Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics
Abstract Background Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-03-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2619-6 |
| _version_ | 1818251787870666752 |
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| author | Philip Berg Evan W. McConnell Leslie M. Hicks Sorina C. Popescu George V. Popescu |
| author_facet | Philip Berg Evan W. McConnell Leslie M. Hicks Sorina C. Popescu George V. Popescu |
| author_sort | Philip Berg |
| collection | DOAJ |
| description | Abstract Background Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data. Results Our methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data. Conclusion Identifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method. |
| first_indexed | 2024-12-12T16:13:50Z |
| format | Article |
| id | doaj.art-1e7ef4188c3649d0af4169d4aa58b3d0 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-12T16:13:50Z |
| publishDate | 2019-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-1e7ef4188c3649d0af4169d4aa58b3d02022-12-22T00:19:08ZengBMCBMC Bioinformatics1471-21052019-03-0120S271610.1186/s12859-019-2619-6Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomicsPhilip Berg0Evan W. McConnell1Leslie M. Hicks2Sorina C. Popescu3George V. Popescu4Department of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State UniversityDepartment of Chemistry, University of North Carolina at Chapel HillDepartment of Chemistry, University of North Carolina at Chapel HillDepartment of Biochemistry, Molecular Biology, Entomology, and Plant Pathology, Mississippi State UniversityInstitute for Genomics, Biocomputing and Biotechnology, Mississippi State UniversityAbstract Background Several methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data. Results Our methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data. Conclusion Identifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.http://link.springer.com/article/10.1186/s12859-019-2619-6Post-translational modificationsRedox proteomeMass spectrometryMultiple imputationLinear regression models |
| spellingShingle | Philip Berg Evan W. McConnell Leslie M. Hicks Sorina C. Popescu George V. Popescu Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics BMC Bioinformatics Post-translational modifications Redox proteome Mass spectrometry Multiple imputation Linear regression models |
| title | Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics |
| title_full | Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics |
| title_fullStr | Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics |
| title_full_unstemmed | Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics |
| title_short | Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics |
| title_sort | evaluation of linear models and missing value imputation for the analysis of peptide centric proteomics |
| topic | Post-translational modifications Redox proteome Mass spectrometry Multiple imputation Linear regression models |
| url | http://link.springer.com/article/10.1186/s12859-019-2619-6 |
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