High throughput discovery of protein variants using proteomics informed by transcriptomics.

Area of Science:

• Genomics
• Proteomics
• Bioinformatics

Background:

• Proteomics informed by transcriptomics (PIT) can identify novel translated genomic elements (TGEs).
• Distinguishing novel TGEs, variants, and assembly artifacts in PIT data is challenging.
• Existing methods often rely on known variant databases, limiting applicability.

Purpose of the Study:

• To develop an automated computational solution for classifying putative TGEs identified through PIT.
• To enable large-scale identification of sequence polymorphisms, splice isoforms, and novel TGEs.
• To create a method applicable to non-model organisms without requiring prior variant knowledge.

Main Methods:

• Developed an automated classification pipeline for putative TGEs by comparing against reference proteomes.
• Utilized proteomic MS/MS spectra searched against de novo assembled transcript open reading frames.
• Validated the method on human data and applied it to Mus musculus, Pteropus alecto, and Aedes aegypti.

Main Results:

• Successfully classified putative TGEs, enabling identification of sequence polymorphisms and splice isoforms.
• Discovered 60 human protein isoforms and 32,392 polymorphisms in Pteropus alecto.
• Identified TGEs with non-methionine start sites, including tyrosine, across different species.

Conclusions:

• The automated PIT classification method accurately distinguishes novel TGEs, variants, and artifacts.
• This approach significantly enhances the discovery of genetic variations and novel protein-coding elements, especially in non-model organisms.
• The findings expand the understanding of proteomic diversity and translation initiation.