Database construction and peptide identification strategies for proteogenomic studies on sequenced genomes

Area of Science:

• Genomics
• Proteomics
• Bioinformatics

Background:

• High-throughput DNA sequencing generates vast genomic data, necessitating accurate genome annotation.
• Gene prediction software requires experimental validation for functional gene product confirmation.

Purpose of the Study:

• To review strategies for Mass Spectrometry (MS)-based proteogenomics experiments.
• To guide the validation of genome annotations using proteomic data.

Main Methods:

• Discusses bottom-up proteogenomics approaches.
• Evaluates various genomic database construction strategies (whole genome, ORF, cDNA, EST, RNA-Seq).
• Covers statistical assessment of peptide identifications and mapping back to genomic information.

Main Results:

• Highlights the strengths and weaknesses of different database construction methods.
• Emphasizes the importance of statistical rigor in peptide identification.
• Provides references for tools used in proteogenomic data analysis.

Conclusions:

• MS-based proteomics is crucial for validating genome annotations.
• Careful database selection and statistical analysis are key for successful proteogenomic studies.