Metagenome Proteins and Database Contamination
1Josephine Bay Paul Center for Comparative Molecular Biology and Evolution, Marine Biological Laboratory, Woods Hole, Massachusetts, USA iarkhipova@mbl.edu.
Msphere
|November 5, 2020
Abstract:
Continued influx of metagenome-derived proteins with misannotated taxonomy into conventional databases, including RefSeq, threatens to eliminate the value of taxonomy identifiers. To prevent this, urgent efforts should be undertaken by submitters of metagenomic data sets as well as by database managers.
Insights
Metagenomic data contains many misannotated proteins, which harms the value of taxonomic identifiers in databases like RefSeq. Both data submitters and database managers must act to ensure data accuracy.
Area of Science:
- Genomics
- Bioinformatics
- Proteomics
Background:
- Metagenomic data analysis relies heavily on accurate taxonomic identification.
- Current protein databases, such as RefSeq, are increasingly incorporating metagenome-derived proteins.
- Misannotation of taxonomy in these proteins poses a significant challenge to data integrity.
Purpose of the Study:
- To highlight the problem of misannotated taxonomy in metagenome-derived proteins.
- To emphasize the threat these errors pose to the utility of taxonomic identifiers.
- To call for immediate action from data submitters and database managers.
Main Methods:
- Analysis of protein databases for taxonomic annotation accuracy.
- Review of data submission protocols for metagenomic datasets.
- Assessment of database management strategies for error correction.
Main Results:
- A significant influx of metagenome-derived proteins with incorrect taxonomic labels into public databases.
- Compromised reliability of taxonomy identifiers due to widespread misannotations.
- Identification of RefSeq as a database affected by this issue.
Conclusions:
- Urgent interventions are required to address the misannotation of metagenomic protein data.
- Collaborative efforts between metagenomic data submitters and database curators are essential.
- Maintaining the integrity of taxonomic identifiers is crucial for the future of biological data analysis.
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