Evidence map›Paper›PMID 28253837›Full record

ArticleBMC bioinformatics2017

DISMS2: A flexible algorithm for direct proteome- wide distance calculation of LC-MS/MS runs.

Vera Rieder, Bernhard Blank-Landeshammer, Marleen Stuhr, Tilman Schell, Karsten Biß, Laxmikanth Kollipara, Achim Meyer, Markus Pfenninger, Hildegard Westphal, Albert Sickmann and 1 more

Abstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
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  4. MS1Connect: a mass spectrometry run similarity measure.Bioinformatics (Oxford, England) · 2023
    Article
  5. Article
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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

11 authors.

Vera RiederDepartment of Statistics, TU Dortmund University, Dortmund, Germany.
Bernhard Blank-LandeshammerLeibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
Marleen StuhrLeibniz Center for Tropical Marine Ecology (ZMT), Bremen, Germany.
Tilman SchellBiodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Frankfurt, Germany.
Karsten BißLeibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
Laxmikanth KolliparaLeibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
Achim MeyerLeibniz Center for Tropical Marine Ecology (ZMT), Bremen, Germany.
Markus PfenningerBiodiversity and Climate Research Centre, Senckenberg Gesellschaft für Naturforschung, Frankfurt, Germany.
Hildegard WestphalLeibniz Center for Tropical Marine Ecology (ZMT), Bremen, Germany.
Albert SickmannLeibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
Jörg RahnenführerDepartment of Statistics, TU Dortmund University, Dortmund, Germany. rahnenfuehrer@statistik.tu-dortmund.de.ORCID http://orcid.org/0000-0002-8947-440X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe classification of samples on a molecular level has manifold applications, from patient classification regarding cancer treatment to phylogenetics for identifying evolutionary relationships between species. Modern methods employ the alignment of DNA or amino acid sequences, mostly not genome-wide but only on selected parts of the genome. Recently proteomics-based approaches have become popular. An established method for the identification of peptides and proteins is liquid chromatography-tandem mass spectrometry (LC-MS/MS). First, protein sequences from MS/MS spectra are identified by means of database searches, given samples with known genome-wide sequence information, then sequence based methods are applied. Alternatively, de novo peptide sequencing algorithms annotate MS/MS spectra and deduce peptide/protein information without a database. A newer approach independent of additional information is to directly compare unidentified tandem mass spectra. The challenge then is to compute the distance between pairwise MS/MS runs consisting of thousands of spectra.

methodsWe present DISMS2, a new algorithm to calculate proteome-wide distances directly from MS/MS data, extending the algorithm compareMS2, an approach that also uses a spectral comparison pipeline.

resultsOur new more flexible algorithm, DISMS2, allows for the choice of the spectrum distance measure and includes different spectra preprocessing and filtering steps that can be tailored to specific situations by parameter optimization.

conclusionsDISMS2 performs well for samples from species with and without database annotation and thus has clear advantages over methods that are purely based on database search.

Indexed as

AlgorithmsAmino Acid SequenceChromatography, LiquidDatabases, ProteinHumansPeptidesProteomeProteomicsTandem Mass SpectrometryPeptidesProteomeComparison of MS/MS spectraDistance of LC-MS/MS runsLC-MS/MSMass spectrometryPeptide identificationProteomics

Identifiers

PMID28253837
PMCPMC5335755

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.