Orthology and Paralogy Relationships at Transcript Level.

Area of Science:

• Molecular Biology
• Bioinformatics
• Evolutionary Genetics

Background:

• Eukaryotic gene alternative processing generates transcriptome diversity, producing multiple transcripts from a single gene.
• Over half of human genes are affected by alternative processing, with conserved transcripts across species.
• Understanding transcript homology is crucial for deciphering gene evolution and function.

Purpose of the Study:

• To define orthology and paralogy for transcripts of homologous genes.
• To develop an algorithm for clustering conserved orthologous and paralogous transcripts.
• To explore transcript conservation beyond strict orthologs.

Main Methods:

• Utilized gene-level homology to define transcript homology relationships.
• Employed a Reciprocal Best Hits approach for inferring isoorthologous and paralogous transcript clusters.
• Applied the algorithm to simulated and Ensembl-Compara real gene families.

Main Results:

• Successfully computed clusters of conserved orthologous and paralogous transcripts.
• Results align with previous studies comparing orthologous gene transcripts.
• Demonstrated the value of searching for conserved transcripts among homologous genes, not just orthologs.

Conclusions:

• The developed algorithm effectively clusters conserved homologous transcripts.
• Findings support the significance of alternative processing in generating transcriptome diversity.
• Investigating transcript conservation across homologous genes offers valuable evolutionary insights.