1 / 23

Generalized Protein Parsimony and Spectral Counting for Functional Enrichment Analysis

Generalized Protein Parsimony and Spectral Counting for Functional Enrichment Analysis. Nathan Edwards Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center. Systems Biology. Structured High-Throughput Experiments. Knowledge Databases.

mari
Télécharger la présentation

Generalized Protein Parsimony and Spectral Counting for Functional Enrichment Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Generalized Protein Parsimony and Spectral Counting for FunctionalEnrichment Analysis Nathan Edwards Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center

  2. Systems Biology Structured High-ThroughputExperiments KnowledgeDatabases

  3. Systems Biology molecular biology ↕phenotype molecular biology↕biology Structured High-ThroughputExperiments KnowledgeDatabases • Proteomics • Sequencing • Microarrays • Metabolomics • Localization • Function • Process • Interactions • Pathway • Mutation

  4. Systems Biology molecular biology ↕phenotype molecular biology↕biology Structured High-ThroughputExperiments KnowledgeDatabases • Proteomics • Sequencing • Microarrays • Metabolomics • Localization • Function • Process • Interactions • Pathway • Mutation MathematicalModels

  5. Systems Biology molecular biology ↕phenotype molecular biology↕biology Structured High-ThroughputExperiments KnowledgeDatabases FunctionalAnnotation Enrichment • Proteomics • Sequencing • Microarrays • Metabolomics • Localization • Function • Process • Interactions • Pathway • Mutation MathematicalModels

  6. Systems Biology molecular biology ↕phenotype molecular biology↕biology Structured High-ThroughputExperiments KnowledgeDatabases FunctionalAnnotation Enrichment • Proteomics • Sequencing • Microarrays • Metabolomics • Localization • Function • Process • Interactions • Pathway • Mutation MathematicalModels

  7. Systems Biology molecular biology ↕phenotype molecular biology↕biology Structured High-ThroughputExperiments KnowledgeDatabases FunctionalAnnotation Enrichment • Proteomics • Sequencing • Microarrays • Metabolomics • Localization • Function • Process • Interactions • Pathway • Mutation MathematicalModels

  8. Functional Annotation Enrichment • In any draw, we expect: • ~ 5 "evens", ~ 2 "≤ 10", etc. • Each ball is equally likely • Balls are independent • p-value is surprise! • For transcriptomics: • Genes ↔ Balls • Genome ↔ Tumbler • Diff. Expr. ↔ Draw • Annotation ↔ "evens",… Draw 10 of 50!

  9. Why not in proteomics? • Double counting and false positives… • …due to traditional protein inference • Proteomics cannot see all proteins… • …proteins are not equally likely to be drawn • Good relative abundance is hard… • …extra chemistries, workflows, and software • …missing values are particularly problematic

  10. In proteomics… • Double counting and false positives… • Use generalized protein parsimony • Proteomics cannot see all proteins… • Use identified proteins as background • Good relative abundance is hard… • Model differential spectral counts directly

  11. Ignore some PSMs • FDR filtering leaves some false PSMs • Enforce strict protein inference criteria • Leave some PSMs uncovered PSMs Proteins 10%

  12. Ignore some PSMs • FDR filtering leaves some false PSMs • Enforce strict protein inference criteria • Leave some PSMs uncovered PSMs Proteins 90%

  13. Match uncovered PSMs to FDR

  14. Plasma membrane enrichment • Pellicle enrichment of plasma membrane • Choksawangkarnet al. JPR 2013 (Fenselau Lab) • Six replicate LC-MS/MS analyses each • Cell-lysate (44,861 MS/MS) • Fe3O4-Al2O3 pellicle (21,871 MS/MS) • 625 3-unique proteins to match 10% FDR: • Lysate: 18,976 PSMs; Pellicle: 13,723 PSMs • 89 proteins with significantly (< 10-5) increased counts

  15. Plasma membrane enrichment • Na/K+ ATPase subunit alpha-1 (P05023): • Lysate: 1; Pellicle: 90; p-value: 5.2 x 10-33 • Transferrin receptor protein 1 (P02786): • Lysate: 17; Pellicle: 63; p-value: 2.0 x 10-11 • DAVID Bioinformatics analysis (89/625): • Plasma membrane (GO:0005886) : 29 (5.2 x 10-5) • Transmembrane (SwissProtKW): 24 (1.3 x 10-6) • Transmembrane (SwissProtKW): • Lysate: 524; Pellicle: 1335; p-value: 2.6 x 10-158

  16. A protein's PSMs rise and fall together!

  17. A protein's PSMs rise and fall together?

  18. Anomalies indicate proteoforms

  19. Nascent polypeptide-associated complex subunit alpha 7.3 x 10-8

  20. Pyruvate kinase isozymes M1/M2 2.5 x 10-5

  21. Summary • Functional annotation enrichment for proteomics too: • Careful counting (generalized parsimony) • Differential abundance by spectral counts • Use (multivariate-)hypergeometric model for • Differential abundance by spectral counts • Proteoform detection

  22. HER2/Neu Mouse Model of Breast Cancer • Paulovich, et al. JPR, 2007 • Study of normal and tumor mammary tissue by LC-MS/MS • 1.4 million MS/MS spectra • Peptide-spectrum assignments • Normal samples (Nn): 161,286 (49.7%) • Tumor samples (Nt): 163,068 (50.3%) • 4270 proteins identified in total • 2-unique generalized protein parsimony

  23. Distribution of p-values (Yeast)

More Related