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dc.contributor.authorWeisser, H
dc.contributor.authorChoudhary, JS
dc.date.accessioned2020-10-15T15:31:25Z
dc.date.issued2017-08
dc.identifier.citationJournal of proteome research, 2017, 16 (8), pp. 2964 - 2974
dc.identifier.issn1535-3893
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4162
dc.identifier.eissn1535-3907
dc.identifier.doi10.1021/acs.jproteome.7b00248
dc.description.abstractLabel-free quantification of shotgun LC-MS/MS data is the prevailing approach in quantitative proteomics but remains computationally nontrivial. The central data analysis step is the detection of peptide-specific signal patterns, called features. Peptide quantification is facilitated by associating signal intensities in features with peptide sequences derived from MS2 spectra; however, missing values due to imperfect feature detection are a common problem. A feature detection approach that directly targets identified peptides (minimizing missing values) but also offers robustness against false-positive features (by assigning meaningful confidence scores) would thus be highly desirable. We developed a new feature detection algorithm within the OpenMS software framework, leveraging ideas and algorithms from the OpenSWATH toolset for DIA/SRM data analysis. Our software, FeatureFinderIdentification ("FFId"), implements a targeted approach to feature detection based on information from identified peptides. This information is encoded in an MS1 assay library, based on which ion chromatogram extraction and detection of feature candidates are carried out. Significantly, when analyzing data from experiments comprising multiple samples, our approach distinguishes between "internal" and "external" (inferred) peptide identifications (IDs) for each sample. On the basis of internal IDs, two sets of positive (true) and negative (decoy) feature candidates are defined. A support vector machine (SVM) classifier is then trained to discriminate between the sets and is subsequently applied to the "uncertain" feature candidates from external IDs, facilitating selection and confidence scoring of the best feature candidate for each peptide. This approach also enables our algorithm to estimate the false discovery rate (FDR) of the feature selection step. We validated FFId based on a public benchmark data set, comprising a yeast cell lysate spiked with protein standards that provide a known ground-truth. The algorithm reached almost complete (>99%) quantification coverage for the full set of peptides identified at 1% FDR (PSM level). Compared with other software solutions for label-free quantification, this is an outstanding result, which was achieved at competitive quantification accuracy and reproducibility across replicates. The FDR for the feature selection was estimated at a low 1.5% on average per sample (3% for features inferred from external peptide IDs). The FFId software is open-source and freely available as part of OpenMS ( www.openms.org ).
dc.formatPrint-Electronic
dc.format.extent2964 - 2974
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectPeptides
dc.subjectFungal Proteins
dc.subjectFalse Positive Reactions
dc.subjectChromatography, Liquid
dc.subjectReproducibility of Results
dc.subjectProteomics
dc.subjectAlgorithms
dc.subjectTandem Mass Spectrometry
dc.subjectStatistics as Topic
dc.subjectSupport Vector Machine
dc.titleTargeted Feature Detection for Data-Dependent Shotgun Proteomics.
dc.typeJournal Article
dcterms.dateAccepted2017-07-04
rioxxterms.versionofrecord10.1021/acs.jproteome.7b00248
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2017-08
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfJournal of proteome research
pubs.issue8
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR
pubs.publication-statusPublished
pubs.volume16
pubs.embargo.termsNot known
dc.contributor.icrauthorChoudhary, Jyotien


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