Evidence map›Paper›PMID 33692656›Full record

ArticleCurrent bioinformatics2020

An Algorithm to Improve the Speed of Semi and Non-Specific Enzyme Searches in Proteomics.

Zach Rolfs, Robert J Millikin, Lloyd M Smith

Abstract read
In one paragraph

Article in Current bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Enhanced Proteomic Data Analysis with MetaMorpheus.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zach RolfsDepartment of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706.
Robert J MillikinDepartment of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706.
Lloyd M SmithDepartment of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706.

Funding

Institutional Training in the Genomic SciencesT32HG002760 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI Qiongshi Lu · 2003 to 2026
$17.7M
Revealing Proteoforms: The Primary Effectors of Biological FunctionR35GM126914 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI Lloyd M Smith · 2018 to 2026
$6.2M
NHGRI NIH HHS T32 HG002760NIGMS NIH HHS R35 GM126914
6 · The paper itself

Abstract

backgroundThe identification of non-specifically cleaved peptides in proteomics and peptidomics poses a significant computational challenge. Current strategies for the identification of such peptides are typically time consuming and hinder routine data analysis.

objectiveWe aimed to design an algorithm that would improve the speed of semi- and non-specific enzyme searches and could be applicable to existing search programs.

methodWe developed a novel search algorithm that leverages fragment-ion redundancy to simultaneously search multiple non-specifically cleaved peptides at once. Briefly, a theoretical peptide tandem mass spectrum is generated using only the fragment-ion series from a single terminus. This spectrum serves as a proxy for several shorter theoretical peptides sharing the same terminus. After database searching, amino acids are removed from the opposing terminus until the observed and theoretical precursor masses match within a given mass tolerance.

resultsThe algorithm was implemented in the search program MetaMorpheus and found to perform an order of magnitude faster than the traditional MetaMorpheus search and produce superior results.

conclusionWe report a speedy non-specific enzyme search algorithm which is open-source and enables search programs to utilize fragment-ion redundancy to achieve a notable increase in search speed.

Indexed as

AlgorithmNo-enzymeNon-specificNonspecificOpen-SourcePeptidomicsProteomicsSemi-specific

Identifiers

PMID33692656
PMCPMC7943061

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Registered trials

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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.