13.3 Proteogenomics and multi-omics integration

Proteogenomics merges protein and genetic data, bridging the gap between genes and their functions. It's a powerful approach that combines mass spectrometry, genome annotation, and multi-omics integration to uncover hidden biological insights.

This field is revolutionizing our understanding of genomes, helping discover new genes and improve annotations. It's especially useful in cancer research, microbiology, and evolutionary studies, offering a more complete picture of complex biological systems.

Proteogenomics Fundamentals

Definition of proteogenomics

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• Proteogenomics integrates proteomics and genomics data combining protein-level information with genomic sequence data to bridge genotype-phenotype gap
• Data integration validates gene predictions and annotations, identifies novel protein-coding regions, improves understanding of gene expression and regulation
• Key components encompass genomic data (DNA sequences, gene annotations), transcriptomic data (RNA sequences, expression levels), proteomic data (protein sequences, abundance, modifications)

Methods in proteogenomics

• Mass spectrometry-based protein identification involves sample preparation (protein extraction, digestion, fractionation), LC-MS/MS analysis, database searching (matching spectra to peptide sequences), peptide-spectrum matching algorithms
• Genome annotation methods utilize ab initio gene prediction (computational algorithms), homology-based annotation (comparison to known genes), RNA-seq data integration (transcript evidence)
• Proteogenomic workflow includes:
  1. Custom protein sequence databases creation
  2. Spectral matching against customized databases
  3. Peptide-to-genome mapping
  4. Identification of novel peptides and protein variants

Applications and Importance

Applications for genome annotation

• Discovery of novel protein-coding regions uncovers unannotated genes, detects alternative splice variants, characterizes gene fusion products
• Improvement of genome annotation validates predicted genes, corrects gene boundaries, identifies translation start sites
• Field-specific applications include:
  • Cancer research detects cancer-specific protein variants (BRCA1/2 mutations)
  • Microbiology annotates bacterial and viral genomes (SARS-CoV-2)
  • Evolutionary biology studies protein evolution across species (human-chimpanzee comparisons)

Importance of multi-omics integration

• Multi-omics integration combines data from multiple omics technologies providing holistic view of biological systems
• Integration benefits improve understanding of complex biological processes, enhance ability to identify disease mechanisms, enable more accurate biomarker discovery
• Types of integrated omics data include genomics (DNA sequence and structure), transcriptomics (RNA expression and regulation), proteomics (protein abundance and modifications), metabolomics (metabolite profiles)
• Integration challenges involve data heterogeneity and normalization, computational complexity, biological interpretation of integrated results
• Tools and approaches for integration utilize statistical methods (correlation analysis, network-based approaches), machine learning algorithms (clustering, dimensionality reduction), pathway and functional enrichment analysis (KEGG, Gene Ontology)