology
|June 26, 2018
Summary
Metagenomic sequencing aids infection diagnosis but suffers from genome contamination. This study developed a bioinformatics system to create a clean eukaryotic pathogen genome database, improving diagnostic accuracy and reducing false positives.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Metagenomic sequencing of patient samples is a powerful tool for diagnosing human infections by capturing all DNA/RNA from pathogens.
- Accurate pathogen identification relies on high-quality reference genomes, but contamination in public databases leads to false positives.
- Existing genomic databases for eukaryotic pathogens contain contaminants (human, bacterial, archaeal, viral) and low-complexity sequences, compromising diagnostic reliability.
Purpose of the Study:
- To develop and validate a bioinformatics system for removing contamination and low-complexity sequences from eukaryotic pathogen genomes.
- To create a curated, "clean" database of eukaryotic pathogen genomes for improved metagenomic analysis.
- To enhance the accuracy and reliability of pathogen detection in clinical metagenomic samples.
Main Methods:
- Developed a bioinformatics pipeline to identify and remove non-target DNA/RNA sequences (human, bacterial, archaeal, viral) from draft eukaryotic pathogen genomes.
- Implemented filtering for low-complexity genomic sequences to eliminate another source of false positives.
- Applied the pipeline to a comprehensive database of sequenced eukaryotic pathogen genomes to generate a cleaned dataset.
Main Results:
- Successfully produced a database of "clean" eukaryotic pathogen genomes by removing contaminating sequences and low-complexity regions.
- Demonstrated that the new database significantly reduces false positives when identifying eukaryotic pathogens in metagenomic samples.
- Showcased improved sensitivity in detecting eukaryotic pathogens compared to using original, uncleaned genome databases.
Conclusions:
- The developed bioinformatics system effectively purges contamination and low-complexity sequences from eukaryotic pathogen genome databases.
- The resulting "clean" genome database enhances the accuracy and sensitivity of metagenomic diagnostics for human infections.
- This work provides a critical resource for improving the reliability of pathogen identification in clinical metagenomic sequencing.
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