- Phosphorylation Data Analysis 89
differential analyses provide different information to assist users to easily
interpret their phosphoproteomics data.
References
- Locasale JW and Wolf-Yadlin A. (2009) Maximum entropy reconstructions of dynamic
signaling networks from quantitative proteomics data.PloS one. 4 (8):e6522. - Ochoa D, Jonikas M, Lawrence RTet al.(2016) An atlas of human kinase regulation.
Mol. Syst. Biol. 12 (12):888. - Bennetzen MV, Cox J, Mann Met al.(2012) PhosphoSiteAnalyzer: A bioinformatic
platform for deciphering phospho proteomes using kinase predictions retrieved from
NetworKIN.J. Proteome Res. 11 (6):3480–3486. - Huang da W, Sherman BT and Lempicki RA. (2009) Systematic and integrative
analysis of large gene lists using DAVID bioinformatics resources.Nat. Protoc. 4 (1):
44–57. - Herwig R, Hardt C, Lienhard Met al.(2016) Analyzing and interpreting genome data
at the network level with ConsensusPathDB.Nat. Protoc. 11 (10):1889–1907. - Raaijmakers LM, Giansanti P, Possik PA,et al.(2015) PhosphoPath: Visualization
of Phosphosite-centric Dynamics in Temporal Molecular Networks.J. Proteome Res.
14 (10):4332–4341. - Linding R, Jensen LJ, Pasculescu A et al. (2008) NetworKIN: A resource for
exploring cellular phosphorylation networks.Nucleic Acids Res. 36 (Database issue):
D695–D699. - Szklarczyk D, Franceschini A, Wyder Set al.(2015) STRING v10: Protein-protein
interaction networks, integrated over the tree of life.Nucleic Acids Res. 43 (Database
issue):D447–D452. - Petsalaki E, Helbig AO, Gopal Aet al.(2015) SELPHI: Correlation-based identifica-
tion of kinase-associated networks from global phospho-proteomics data sets.Nucleic
Acids Res. 43 (W1):W276–W282.
10.HsuCL,WangJK,LuPCet al. (2017) DynaPho: A web platform
for inferring the dynamics of time-series phosphoproteomics. Bioinformatics.
doi:10.1093/bioinformatics/btx443. - Cox J and Mann M. (2008) MaxQuant enables high peptide identification rates,
individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.
Nat. Biotechnol. 26 (12):1367–1372. - Kumar L and Futschik ME. (2007) Mfuzz: A software package for soft clustering of
microarray data.Bioinformation. 2 (1):5–7. - Schwartz D and Gygi SP. (2005) An iterative statistical approach to the identification
of protein phosphorylation motifs from large-scale data sets.Nat. Biotechnol. 23 (11):
1391–1398.