| 1. |
- Käll, Lukas, 1969-, et al.
(författare)
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Assigning significance to peptides identified by tandem mass spectrometry using decoy databases
- 2008
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 7:1, s. 29-34
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Tidskriftsartikel (refereegranskat)abstract
- Automated methods for assigning peptides to observed tandem mass spectra typically return a list of peptide-spectrum matches, ranked according to an arbitrary score. In this article, we describe methods for converting these arbitrary scores into more useful statistical significance measures. These methods employ a decoy sequence database as a model of the null hypothesis, and use false discovery rate (FDR) analysis to correct for multiple testing. We first describe a simple FDR inference method and then describe how estimating and taking into account the percentage of incorrectly identified spectra in the entire data set can lead to increased statistical power.
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| 2. |
- Käll, Lukas, 1969-, et al.
(författare)
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Posterior error probabilities and false discovery rates : two sides of the same coin
- 2008
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 7:1, s. 40-44
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Tidskriftsartikel (refereegranskat)abstract
- A variety of methods have been described in the literature for assigning statistical significance to peptides identified via tandem mass spectrometry. Here, we explain how two types of scores, the q-value and the posterior error probability, are related and complementary to one another.
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| 3. |
- Käll, Lukas, et al.
(författare)
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Semi-supervised learning for peptide identification from shotgun proteomics datasets
- 2007
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Ingår i: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; 4:11, s. 923-925
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Tidskriftsartikel (refereegranskat)abstract
- Shotgun proteomics uses liquid chromatography-tandem mass spectrometry to identify proteins in complex biological samples. We describe an algorithm, called Percolator, for improving the rate of confident peptide identifications from a collection of tandem mass spectra. Percolator uses semi-supervised machine learning to discriminate between correct and decoy spectrum identifications, correctly assigning peptides to 17% more spectra from a tryptic Saccharomyces cerevisiae dataset, and up to 77% more spectra from non-tryptic digests, relative to a fully supervised approach.
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| 4. |
- Merrihew, Gennifer E., et al.
(författare)
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Use of shotgun proteomics for the identification, confirmation, and correction of C. elegans gene annotations
- 2008
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Ingår i: Genome Research. - : Cold Spring Harbor Laboratory. - 1088-9051 .- 1549-5469. ; 18:10, s. 1660-1669
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Tidskriftsartikel (refereegranskat)abstract
- We describe a general mass spectrometry-based approach for gene annotation of any organism and demonstrate its effectiveness using the nematode Caenorhabditis elegans. We detected 6779 C. elegans proteins (67,047 peptides), including 384 that, although annotated in WormBase WS150, lacked cDNA or other prior experimental support. We also identified 429 new coding sequences that were unannotated in WS150. Nearly half (192/429) of the new coding sequences were confirmed with RT-PCR data. Thirty-three (approximately 8%) of the new coding sequences had been predicted to be pseudogenes, 151 (approximately 35%) reveal apparent errors in gene models, and 245 (57%) appear to be novel genes. In addition, we verified 6010 exon-exon splice junctions within existing WormBase gene models. Our work confirms that mass spectrometry is a powerful experimental tool for annotating sequenced genomes. In addition, the collection of identified peptides should facilitate future proteomics experiments targeted at specific proteins of interest.
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| 5. |
- Park, Christopher Y., et al.
(författare)
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Rapid and accurate peptide identification from tandem mass spectra
- 2008
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 7:7, s. 3022-3027
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Tidskriftsartikel (refereegranskat)abstract
- Mass spectrometry, the core technology in the field of proteomics, promises to enable scientists to identify and quantify the entire complement of proteins in a complex biological sample. Currently, the primary bottleneck in this type of experiment is computational. Existing algorithms for interpreting mass spectra are slow and fail to identify a large proportion of the given spectra. We describe a database search program called Crux that reimplements and extends the widely used database search program Sequest. For speed, Crux uses a peptide indexing scheme to rapidly retrieve candidate peptides for a given spectrum. For each peptide in the target database, Crux generates shuffled decoy peptides on the fly, providing a good null model and, hence, accurate false discovery rate estimates. Crux also implements two recently described postprocessing methods: a p value calculation based upon fitting a Weibull distribution to the observed scores, and a semisupervised method that learns to discriminate between target and decoy matches. Both methods significantly improve the overall rate of peptide identification. Crux is implemented in C and is distributed with source code freely to noncommercial users.
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