Show simple item record

dc.contributor.authorWang, Bing
dc.contributor.authorZhang, Jun
dc.contributor.authorChen, Peng
dc.contributor.authorJi, Zhiwei
dc.contributor.authorDeng, Shuping
dc.contributor.authorLi, Chi
dc.date.accessioned2014-08-27T09:41:26Z
dc.date.available2014-08-27T09:41:26Z
dc.date.issued2013-05-09
dc.identifier.citationWang B, Zhang J, Chen P, Ji Z, Deng S, et al. (2013) Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features. BMC Bioinformatics 14: S9. doi:10.1186/1471-2105-14-S8-S9.
dc.identifier.issn14712105
dc.identifier.pmid23815343
dc.identifier.doi10.1186/1471-2105-14-S8-S9
dc.identifier.urihttp://hdl.handle.net/10754/325237
dc.description.abstractBackground: Ion mobility-mass spectrometry (IMMS), an analytical technique which combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS), can rapidly separates ions on a millisecond time-scale. IMMS becomes a powerful tool to analyzing complex mixtures, especially for the analysis of peptides in proteomics. The high-throughput nature of this technique provides a challenge for the identification of peptides in complex biological samples. As an important parameter, peptide drift time can be used for enhancing downstream data analysis in IMMS-based proteomics.Results: In this paper, a model is presented based on least square support vectors regression (LS-SVR) method to predict peptide ion drift time in IMMS from the sequence-based features of peptide. Four descriptors were extracted from peptide sequence to represent peptide ions by a 34-component vector. The parameters of LS-SVR were selected by a grid searching strategy, and a 10-fold cross-validation approach was employed for the model training and testing. Our proposed method was tested on three datasets with different charge states. The high prediction performance achieve demonstrate the effectiveness and efficiency of the prediction model.Conclusions: Our proposed LS-SVR model can predict peptide drift time from sequence information in relative high prediction accuracy by a test on a dataset of 595 peptides. This work can enhance the confidence of protein identification by combining with current protein searching techniques. 2013 Wang et al.; licensee BioMed Central Ltd.
dc.language.isoen
dc.publisherSpringer Nature
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subject10-fold cross-validation
dc.subjectEffectiveness and efficiencies
dc.subjectIon mobility spectrometry
dc.subjectIon mobility-mass spectrometry
dc.subjectPrediction performance
dc.subjectProtein identification
dc.subjectSequence informations
dc.subjectSupport vectors regression
dc.subjectForecasting
dc.subjectIdentification (control systems)
dc.subjectIon mobility spectrometers
dc.subjectIons
dc.subjectLeast squares approximations
dc.subjectMass spectrometry
dc.subjectMolecular biology
dc.subjectStatistical tests
dc.subjectPeptides
dc.subjectpeptide
dc.subjectprotein
dc.subjectalgorithm
dc.subjectchemistry
dc.subjectmass spectrometry
dc.subjectmethodology
dc.subjectproteomics
dc.subjectregression analysis
dc.subjectsequence analysis
dc.subjectsupport vector machine
dc.subjectAlgorithms
dc.subjectLeast-Squares Analysis
dc.subjectMass Spectrometry
dc.subjectPeptides
dc.subjectProteins
dc.subjectProteomics
dc.subjectRegression Analysis
dc.subjectSequence Analysis, Protein
dc.subjectSupport Vector Machines
dc.titlePrediction of peptide drift time in ion mobility mass spectrometry from sequence-based features
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBMC Bioinformatics
dc.identifier.pmcidPMC3654891
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionAdvanced Research Institute of Intelligent Sensing Network, Tongji University, Shanghai 201804, China
dc.contributor.institutionKey Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China
dc.contributor.institutionSchool of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
dc.contributor.institutionSchool of Electronic Engineering and Automation, Anhui University, Hefei, Anhui 230601, China
dc.contributor.institutionDepartment of Medicine, University of Louisville, Louisville, KY 40202, United States
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personChen, Peng
refterms.dateFOA2018-06-13T14:34:35Z
dc.date.published-online2013-05-09
dc.date.published-print2013


Files in this item

Thumbnail
Name:
Article-BMC_Bioinf-Prediction-2013.pdf
Size:
360.1Kb
Format:
PDF
Description:
Article - Full Text

This item appears in the following Collection(s)

Show simple item record

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.