BioTranslator

BioTranslator performs semantic interpretation and analysis of genomic data using Gene Ontology and KEGG-based pathway enrichment, bootstrapping, and graph-theoretical gene prioritization to support functional genomics and systems biology.

Key Features:

• Automated Statistical Analysis: Performs statistical analysis of annotated gene-profiling experiments using controlled vocabularies such as Gene Ontology and KEGG, reordering and repartitioning terms based on enrichment scores from differentially expressed genes.
• Bootstrapping Techniques: Applies bootstrapping to correct significance estimates and mitigate biases in enrichment analyses.
• Biological Prioritization: Prioritizes terms by membership size to emphasize biologically meaningful gene sets aligned with systems biology principles.
• Graph-Theoretical Methods: Employs graph-theoretical approaches and semantic similarity metrics (edge similarity and Resnik) to explore functional interconnections among genes and identify critical gene roles, including applications to disease-specific studies such as pancreatic cancer and T-cell acute lymphoblastic leukemia.
• Automated Workflow for Pathway Analysis: Automates derivation of enriched KEGG metabolic and signaling pathways from lists of differentially expressed genes and merges them into non-redundant super-networks for downstream analysis and visualization.

Scientific Applications:

• Systems Biology: Enables pathway-level interpretation and network merging to study cellular mechanisms and system-wide interactions.
• Functional Genomics: Facilitates identification of biologically relevant terms and gene prioritization from expression data using controlled-vocabulary enrichment.
• Pancreatic Cancer Research: Supports discovery of regulatory mechanisms and key genes involved in pancreatic cancer.
• T-cell Acute Lymphoblastic Leukemia Research: Aids identification of critical regulatory genes and pathways in T-cell acute lymphoblastic leukemia.

Methodology:

Statistical analysis using controlled vocabularies (Gene Ontology, KEGG); reordering and repartitioning of terms based on enrichment scores from differentially expressed genes; bootstrapping for significance correction; prioritization by membership size; graph-theoretical analysis using edge and Resnik semantic similarity; automated merging of enriched KEGG metabolic and signaling pathways into non-redundant super-networks; integration of methodologies from StRANGER and KEGG Enriched Network Visualizer (KENeV).