HVRLocator: a computationally efficient tool for identifying hypervariable regions in large 16S rRNA datasets

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• 16S rRNA gene metabarcoding is a cost-effective method for microbial diversity assessment.
• Public datasets often lack standardized metadata, hindering accurate data reuse.
• Critical metadata includes sequenced hypervariable regions and primers used.

Purpose of the Study:

• Introduce HVRLocator, a computational tool for analyzing 16S rRNA metabarcoding data.
• Address the challenge of missing or unreliable metadata in public sequence archives.
• Enable accurate curation and large-scale processing of 16S rRNA data.

Main Methods:

• HVRLocator identifies 16S rRNA amplicon start/end positions.
• The tool determines corresponding hypervariable regions.
• It detects the presence of primer sequences within the data.

Main Results:

• HVRLocator processes archived sequences at 6.5 samples/minute.
• It accurately detects amplicon positions and hypervariable regions across diverse datasets.
• The tool flags misannotated metadata and problematic sequences, aiding data curation.

Conclusions:

• HVRLocator overcomes metadata limitations in 16S rRNA studies.
• Accurate identification of amplicon regions and primers ensures reliable data processing.
• Enables reproducible microbial studies, syntheses, and meta-analyses.