Improving Genome Annotation of Salmonella Typhimurium Using Proteomics
Author Information
Author(s): Charles Ansong, Nikola Tolic, Samuel O. Purvine, Steffen Porwollik, Marcus Jones, Hyunjin Yoon, Samuel H. Payne, Jessica L. Martin, Meagan C. Burnet, Matthew E. Monroe, Pratap Venepally, Richard D. Smith, Scott N. Peterson, Fred Heffron, Michael McClelland, Joshua N. Adkins
Primary Institution: Pacific Northwest National Laboratory
Hypothesis
Can proteomics improve the accuracy of genome annotations for Salmonella Typhimurium?
Conclusion
The study demonstrates that proteomics can significantly enhance genome annotations by correcting start sites and identifying novel genes.
Supporting Evidence
- Proteomics data validated approximately half of the predicted protein-coding genes in Salmonella.
- Identified 12 novel genes that were missed by gene prediction programs.
- Corrected start sites for 47 genes based on experimental evidence.
- Characterized over 130 signal peptide and N-terminal methionine cleavage events.
- Revealed a larger repertoire of post-translational modifications than previously thought.
Takeaway
Scientists used a special technique to look at proteins in Salmonella Typhimurium, which helped them find mistakes in the genetic instructions and discover new genes.
Methodology
The study employed bottom-up proteomics and high-resolution mass spectrometry to validate gene annotations and identify post-translational modifications.
Potential Biases
The reliance on computational predictions may introduce biases in gene annotation.
Limitations
The study may not cover all growth conditions for Salmonella Typhimurium, potentially missing some proteins.
Statistical Information
P-Value
< 0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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