FunGAP

FunGAP evaluates and assembles evidence-based gene models from fungal genome assemblies by integrating multiple gene predictors and protein/domain homology to improve gene prediction accuracy.

Key Features:

• Multiple Gene Predictors Integration: Runs multiple gene prediction algorithms on fungal genome assemblies using an ensemble approach to increase coverage and robustness of predicted gene models.
• Evidence-Based Evaluation: Applies a scoring function that estimates congruency of predicted genes to known proteins and domains to evaluate gene model support.
• High-Quality Gene Model Assembly: Selects and assembles gene models supported by homology to known sequences to produce higher-confidence annotations.
• Standardized Evaluation Framework: Provides a systematic framework for evaluating and comparing gene models across fungal genome annotations.
• Python-Based Implementation: Implemented in Python.

Scientific Applications:

• Functional Genomics: Generates accurate gene models to support studies of gene function and regulation in fungi.
• Comparative Genomics: Produces consistent gene predictions to enable comparative analyses across fungal species.
• Pathogen Research: Facilitates annotation of pathogenic fungal genomes to aid identification of virulence factors and candidate therapeutic targets.

Methodology:

Runs multiple gene prediction algorithms on fungal genome assemblies, scores predicted genes using a congruency-based scoring function against known proteins and domains, and assembles gene models supported by that evidence.