ArticleNature methods2012
False discovery rate estimation for cross-linked peptides identified by mass spectrometry.
Article in Nature methods, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 185 papers.
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Who cites it
185 citing papers in PubMed.
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- Error-controlled non-additive interaction discovery in machine learning models.Nature machine intelligence · 2025Article
- Kinetic principles of chemical cross-link formation for protein-protein interactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- An inhibitory segment within G-patch activators tunes Prp43-ATPase activity during ribosome assembly.Nature communications · 2024Article
- Rescuing error control in crosslinking mass spectrometry.Molecular systems biology · 2024Article
- Stoichiometry and architecture of the human pyruvate dehydrogenase complex.Science advances · 2024Article
- An algorithm for decoy-free false discovery rate estimation in XL-MS/MS proteomics.Bioinformatics (Oxford, England) · 2024Article
125 more citing papers are in PubMed but not listed here.
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8 authors.
Funding
Abstract
The mass spectrometric identification of chemically cross-linked peptides (CXMS) specifies spatial restraints of protein complexes; these values complement data obtained from common structure-determination techniques. Generic methods for determining false discovery rates of cross-linked peptide assignments are currently lacking, thus making data sets from CXMS studies inherently incomparable. Here we describe an automated target-decoy strategy and the software tool xProphet, which solve this problem for large multicomponent protein complexes.
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