PGMicroD

PGMicroD detects pathogenic microbes from 16S rRNA next-generation sequencing (NGS) data and estimates species composition and abundance in complex biological samples.

Key Features:

• High-Resolution Microbial Detection: Uses NGS-derived 16S rRNA data to resolve composition and abundance of pathogenic microbes in complex samples.
• Advanced Filtering and Feature Extraction: Filters potentially erroneous reads and extracts multiple species-related features from 16S rRNA sequencing data to improve identification accuracy amid noise.
• Machine Learning Integration: Incorporates a Support Vector Machine (SVM) classifier trained on extracted features to predict microbial composition and assign reads to specific microbes.
• Abundance Estimation: Groups multiple-mapped sequencing reads by predicted species references to estimate each species' abundance.

Scientific Applications:

• Microbiome Studies: Profiles microbial community composition and relative abundances from 16S rRNA NGS datasets.
• Infectious Disease Diagnostics: Detects and quantifies pathogenic microbes in clinical samples using 16S rRNA sequencing and SVM-based classification.
• Environmental Microbiology: Identifies and estimates abundances of pathogenic or community members in environmental 16S rRNA NGS samples.

Methodology:

Initial read filtering; feature extraction from 16S rRNA data; classification using a Support Vector Machine (SVM) on the extracted features; grouping of multiple-mapped sequencing reads by predicted species references for abundance estimation.