A Data-Mining Scheme for Identifying Peptide Structural Motifs Responsible for Different MS/MS Fragmentation Intensity Patterns

@article{citeulike:2184201, abstract = {Abstract: Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28 330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms.}, address = {Department of Chemistry, University of Arizona, Tucson, Arizona 85721, Department of Biostatistics and Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, Department of Statistics, University of California, Los Angeles, California 90095, and Pacific Northwest National Laboratory, Richland, Washington 99352}, author = {Huang, Yingying and Tseng, George C. and Yuan, Shinsheng and Pasa-Tolic, Ljiljana and Lipton, Mary S. and Smith, Richard D. and Wysocki, Vicki H. }, citeulike-article-id = {2184201}, doi = {10.1021/pr070106u}, journal = {J. Proteome Res.}, month = {December}, posted-at = {2008-05-15 10:27:00}, priority = {2}, title = {A Data-Mining Scheme for Identifying Peptide Structural Motifs Responsible for Different MS/MS Fragmentation Intensity Patterns}, url = {http://dx.doi.org/10.1021/pr070106u}, year = {2007} }

TY - JOUR ID - citeulike:2184201 L3 - citeulike-article-id:2184201 TI - A Data-Mining Scheme for Identifying Peptide Structural Motifs Responsible for Different MS/MS Fragmentation Intensity Patterns JF - J. Proteome Res. AD - Department of Chemistry, University of Arizona, Tucson, Arizona 85721, Department of Biostatistics and Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, Department of Statistics, University of California, Los Angeles, California 90095, and Pacific Northwest National Laboratory, Richland, Washington 99352 N2 - Abstract: Although tandem mass spectrometry (MS/MS) has become an integral part of proteomics, intensity patterns in MS/MS spectra are rarely weighted heavily in most widely used algorithms because they are not yet fully understood. Here a knowledge mining approach is demonstrated to discover fragmentation intensity patterns and elucidate the chemical factors behind such patterns. Fragmentation intensity information from 28 330 ion trap peptide MS/MS spectra of different charge states and sequences went through unsupervised clustering using a penalized K-means algorithm. Without any prior chemistry assumptions, four clusters with distinctive fragmentation patterns were obtained. A decision tree was generated to investigate peptide sequence motif and charge state status that caused these fragmentation patterns. This data-mining scheme is generally applicable for any large data sets. It bypasses the common prior knowledge constraints and reports on the overall peptide fragmentation behavior. It improves the understanding of gas-phase peptide dissociation and provides a foundation for new or improved protein identification algorithms. AU - Huang, Yingying AU - Tseng, George AU - Yuan, Shinsheng AU - Pasa-Tolic, Ljiljana AU - Lipton, Mary AU - Smith, Richard AU - Wysocki, Vicki PY - 2007/12/04/ UR - http://dx.doi.org/10.1021/pr070106u ER -