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Open Access Research article

High accuracy mass spectrometry analysis as a tool to verify and improve gene annotation using Mycobacterium tuberculosis as an example

Gustavo A de Souza1, Hiwa Målen1, Tina Søfteland1, Gisle Sælensminde23, Swati Prasad2, Inge Jonassen23 and Harald G Wiker14*

Author Affiliations

1 Section for Microbiology and Immunology, The Gade Institute, University of Bergen, Bergen, Norway

2 Department of Informatics, High Technology Center, University of Bergen, Bergen, Norway

3 Computational Biology Unit, BCCS, High Technology University of Bergen, Bergen, Norway

4 Department of Microbiology and Immunology, Haukeland University Hospital, Bergen, Norway

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BMC Genomics 2008, 9:316  doi:10.1186/1471-2164-9-316

Published: 2 July 2008

Abstract

Background

While the genomic annotations of diverse lineages of the Mycobacterium tuberculosis complex are available, divergences between gene prediction methods are still a challenge for unbiased protein dataset generation. M. tuberculosis gene annotation is an example, where the most used datasets from two independent institutions (Sanger Institute and Institute of Genomic Research-TIGR) differ up to 12% in the number of annotated open reading frames, and 46% of the genes contained in both annotations have different start codons. Such differences emphasize the importance of the identification of the sequence of protein products to validate each gene annotation including its sequence coding area.

Results

With this objective, we submitted a culture filtrate sample from M. tuberculosis to a high-accuracy LTQ-Orbitrap mass spectrometer analysis and applied refined N-terminal prediction to perform comparison of two gene annotations. From a total of 449 proteins identified from the MS data, we validated 35 tryptic peptides that were specific to one of the two datasets, representing 24 different proteins. From those, 5 proteins were only annotated in the Sanger database. In the remaining proteins, the observed differences were due to differences in annotation of transcriptional start sites.

Conclusion

Our results indicate that, even in a less complex sample likely to represent only 10% of the bacterial proteome, we were still able to detect major differences between different gene annotation approaches. This gives hope that high-throughput proteomics techniques can be used to improve and validate gene annotations, and in particular for verification of high-throughput, automatic gene annotations.