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Unlock your guides1.3 Relationship between genomics, transcriptomics, and proteomics
2 min read•july 25, 2024
Molecular biology's central dogma oversimplifies . The real process involves complex , , and . Genomics and transcriptomics have limitations in predicting protein behavior and interactions.
Integrating proteomics with other omics approaches provides a holistic view of cellular processes. This enhances , , and , leading to improved understanding of biological systems and applications in personalized medicine.
Molecular Biology and Omics Integration
Central dogma vs protein synthesis
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- Central dogma DNA → RNA → Protein describes one-way flow of genetic information oversimplifies process
- Actual flow involves DNA to mRNA, processing (splicing, capping, polyadenylation), to proteins,
- Real-world process includes regulatory mechanisms, feedback loops, epigenetic factors influencing (, )
Limitations of genomics and transcriptomics
- Genomics fails to account for , predict events, capture post-translational modifications (, )
- Transcriptomics struggles with , ,
- Both unable to detect (nucleus, cytoplasm), predict protein activity or functional state, capture over time (, )
Integrating Omics Approaches
Integration of proteomics data
- Multi-omics data integration combines genomic, transcriptomic, proteomic datasets provides holistic view of cellular processes (metabolism, signaling pathways)
- Complementary information genomics reveals genetic variations, transcriptomics shows gene expression patterns, proteomics measures actual protein abundance
- Enhanced pathway analysis identifies discrepancies between mRNA and protein levels reveals post-transcriptional regulation mechanisms (miRNA regulation, protein degradation)
- Improved biomarker discovery combines genetic predisposition with protein expression increases accuracy in disease diagnosis and prognosis (cancer, neurodegenerative disorders)
Proteogenomics for genome annotation
- integrates proteomics data with genomic and transcriptomic information improves genome annotation
- Validates predicted protein-coding genes, identifies , corrects (, )
- Confirms expression of , reveals alternative splicing events at protein level, identifies and their functions
- Applications in personalized medicine detects , improves interpretation of
- Challenges include need for advanced , and analysis pipelines
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