ArticleMolecular & cellular proteomics : MCP2014
JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy.
Article in Molecular & cellular proteomics : MCP, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers, 1 of them a synthesis that pooled it.
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133 citing papers in PubMed, 1 synthesis or guideline pooled it, 190 citations in OpenAlex.
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73 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from "mixture spectra" to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.