Evidence map›Paper›PMID 26335531›Full record

ArticleBMC bioinformatics2015

Scientific workflow optimization for improved peptide and protein identification.

Sonja Holl, Yassene Mohammed, Olav Zimmermann, Magnus Palmblad

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

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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

4 authors at 2 institutions in 2 countries.

Sonja HollJülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52425, Jülich, Germany. s.holl@fz-juelich.de.
Yassene MohammedCenter for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300, RC, Leiden, The Netherlands. y.mohammed@lumc.nl.
Olav ZimmermannJülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52425, Jülich, Germany. olav.zimmermann@fz-juelich.de.
Magnus PalmbladCenter for Proteomics and Metabolomics, Leiden University Medical Center, PO Box 9600, 2300, RC, Leiden, The Netherlands. n.m.palmblad@lumc.nl.
Forschungszentrum Jülich · DENetherlands Metabolomics Centre · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeptide-spectrum matching is a common step in most data processing workflows for mass spectrometry-based proteomics. Many algorithms and software packages, both free and commercial, have been developed to address this task. However, these algorithms typically require the user to select instrument- and sample-dependent parameters, such as mass measurement error tolerances and number of missed enzymatic cleavages. In order to select the best algorithm and parameter set for a particular dataset, in-depth knowledge about the data as well as the algorithms themselves is needed. Most researchers therefore tend to use default parameters, which are not necessarily optimal.

resultsWe have applied a new optimization framework for the Taverna scientific workflow management system (http://ms-utils.org/Taverna_Optimization.pdf) to find the best combination of parameters for a given scientific workflow to perform peptide-spectrum matching. The optimizations themselves are non-trivial, as demonstrated by several phenomena that can be observed when allowing for larger mass measurement errors in sequence database searches. On-the-fly parameter optimization embedded in scientific workflow management systems enables experts and non-experts alike to extract the maximum amount of information from the data. The same workflows could be used for exploring the parameter space and compare algorithms, not only for peptide-spectrum matching, but also for other tasks, such as retention time prediction.

conclusionUsing the optimization framework, we were able to learn about how the data was acquired as well as the explored algorithms. We observed a phenomenon identifying many ammonia-loss b-ion spectra as peptides with N-terminal pyroglutamate and a large precursor mass measurement error. These insights could only be gained with the extension of the common range for the mass measurement error tolerance parameters explored by the optimization framework.

Indexed as

AlgorithmsSoftwareWorkflowComputational BiologyDatabases, ProteinHumansMass SpectrometryModels, StatisticalPeptide FragmentsProgramming LanguagesProteinsProteomicsUser-Computer InterfacePeptide FragmentsProteins

Identifiers

PMID26335531
PMCPMC4558836
OpenAlexW2097126585

What OpenQuestion holds

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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.