Integrating gene annotation with orthology inference at scale

Area of Science:

• Genomics
• Evolutionary Biology
• Bioinformatics

Background:

• Gene annotation and ortholog inference are critical genomic tasks.
• Current methods are often separate, limiting scalability and accuracy.
• Highly fragmented assemblies pose significant challenges.

Purpose of the Study:

• To present TOGA (Tool to infer Orthologs from Genome Alignments), a novel method integrating gene annotation and orthology inference.
• To improve the accuracy and scalability of ortholog detection and gene annotation.
• To create the largest comparative gene resources for mammals and birds.

Main Methods:

• TOGA integrates structural gene annotation with orthology inference.
• The method employs a new paradigm for inferring orthologous loci.
• It is applied to large-scale genomic datasets, including fragmented assemblies.

Main Results:

• TOGA outperforms state-of-the-art methods in ortholog detection and gene annotation.
• The tool successfully processed 488 placental mammal and 501 bird assemblies.
• It enabled the creation of the largest comparative gene resources to date.
• TOGA accurately detects gene losses and assesses mammalian genome quality.

Conclusions:

• TOGA is a powerful and scalable solution for gene annotation and comparative genomics.
• The method enhances the analysis of conserved genes across numerous genomes.
• It provides a valuable resource for evolutionary biology and genomic research.