The necessity of adjusting tests of protein category enrichment in discovery proteomics

Bioinformatics

Volumn 26, Issue 24, 2010, Pages 3007-3011

  Louie, Brenton ^a,b   Higdon, Roger ^a,b   Kolker, Eugene ^a,b,c  

^a SEATTLE CHILDREN S RESEARCH INSTITUTE   (United States)

^b SEATTLE CHILDREN S HOSPITAL   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FUNGAL PROTEIN; PEPTIDE; PROTEIN;

ARTICLE; CHEMICAL PHENOMENA; CHEMISTRY; CLASSIFICATION; GENETICS; MASS SPECTROMETRY; METABOLISM; METHODOLOGY; PROTEOMICS; STATISTICAL MODEL;

FUNGAL PROTEINS; HYDROPHOBIC AND HYDROPHILIC INTERACTIONS; LOGISTIC MODELS; MASS SPECTROMETRY; PEPTIDES; PROTEINS; PROTEOMICS;

EID: 79951747696     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq541     Document Type: Article

References (21)

1. Alexa, A. et al. (2006) Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics, 22, 1600-1607.
2. Braisted, J. et al. (2008) The APEX quantitative proteomics tool: generating protein quantitation estimates from LC-MS/MS proteomics results. BMC Bioinformatics, 9, 529.
3. Curtis, R.K. et al. (2005) Pathways to the analysis of microarray data. Trends Biotechnol., 23, 429-435.
4. Falcon, S. and Gentleman, R. (2007) Using GOstats to test gene lists for GO term association. Bioinformatics, 23, 257-258.
5. Fusaro, V.A. et al. (2009) Prediction of high-responding peptides for targeted protein assays by mass spectrometry. Nat. Biotechnol., 27, 190-198.
6. Ghaemmaghami, S. et al. (2003) Global analysis of protein expression in yeast. Nature, 425, 737-741.
7. Hather, G. et al. (2010) Estimating false discovery rates for peptide and protein identification using randomized databases. Proteomics, 10, 2369-2376.
8. Higdon, R. and Kolker, E. (2007) A predictive model for identifying proteins by a single peptide match. Bioinformatics, 23, 277-280.
9. Higdon, R. et al. (2008)Anote on the false discovery rate and inconsistent comparisons between experiments. Bioinformatics, 24, 1225-1228.
10. Higdon, R. et al. (2010) Meta-analysis for protein identification: a case study on yeast data. OMICS, 14, 309-314.
11. Hong, E.L. et al. (2008) Gene Ontology annotations at SGD: new data sources and annotation methods. Nucleic Acids Res., 36, D577-D581.
12. Kawashima, S. and Kanehisa, M. (2000) AAindex: amino acid index database. Nucleic Acids Res., 28, 374.
13. Kolker, E. et al. (2006) Protein identification and expression analysis using mass spectrometry. Trends Microbiol., 14, 229-235.
14. Louie, B. et al. (2009) A statistical model of protein sequence similarity and function similarity reveals overly-specific function predictions. PLoS ONE, 4, e7546.
15. Mallick, P. et al. (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotechnol., 25, 125-131.
16. McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models, 2nd edn. Chapman & Hall/CRC, London.
17. Pandey, A. and Mann, M. (2000) Proteomics to study genes and genomes. Nature, 405, 837-846.
18. Rosenbaum, P.R. and Rubin, D.B. (1983) The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
19. Schrimpf, S.P. et al. (2009) Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol., 7, e48.
20. Storey, J.D. and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA, 100, 9440-9445.
21. Subramanian, A. et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA, 102, 15545-15550.