Related Experiment Video
Updated: Jun 25, 2026
11:30
Quantitative Analysis of Aspergillus nidulans Growth Rate using Live Microscopy and Open-Source Software
Published on: July 24, 2021
Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger.
James C Wright1, Deana Sugden, Sue Francis-McIntyre
1Dept Veterinary Preclinical Sciences, University of Liverpool, Liverpool, UK. james.wright@manchester.ac.uk
BMC Genomics
|February 6, 2009
Summary
Proteomics data from Aspergillus niger improves genome annotation by identifying gene models and validating gene structures. This integration enhances accuracy and reveals discrepancies in existing gene predictions.
Area of Science:
- Genomics
- Proteomics
- Bioinformatics
Background:
- Proteomic data offers a valuable, yet underutilized, resource for fungal genome annotation.
- Peptide identifications from tandem mass spectrometry (MS/MS) can validate gene predictions and distinguish between candidate gene models.
Purpose of the Study:
- To apply proteomic data for the annotation of the Aspergillus niger genome.
- To evaluate the utility of peptide identifications in refining gene models and identifying discrepancies.
Main Methods:
- Acquisition of tandem mass spectra (MS/MS) from 1D gel electrophoresis bands.
- Searching MS/MS data against all available gene models using Average Peptide Scoring (APS) and reverse database searching.
- Ensuring confident identifications at an acceptable false discovery rate (FDR).
Main Results:
- 405 identified peptides mapped to 214 genomic loci, implicating 2872 of 4093 predicted gene models.
- Discrepancies were found in 6% of loci, where identified peptides did not match the preferred or "best" predicted gene model.
- Proteomic evidence supported the prediction of gene structures, including 54 introns.
Conclusions:
- Integrating experimental proteomics data into genomic annotation pipelines is highly beneficial, similar to the impact of expressed sequence tag (EST) data.
- Comparison with another A. niger genome strain revealed differences in gene models supported by proteomics, underscoring the method's utility.
