| 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, et al.
(författare)
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Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry
- 2008
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 24:16, s. i42-i48
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: A mass spectrum produced via tandem mass spectrometry can be tentatively matched to a peptide sequence via database search. Here, we address the problem of assigning a posterior error probability (PEP) to a given peptide-spectrum match (PSM). This problem is considerably more difficult than the related problem of estimating the error rate associated with a large collection of PSMs. Existing methods for estimating PEPs rely on a parametric or semiparametric model of the underlying score distribution. Results: We demonstrate how to apply non-parametric logistic regression to this problem. The method makes no explicit assumptions about the form of the underlying score distribution; instead, the method relies upon decoy PSMs, produced by searching the spectra against a decoy sequence database, to provide a model of the null score distribution. We show that our non-parametric logistic regression method produces accurate PEP estimates for six different commonly used PSM score functions. In particular, the estimates produced by our method are comparable in accuracy to those of PeptideProphet, which uses a parametric or semiparametric model designed specifically to work with SEQUEST. The advantage of the non-parametric approach is applicability and robustness to new score functions and new types of data.
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| 3. |
- 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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| 4. |
- Käll, Lukas, 1969-, et al.
(författare)
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QVALITY : non-parametric estimation of q-values and posterior error probabilities
- 2009
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 25:7, s. 964-966
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Tidskriftsartikel (refereegranskat)abstract
- Qvality is a C++ program for estimating two types of standard statistical confidence measures: the q-value, which is an analog of the p-value that incorporates multiple testing correction, and the posterior error probability (PEP, also known as the local false discovery rate), which corresponds to the probability that a given observation is drawn from the null distribution. In computing q-values, qvality employs a standard bootstrap procedure to estimate the prior probability of a score being from the null distribution; for PEP estimation, qvality relies upon non-parametric logistic regression. Relative to other tools for estimating statistical confidence measures, qvality is unique in its ability to estimate both types of scores directly from a null distribution, without requiring the user to calculate p-values.
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| 5. |
- 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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| 6. |
- 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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| 7. |
- Reynolds, Sheila M., et al.
(författare)
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Transmembrane topology and signal peptide prediction using dynamic bayesian networks
- 2008
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 4:11, s. e1000213-
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Tidskriftsartikel (refereegranskat)abstract
- Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr.
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| 8. |
- Spivak, Marina, et al.
(författare)
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Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets
- 2009
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 8:7, s. 3737-3745
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Tidskriftsartikel (refereegranskat)abstract
- Shotgun proteomics coupled with database search software allows the identification of a large number of peptides in a single experiment. However, some existing search algorithms, such as SEQUEST, use score functions that are designed primarily to identify the best peptide for a given spectrum. Consequently, when comparing identifications across spectra, the SEQUEST score function Xcorr fails to discriminate accurately between correct and incorrect peptide identifications. Several machine learning methods have been proposed to address the resulting classification task of distinguishing between correct and incorrect peptide-spectrum matches (PSMs). A recent example is Percolator, which uses semisupervised learning and a decoy database search strategy to learn to distinguish between correct and incorrect PSMs identified by a database search algorithm. The current work describes three improvements to Percolator. (1) Percolator's heuristic optimization is replaced with a clear objective function, with intuitive reasons behind its choice. (2) Tractable nonlinear models are used instead of linear models, leading to improved accuracy over the original Percolator. (3) A method, Q-ranker, for directly optimizing the number of identified spectra at a specified q value is proposed, which achieves further gains.
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