The paralog-to-contig assignment problem: high quality gene models from fragmented assemblies.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Accurate gene annotation is crucial for understanding newly sequenced genomes.
• Existing comparative pipelines struggle with gene models, particularly for similar paralogs in draft assemblies.
• High-quality protein-coding gene models are essential for phylogenetics and gene family history analysis.

Purpose of the Study:

• To present a computational pipeline, ExonMatchSolver, for generating and curating high-quality protein-coding gene models.
• To address the challenge of assigning coding exon groups to specific paralogous genes within fragmented genome assemblies.

Main Methods:

• ExonMatchSolver pipeline development.
• Formulation and solution of the paralog-to-contig assignment problem as an Integer Linear Programming (ILP) problem.
• Demonstration of the NP-complete nature of the paralog-to-contig assignment.

Main Results:

• ExonMatchSolver effectively identifies coding exon groups belonging to the same paralogous genes in fragmented genomes.
• The pipeline successfully generates accurate protein-coding gene models, even across multiple genomic fragments.
• The ILP approach provides a robust solution to the complex paralog-to-contig assignment.

Conclusions:

• ExonMatchSolver enables the construction of highly accurate protein-coding gene models from fragmented genomic data.
• This facilitates improved understanding of evolutionary histories in gene families with numerous paralogs and frequent duplications.
• The pipeline supports advanced phylogenetic and gene family analyses.