ArticleJournal of proteome research2015
Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics.
Article in Journal of proteome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Viral Mutation Profiling and Discovery Strategy for Sensitive Multiplex Detection of Viruses and Variants in Saliva by Proteomics.bioRxiv : the preprint server for biology · 2026Article
- Improved Peptide Search for Identification of SUMO and Sequence-Based Modifiers, in MaxSBM.Molecular & cellular proteomics : MCP · 2026Article
- Article
- An analysis of proteogenomics and how and when transcriptome-informed reduction of protein databases can enhance eukaryotic proteomics.Genome biology · 2022Article
- Bioenergetic shift and actin cytoskeleton remodelling as acute vascular adaptive mechanisms to angiotensin II in murine retina and ophthalmic artery.Redox biology · 2020Article
- Partial proteolysis improves the identification of the extracellular segments of transmembrane proteins by surface biotinylation.Scientific reports · 2020Article
- Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.Nature methods · 2019Article
- Building ProteomeTools based on a complete synthetic human proteome.Nature methods · 2017Article
- The impact of sequence database choice on metaproteomic results in gut microbiota studies.Microbiome · 2016Article
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Authors and funding
2 authors.
Funding
Abstract
In shotgun proteomics, peptides are typically identified using database searching, which involves scoring acquired tandem mass spectra against peptides derived from standard protein sequence databases such as Uniprot, Refseq, or Ensembl. In this strategy, the sensitivity of peptide identification is known to be affected by the size of the search space. Therefore, creating a targeted sequence database containing only peptides likely to be present in the analyzed sample can be a useful technique for improving the sensitivity of peptide identification. In this study, we describe how targeted peptide databases can be created based on the frequency of identification in the global proteome machine database (GPMDB), the largest publicly available repository of peptide and protein identification data. We demonstrate that targeted peptide databases can be easily integrated into existing proteome analysis workflows and describe a computational strategy for minimizing any loss of peptide identifications arising from potential search space incompleteness in the targeted search spaces. We demonstrate the performance of our workflow using several data sets of varying size and sample complexity.
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