Lost and Found: Re-searching and Re-scoring Proteomics Data Aids Genome Annotation and Improves Proteome Coverage

Area of Science:

• Genomics
• Proteomics
• Bioinformatics

Background:

• Automated prokaryotic genome annotation pipelines often propagate errors and underestimate genome complexity.
• Accurate identification of all protein-coding genes (open reading frames) remains a challenge in prokaryotic genome annotation.

Purpose of the Study:

• To develop and optimize a proteogenomic workflow for improved prokaryotic genome annotation.
• To identify unannotated proteins and alternative protein forms in prokaryotes using ribosome profiling and proteomics data.

Main Methods:

• Utilized ribosome profiling (ribo-seq) and proteomic data analysis for *Salmonella enterica* serovar Typhimurium.
• Employed advanced data analysis including searching cofragmenting peptides and extended peptide-to-spectrum quality features.
• Reanalyzed public *Deinococcus radiodurans* datasets to demonstrate pipeline applicability.

Main Results:

• Achieved enhanced proteome depth and increased confidence in identifying unannotated peptide hits.
• Successfully identified unannotated proteins and alternative protein forms.
• Demonstrated the general applicability and effectiveness of the optimized workflow on different prokaryotic species.

Conclusions:

• Systematic reanalysis of omics data using the described proteogenomic workflow significantly improves genome annotation accuracy.
• This approach offers a promising strategy for experimentally based genome annotation, aiding in the annotation of numerous sequenced prokaryotic genomes.
• The workflow enhances proteome coverage and sensitive detection of unannotated open reading frames, crucial for bacterial genome reannotation.