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

Overview of attention for article published in BMC Bioinformatics, March 2017
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Title
DISMS2: A flexible algorithm for direct proteome- wide distance calculation of LC-MS/MS runs
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1514-2
Pubmed ID
Authors

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

Abstract

The 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. We 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. Our 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. DISMS2 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.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
South Africa 1 4%
Unknown 27 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 29%
Student > Bachelor 4 14%
Student > Ph. D. Student 4 14%
Student > Master 3 11%
Professor > Associate Professor 2 7%
Other 4 14%
Unknown 3 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 25%
Computer Science 4 14%
Biochemistry, Genetics and Molecular Biology 4 14%
Environmental Science 3 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 3 11%
Unknown 6 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 September 2017.
All research outputs
#20,408,464
of 22,958,253 outputs
Outputs from BMC Bioinformatics
#6,882
of 7,307 outputs
Outputs of similar age
#270,688
of 310,523 outputs
Outputs of similar age from BMC Bioinformatics
#121
of 138 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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