MOSMAP: Mosquito metagenome analysis pipeline.

Area of Science:

• Bioinformatics
• Metagenomics
• Vector Biology

Background:

• Metagenomic analysis of mosquito populations is crucial for understanding disease transmission.
• Existing bioinformatics workflows can be complex and require specialized expertise.
• There is a need for accessible tools to streamline mosquito metagenome analysis.

Purpose of the Study:

• To develop and validate MosMAP, a user-friendly bioinformatics pipeline for mosquito metagenome analysis.
• To automate key processes including quality control, taxonomic classification, and abundance estimation.
• To make complex metagenomic analyses accessible to researchers with limited computational backgrounds.

Main Methods:

• MosMAP integrates established bioinformatics tools: Trimgalore for quality control, Kraken 2 and Bracken for taxonomic classification and abundance estimation, and Krona for visualization.
• The pipeline automates the workflow from raw sequencing data to interpretable results.
• Standard bioinformatics workflows were used as benchmarks for comparison.

Main Results:

• MosMAP demonstrated high concordance with standard bioinformatics workflows (Kraken, Bracken) in read retention, taxonomic accuracy, and abundance estimation.
• The pipeline successfully processed metagenomes from mosquito samples collected in Bhopal, India.
• Automated processes ensure efficient and reproducible results.

Conclusions:

• MosMAP provides a simplified and accessible approach to mosquito metagenome analysis.
• The pipeline supports research in microbiology, ecology, and vector-borne diseases by facilitating data interpretation.
• MosMAP empowers researchers with varying computational expertise to conduct advanced metagenomic studies.