Hortense: Horizontal gene transfer detection directly from proteomic MS/MS data
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Public Health, Computational Science
- Keywords
- Bacteria, Bioinformatics, Microbiology, Tandem Mass Spectrometry, Bacterial Proteomics, Horizontal Gene Transfer, Protein Identification
- Copyright
- © 2017 Trappe et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Hortense: Horizontal gene transfer detection directly from proteomic MS/MS data. PeerJ Preprints 5:e3248v1 https://doi.org/10.7287/peerj.preprints.3248v1
Abstract
Horizontal gene transfer (HGT) is a powerful mechanism that allows bacteria to directly transfer long stretches of genomic sequence from one individual to another. The transfer of antimicrobial resistance genes is a prominent example of HGT events in the context of multi-resistant bacteria which pose a high risk to human health. While several approaches for HGT detection exist on the genomic level, to the best of our knowledge, HGT events have not been investigated in a detailed mass spectrometry (MS)-based proteomic study. However, the mere presence of a gene does not necessarily correlate with its expression at the protein level. Consequently, to draw conclusions with respect to the expression of HGT-mediated genes, MS-based proteomics can be employed. We developed a first computational approach - called Hortense - for automated HGT detection directly from shotgun proteomics experiments. We extend the standard database search by a critical cross-validation to unravel potential HGT proteins. A proteogenomic extension gives information about the genomic origin and enables an integration with existing genome-based methods. We successfully validated our approach on simulated data, and further evaluated it on real data from a transgenic organism and a negative control from an organism not harboring a transferred gene. Our results indicate that our method facilitates MS-based analysis for proteomic evidence of HGT events. Especially as an orthogonal approach to genome-based HGT detection methods, our proposed workflow is a first step toward a systematic and large scale analysis of HGT events in, e.g., antimicrobial resistance context. Hortense is publicly available at https://gitlab.com/rki_bioinformatics/.
Author Comment
This is an article which has been accepted for the GCB 2017 Conference.