Scorecard

Scorecard analyzes gene expression changes across multiple experimental conditions by grouping genes based on fold-change and statistical significance to identify patterns of gene regulation.

Key Features:

• Gene Grouping: Groups genes based on relative fold-change and statistical significance across multiple experimental conditions.
• Pattern Detection: Identifies unique patterns of gene regulation, including genes consistently up- or down-regulated across experiments.
• Data Visualization: Generates graphs and summaries to characterize datasets and support knowledge extraction.
• Gene Tracking: Tracks genes over time or experiments to identify those meaningful for bacterial metabolism and survival.

Scientific Applications:

• Bacterial Biofilm Transcriptomics: Analyzes RNA-seq bacterial biofilm expression levels to study heterogeneity of gene changes in response to antibiotics, adjuvants, and biocompatible materials.
• Antibiotic Resistance Mechanism Identification: Detects bacterial persistence strategies including modifications in penicillin-related protein binding affinity, ribosomal subunit changes, alternative metabolic pathways, cell wall thickening, efflux pump activity, and enzymatic inactivation through hydrolyzation, phosphorylation, or adenylation.
• Multi-Condition Comparison: Facilitates information mining across independently analyzed GEO datasets to characterize gene regulation patterns.

Methodology:

The analysis workflow uses a scorecard that groups genes based on relative fold-change and statistical significance, with Python-scripted implementation for processing RNA-seq data.