Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics
<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing me...
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| Format: | Article |
| Language: | English |
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BMC
2007-11-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/8/468 |
| _version_ | 1831520852785496064 |
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| author | Huber Christian G Leinenbach Andreas Pfeifer Nico Kohlbacher Oliver |
| author_facet | Huber Christian G Leinenbach Andreas Pfeifer Nico Kohlbacher Oliver |
| author_sort | Huber Christian G |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p> |
| first_indexed | 2024-12-13T17:40:25Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-13T17:40:25Z |
| publishDate | 2007-11-01 |
| publisher | BMC |
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| series | BMC Bioinformatics |
| spelling | doaj.art-9bedd321e9864179a94c5892841f2c752022-12-21T23:36:47ZengBMCBMC Bioinformatics1471-21052007-11-018146810.1186/1471-2105-8-468Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomicsHuber Christian GLeinenbach AndreasPfeifer NicoKohlbacher Oliver<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p>http://www.biomedcentral.com/1471-2105/8/468 |
| spellingShingle | Huber Christian G Leinenbach Andreas Pfeifer Nico Kohlbacher Oliver Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics BMC Bioinformatics |
| title | Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics |
| title_full | Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics |
| title_fullStr | Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics |
| title_full_unstemmed | Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics |
| title_short | Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics |
| title_sort | statistical learning of peptide retention behavior in chromatographic separations a new kernel based approach for computational proteomics |
| url | http://www.biomedcentral.com/1471-2105/8/468 |
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