gFACs: Gene Filtering, Analysis, and Conversion to Unify Genome Annotations Across Alignment and Gene Prediction...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Published genome annotations frequently contain errors in gene models, impacting downstream analyses.
• Inconsistent file formats and lack of robust filtering in gene prediction frameworks contribute to annotation inaccuracies.
• Existing frameworks often neglect functional attributes like protein domains for gene model validation.

Purpose of the Study:

• To introduce a software package, Gene Filtering, Analysis, and Conversion (gFACs), for improving the quality of genome annotations.
• To provide tools for filtering, analyzing, and converting predicted gene models and alignments.
• To enable the assessment of gene model reliability using structural and functional attributes.

Main Methods:

• gFACs processes various alignment, analysis, and gene prediction files.
• The software employs a flexible framework for defining gene models with structural and functional attributes.
• It supports common downstream applications, including genome browsers, and generates detailed filtering reports.

Main Results:

• gFACs effectively filters and analyzes predicted gene models and alignments.
• The software ensures gene annotations are consistent with current file standards.
• It facilitates the validation of gene models using functional attributes and provides visualizations of the gene space.

Conclusions:

• gFACs offers a robust solution for identifying and correcting erroneous gene models in published genomes.
• The software enhances the reliability and usability of genome annotations for various applications.
• gFACs promotes higher quality genome data through standardized filtering and analysis.