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Mass Fingerprinting of Complex Mixtures: Protein Inference from High-Resolution Peptide Masses and Predicted Retention Times
KTH, Centres, Science for Life Laboratory, SciLifeLab.
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2013 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 12, no 12, 5730-5741 p.Article in journal (Refereed) Published
Abstract [en]

In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.

Place, publisher, year, edition, pages
2013. Vol. 12, no 12, 5730-5741 p.
Keyword [en]
bioinformatics, mass spectrometry, computational proteomics, shotgun proteomics, mass fingerprinting, retention time prediction
National Category
Biochemistry and Molecular Biology
URN: urn:nbn:se:kth:diva-139518DOI: 10.1021/pr400705qISI: 000328231300033ScopusID: 2-s2.0-84890029812OAI: diva2:687713
Swedish Research CouncilSwedish e‐Science Research CenterScience for Life Laboratory - a national resource center for high-throughput molecular bioscience

QC 20140115

Available from: 2014-01-15 Created: 2014-01-14 Last updated: 2014-01-17Bibliographically approved

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Rosenlund, MagnusKäll, Lukas
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Science for Life Laboratory, SciLifeLabGene TechnologySeRC - Swedish e-Science Research Centre
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