Peptimapper: proteogenomics workflow for the expert annotation of eukaryotic genomes

Area of Science:

• Genomics
• Proteomics
• Bioinformatics

Background:

• Accurate genome structural annotation remains a significant challenge, particularly for eukaryotic species.
• Gene structure prediction is often erroneous and incomplete, hindering biological research.
• Proteogenomics offers a powerful strategy to overcome these limitations by integrating proteomics and bioinformatics.

Purpose of the Study:

• To develop and apply a proteogenomics workflow (Peptimapper) for accurate genome annotation.
• To identify novel protein-coding genes and splice isoforms.
• To validate predicted exons and genes, and correct gene structures.

Main Methods:

• Applied the Peptimapper workflow to Ectocarpus sp. genome annotation.
• Generated shotgun proteomics data across various life cycle stages and subcellular fractions.
• Mapped peptide sequence tags (PSTs) to the genomic sequence, clustered hits, and compared with existing gene predictions.

Main Results:

• Identified two potential novel genes: ribosomal protein L22 and aryl sulfotransferase.
• Corrected the gene structure of dihydrolipoamide acetyltransferase.
• Experimentally validated findings using RT-PCR and transcriptomics data.

Conclusions:

• Peptimapper serves as a valuable complementary tool for expert genome annotation.
• The workflow is adaptable to various organisms and accessible via Docker images and the Galaxy framework.
• Proteogenomics significantly enhances the accuracy and completeness of genome structural annotation.