Determining the significance of protein network features and attributes using permutation testing

Methods in Molecular Biology

Volumn 1549, Issue , 2017, Pages 199-208

  Cursons, Joseph ^a,b   Davis, Melissa J ^a,b,c  

^a The University of Melbourne   (Australia)

^b WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^c UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Computational systems biology; Network structure; Permutation testing; PPI; PROSPERITI; Protein interaction; Proteomics

Indexed keywords

CONTROLLED STUDY; HUMAN; HUMAN CELL; MDA MB 231 CELL LINE; PROTEIN DETERMINATION; PROTEIN INTERACTION; PROTEIN PROTEIN INTERACTION; PROTEOMICS; SIGNAL TRANSDUCTION; TANDEM MASS SPECTROMETRY; ANIMAL; BIOLOGY; GENETICS; METABOLISM; PROCEDURES; PROTEIN ANALYSIS; PROTEIN DATABASE; SOFTWARE; SYSTEMS BIOLOGY; WEB BROWSER; WORKFLOW;

PROTEIN;

ANIMALS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; HUMANS; PROTEIN INTERACTION MAPPING; PROTEINS; PROTEOMICS; SOFTWARE; SYSTEMS BIOLOGY; WEB BROWSER; WORKFLOW;

EID: 85006341634     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-6740-7_15     Document Type: Chapter

References (15)

1. Aderem A (2005) Systems biology: its practice and challenges. Cell 121(4):511–513. doi:10.1016/j.cell.2005.04.020
2. Genovesi LA, Ng CG, Davis MJ, Remke M, Taylor MD, Adams DJ, Rust AG, Ward JM, Ban KH, Jenkins NA, Copeland NG, Wainwright BJ (2013) Sleeping beauty mutagenesis in a mouse medulloblastoma model defines networks that discriminate between human molecular subgroups. Proc Natl Acad Sci U S A 110(46):E4325–4334. doi:10.1073/pnas.1318639110
3. Gajadhar AS, White FM (2014) System level dynamics of post-translational modifications. Curr Opin Biotechnol 28:83–87. doi:10.1016/j.copbio.2013.12.009
4. Sevimoglu T, Arga KY (2014) The role of protein interaction networks in systems biomedicine. Comput Struct Biotechnol J 11(18):22–27. doi:10.1016/j.csbj.2014.08.008
5. Inder KL, Davis M, Hill MM (2013) Ripples in the pond—using a systems approach to decipher the cellular functions of membrane microdomains. Mol Biosyst 9(3):330–338. doi:10.1039/c2mb25300c
6. Sun J, Zhao Z (2010) A comparative study of cancer proteins in the human protein-protein interaction network. BMC genomics 11(Suppl 3):S5. doi:10.1186/1471-2164-11-S3-S5
7. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8(2):e1002375. doi:10.1371/journal.pcbi.1002375
8. Phipson B, Smyth GK (2010) Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat Appl Genet Mol Biol 9:39. doi:10.2202/1544-6115.1585
9. Ernst MD (2004) Permutation methods: a basis for exact inference. Stat Sci 19(4):676–685
10. Good P (2013) Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer Science & Business Media, New York, NY
11. Hochgrafe F, Zhang L, O'Toole SA, Browne BC, Pinese M, Porta Cubas A, Lehrbach GM, Croucher DR, Rickwood D, Boulghourjian A, Shearer R, Nair R, Swarbrick A, Faratian D, Mullen P, Harrison DJ, Biankin AV, Sutherland RL, Raftery MJ, Daly RJ (2010) Tyrosine phosphorylation profiling reveals the signaling network characteristics of basal breast cancer cells. Cancer Res 70(22):9391–9401. doi:10.1158/0008-5472.CAN-10-0911
12. Cowley MJ, Pinese M, Kassahn KS, Waddell N, Pearson JV, Grimmond SM, Biankin AV, Hautaniemi S, Wu J (2012) PINA v2.0: mining interactome modules. Nucleic Acids Res 40(Database Issue):D862–865. doi:10.1093/nar/gkr967
13. Hermjakob H (2006) The HUPO proteomics standards initiative—overcoming the fragmentation of proteomics data. Proteomics 6(Suppl 2):34–38. doi:10.1002/pmic.200600537
14. Knijnenburg TA, Wessels LF, Reinders MJ, Shmulevich I (2009) Fewer permutations, more accurate P-values. Bioinformatics 25(12):i161–168. doi:10.1093/bioinformatics/btp211
15. Cursons J, Leuchowius KJ, Waltham M, Tomaskovic-Crook E, Foroutan M, Bracken CP, Redfern A, Crampin EJ, Street I, Davis MJ, Thompson EW (2015) Stimulus-dependent differences in signalling regulate epithelial-mesenchymal plasticity and change the effects of drugs in breast cancer cell lines. Cell Commun Signal 13:26. doi: 10.1186/s12964-015-0106-x