TaxMan: a server to trim rRNA reference databases and inspect taxonomic coverage

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Amplicon sequencing of ribosomal RNA (rRNA) genes is crucial for identifying bacterial species in complex communities.
• Short next-generation sequencing reads necessitate careful selection of rRNA gene regions and primers for accurate taxonomic assignment.
• Existing reference databases and analysis tools can be computationally intensive, especially with large datasets.

Purpose of the Study:

• To introduce TaxMan, a web-based tool designed to optimize primer selection and sequence trimming for bacterial rRNA gene amplicon sequencing.
• To provide users with an in silico assessment of primer specificity and coverage across different taxa.
• To improve the efficiency and accuracy of bacterial community analysis pipelines.

Main Methods:

• Development of a web tool (TaxMan) that trims reference rRNA gene sequences based on user-defined primer pairs.
• Implementation of in silico primer specificity assessment, including interactive plotting of amplified and missed taxa.
• Integration of trimmed sequences into downstream analysis steps like similarity searching, chimera checking, and read clustering.

Main Results:

• TaxMan effectively trims reference sequences, enabling precise primer specificity evaluation.
• The tool provides interactive visualizations for assessing primer performance across bacterial taxa.
• Utilizing trimmed sequences significantly accelerates sequence matching algorithms, reducing run times by up to 98% and lowering memory usage.

Conclusions:

• TaxMan offers a valuable solution for selecting optimal primers and regions for bacterial rRNA gene sequencing.
• The tool enhances the accuracy and efficiency of taxonomic identification in microbial community studies.
• By reducing computational demands, TaxMan facilitates broader application of amplicon sequencing in microbiology.