1. Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal
- Author
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Sebastian A. Leidel, Manuel Kösters, Mechthild Pohlschroder, Aime Bienfait Igiraneza, Christian Fufezan, Johannes Leufken, Benjamin A. Garcia, and Stefan Schulze
- Subjects
Proteomics ,0301 basic medicine ,Computer science ,Computational biology ,Biochemistry ,Article ,03 medical and health sciences ,Search engine ,540 Chemistry ,Databases, Protein ,computer.programming_language ,030102 biochemistry & molecular biology ,Protein database ,General Chemistry ,Python (programming language) ,Combined approach ,Search Engine ,Identification (information) ,030104 developmental biology ,Posttranslational modification ,570 Life sciences ,biology ,Protein Processing, Post-Translational ,computer ,Algorithms ,Software - Abstract
The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8–18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.
- Published
- 2021
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