Species-Specific Quality Control, Assembly and Contamination Detection in Microbial Isolate Sequences with AQUAMIS

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Whole microbial genome sequencing is crucial for outbreak investigations and surveillance.
• Next-generation sequencing generates vast amounts of data, necessitating robust primary quality control.
• Accurate downstream analysis relies heavily on the quality of raw sequencing data.

Purpose of the Study:

• To present AQUAMIS, a Snakemake workflow for automated, comprehensive quality control and assembly of microbial whole-genome sequencing data.
• To enable laboratories to automatize the initial analysis of their sequencing data.
• To provide a user-friendly tool for assessing sequencing quality and detecting contaminations.

Main Methods:

• AQUAMIS automates read trimming, quality control (QC), taxonomic classification, de-novo assembly, reference identification, assembly QC, and contamination detection.
• The workflow processes data at both read and assembly levels.
• Results are presented in an interactive HTML report with species-specific QC thresholds and a JSON file for downstream analysis.

Main Results:

• AQUAMIS was successfully applied to analyze ~13,000 microbial isolates and ~1000 in-silico contaminated datasets.
• The workflow demonstrated high throughput capability in routine sequencing environments.
• A combination of QC metrics within AQUAMIS accurately detected intergenus and intragenus contaminations.

Conclusions:

• AQUAMIS provides an indispensable tool for primary quality control in microbial whole-genome sequencing.
• The workflow facilitates accurate assessment of sequencing quality and reliable contamination prediction.
• AQUAMIS supports non-bioinformaticians in evaluating sequencing experiments and enables efficient data exchange.