Using Proteomics to Improve Genome Annotation in Aspergillus niger
Author Information
Author(s): Wright James C, Sugden Deana, Francis-McIntyre Sue, Riba-Garcia Isabel, Gaskell Simon J, Grigoriev Igor V, Baker Scott E, Beynon Robert J, Hubbard Simon J
Primary Institution: University of Liverpool
Hypothesis
Can proteomic data enhance the accuracy of genome annotation and gene model validation in Aspergillus niger?
Conclusion
Integrating experimental proteomics data into genomic annotation pipelines can significantly improve the accuracy of gene models.
Supporting Evidence
- 405 identified peptide sequences were mapped to 214 different A.niger genomic loci.
- 13 loci had no preferred predicted gene model or the chosen model was not the best match to identified peptides.
- Peptides identified boosted confidence in predicted gene structures spanning 54 introns.
Takeaway
Scientists used protein data to check and improve the accuracy of gene predictions in a fungus called Aspergillus niger, helping to make better maps of its genes.
Methodology
The study involved collecting tandem mass spectrometry data from Aspergillus niger samples and mapping identified peptides to genomic loci.
Potential Biases
Potential for false positives in peptide identifications, particularly with single peptide hits.
Limitations
The study only provides limited coverage of the entire A. niger proteome and was based on a single experimental condition.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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