Improving the reliability and throughput of mass spectrometry‐based proteomics by spectrum quality filtering
Identifieur interne : 000F66 ( Main/Exploration ); précédent : 000F65; suivant : 000F67Improving the reliability and throughput of mass spectrometry‐based proteomics by spectrum quality filtering
Auteurs : Kristian Flikka [Norvège] ; Lennart Martens [Belgique] ; Joël Vandekerckhove [Belgique] ; Kris Gevaert [Belgique] ; Ingvar Eidhammer [Norvège]Source :
- PROTEOMICS [ 1615-9853 ] ; 2006-04.
English descriptors
- Teeft :
- Adtree, Adtree method, Aggregating estimators, Algorithm, Aode, Aode algorithm, Aode classifier, Attribute, Bayesian, Bayesian classifiers, Bergen, Bergen center, Bioinformatics proteomics, Class probabilities, Classifier, Cofradic, Cofradic proteome, Computational biology unit, Computational science, Database, Dataset, Datasets, Decision tree classifiers, Decision trees, Different mass spectrometers, Execution times, Extra dimension, False identifications, False negatives, Feature vector, Fragment peaks, Gmbh, Good ones, Good spectra, Human blood platelets, Identification efficiency, Identification process, Independence assumption, Information gain, International conference, Kgaa, Lazy bayesian rules, Mascot, Mass difference, Mass spectrometer, Methionyl peptides, Modification, Morgan kaufmann, Other methods, Overoptimistic results, Peak lists, Peptide, Peptide fragmentation spectra, Precursor, Precursor mass, Pride accession number, Probability estimates, Program spequal, Project number, Protein identification, Proteome, Proteome studies, Proteomics, Relative intensity, Relative peak intensities, Same peptide, Scientific research flanders, Sequence tags, Significant number, Significant peaks, Similar spectra, Spectrometer, Spectrum, Spequal procedure, Testing times, Total intensity, Tryptic proteome, Unexpected modifications, Unidentifiable spectra, Unidentified, Unidentified spectra, Unique peptide sequences, Various methods, Verlag, Verlag gmbh, Weinheim, Yates.
Abstract
In contemporary peptide‐centric or non‐gel proteome studies, vast amounts of peptide fragmentation data are generated of which only a small part leads to peptide or protein identification. This motivates the development and use of a filtering algorithm that removes spectra that contribute little to protein identification. Removal of unidentifiable spectra reduced both the amount of computational and human time spent on analyzing spectra as well as the chances of obtaining false identifications. Thorough testing on various proteome datasets from different instruments showed that the best suggested machine‐learning classifier is, on average, able to recognize half of the unidentified spectra as bad spectra. Further analyses showed that several unidentified spectra classified as good were derived from peptides carrying unanticipated amino acid modifications or contained sequence tags that allowed peptide identification using homology searches. The implementation of the classifiers is available under the GNU General Public License at http://www.bioinfo.no/software/spectrumquality.
Url:
DOI: 10.1002/pmic.200500309
Affiliations:
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negatives</term><term>Feature vector</term><term>Fragment peaks</term><term>Gmbh</term><term>Good ones</term><term>Good spectra</term><term>Human blood platelets</term><term>Identification efficiency</term><term>Identification process</term><term>Independence assumption</term><term>Information gain</term><term>International conference</term><term>Kgaa</term><term>Lazy bayesian rules</term><term>Mascot</term><term>Mass difference</term><term>Mass spectrometer</term><term>Methionyl peptides</term><term>Modification</term><term>Morgan kaufmann</term><term>Other methods</term><term>Overoptimistic results</term><term>Peak lists</term><term>Peptide</term><term>Peptide fragmentation spectra</term><term>Precursor</term><term>Precursor mass</term><term>Pride accession number</term><term>Probability estimates</term><term>Program spequal</term><term>Project number</term><term>Protein identification</term><term>Proteome</term><term>Proteome studies</term><term>Proteomics</term><term>Relative intensity</term><term>Relative peak intensities</term><term>Same peptide</term><term>Scientific research flanders</term><term>Sequence tags</term><term>Significant number</term><term>Significant peaks</term><term>Similar spectra</term><term>Spectrometer</term><term>Spectrum</term><term>Spequal procedure</term><term>Testing times</term><term>Total intensity</term><term>Tryptic proteome</term><term>Unexpected modifications</term><term>Unidentifiable spectra</term><term>Unidentified</term><term>Unidentified spectra</term><term>Unique peptide sequences</term><term>Various methods</term><term>Verlag</term><term>Verlag gmbh</term><term>Weinheim</term><term>Yates</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In contemporary peptide‐centric or non‐gel proteome studies, vast amounts of peptide fragmentation data are generated of which only a small part leads to peptide or protein identification. This motivates the development and use of a filtering algorithm that removes spectra that contribute little to protein identification. Removal of unidentifiable spectra reduced both the amount of computational and human time spent on analyzing spectra as well as the chances of obtaining false identifications. Thorough testing on various proteome datasets from different instruments showed that the best suggested machine‐learning classifier is, on average, able to recognize half of the unidentified spectra as bad spectra. Further analyses showed that several unidentified spectra classified as good were derived from peptides carrying unanticipated amino acid modifications or contained sequence tags that allowed peptide identification using homology searches. 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