A machine learning pipeline to classify bacterial species (Staphylococcus, Pseudomonas) from Surface-Enhanced Raman Spectroscopy (SERS) data using Python, Scikit-learn, and a Random Forest model.

ARVIA: Antibiotic Resistance Variant Identifier for Pseudomonas aeruginosa

Bioinformatics Workshop 1 for BM327 Microbiology

Comprehensive WGS variant analysis of Pseudomonas aeruginosa (SRR34663677) — includes quality control, alignment (BWA-MEM), variant calling (GATK), annotation (SnpEff), and AMR interpretation.

This GitHub Repository includes all data and analysis relevent to the paper "Bioluminescent Betrayal: Insect-pathogenic fluorescent Pseudomonas can create an ecological trap for aphids" by Zahavah Rojer et al, 202X.

Independent Research in Cell and Systems Biology I at the University of Toronto

RNA-seq expression and enrichment analysis of Pseudomonas aeruginosa (SigX mutant vs wild-type) using FastQC, fastp, STAR, featureCounts, DESeq2, and STRING to identify antibiotic stress-response pathways and key regulatory genes.

Here, we studied the conservation of carP sequence and its occurrence in diverse phylogenetic groups of bacteria. In silico analysis revealed that carP and its two paralogues PA2017 and PA0319 are primarily present in P. aeruginosa and belong to the core genome of the species.

🧬 Analyze RNA-seq data of *Pseudomonas aeruginosa* SigX mutant vs. wild-type to uncover antibiotic stress-response pathways and transcriptional changes.