DynaPho is a web-based and user-friendly platform to comprehensively analyze temporal phosphoproteome datasets. It consists of one preprocessing and five sequential analyzing modules to infer the dynamic phosphorylation signaling. In human HeLa cell cycle dataset, statistical analysis reveals that seven percentage of labeling ratios (6,305 ratios over 2 S.D.) are potential for analyzing and distribute over 14,703 phosphorylation events. After the analysis of profile clustering, eight co-expression profiles are identified. They are further analyzed by function enrichment module. It not only reveals unified biological information but also resolves more deep into the dynamic phosphorylation profiles. After the analysis of function enrichment, DynaPho summarizes core processes over all cycle stages in a functional network and also reveals detailed biological processes. Besides, DynaPho also embraces all biological processes and their dynamic signaling among all cell cycle stages in a heatmap. After the analysis of kinase activation profile module, DynaPho finds potential kinases and further presents the temporal profiles of both activation and deactivation. For instance, both AKT1 and PAK4 are involved in homeostasis functions and CDK1 involved in G1, S, and G2 stage. In the interaction network module, DynaPho links sequential phosphorylation events across all cell cycle stages for the comprehensive signaling, such as EGFR-STAT pathway in G1/S stage and the signaling from RanBP2 to ErbB2 in mitosis/G1 stage. DynaPho improves many shortages of traditional analyses and strengthens the analysis of phosphoproteome. The advancement of modern mass spectrometry technology and the integrity of bioinformatics analyses, to make the analysis of dynamic signaling cell is possible.
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REFERENCES
1. Cohen, P., The origins of protein phosphorylation. Nat Cell Biol, 2002. 4(5): p.
E127-30.
2. Ying, H., et al., The Rho kinase inhibitor fasudil inhibits tumor progression in human and rat tumor models. Mol Cancer Ther, 2006. 5(9): p. 2158-64.
3. Liu, D., et al., Clinical pharmacokinetics of Icotinib, an anti-cancer drug: evaluation of dose proportionality, food effect, and tolerability in healthy subjects. Cancer Chemother Pharmacol, 2014. 73(4): p. 721-7.
4. Bray, L., et al., Identification and functional characterization of the phosphorylation sites of the neuropeptide FF2 receptor. J Biol Chem, 2014. 289(49): p. 33754-66.
5. Ubersax, J.A. and J.E. Ferrell, Jr., Mechanisms of specificity in protein phosphorylation. Nat Rev Mol Cell Biol, 2007. 8(7): p. 530-41.
6. Choudhary, C. and M. Mann, Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Biol, 2010. 11(6): p. 427-39.
7. Deutsch, E.W., H. Lam, and R. Aebersold, PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep, 2008. 9(5): p.
429-34.
8. Mathivanan, S., et al., Human Proteinpedia enables sharing of human protein data.
Nat Biotechnol, 2008. 26(2): p. 164-7.
9. Slotta, D.J., T. Barrett, and R. Edgar, NCBI Peptidome: a new public repository for mass spectrometry peptide identifications. Nat Biotechnol, 2009. 27(7): p. 600-1.
10. Hornbeck, P.V., et al., PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res, 2012. 40(Database issue): p.
D261-70.
27
11. Dinkel, H., et al., Phospho.ELM: a database of phosphorylation sites--update 2011.
Nucleic Acids Res, 2011. 39(Database issue): p. D261-7.
12. Obenauer, J.C., L.C. Cantley, and M.B. Yaffe, Scansite 2.0: Proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res, 2003.
31(13): p. 3635-41.
13. Xue, Y., et al., PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory. BMC Bioinformatics, 2006. 7: p. 163.
14. Li, H., et al., SysPTM: a systematic resource for proteomic research on post-translational modifications. Mol Cell Proteomics, 2009. 8(8): p. 1839-49.
15. Naegle, K.M., et al., PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies. Mol Cell Proteomics, 2010. 9(11): p. 2558-70.
16. Poullet, P., S. Carpentier, and E. Barillot, myProMS, a web server for management and validation of mass spectrometry-based proteomic data. Proteomics, 2007. 7(15):
p. 2553-6.
17. Howes, C.G. and L.J. Foster, PrestOMIC, an open source application for dissemination of proteomic datasets by individual laboratories. Proteome Sci, 2007. 5:
p. 8.
18. Yu, K. and A.R. Salomon, PeptideDepot: flexible relational database for visual analysis of quantitative proteomic data and integration of existing protein information.
Proteomics, 2009. 9(23): p. 5350-8.
19. Courcelles, M., et al., ProteoConnections: a bioinformatics platform to facilitate proteome and phosphoproteome analyses. Proteomics, 2011. 11(13): p. 2654-71.
20. Ubaida Mohien, C., et al., MASPECTRAS 2: An integration and analysis platform for proteomic data. Proteomics, 2010. 10(14): p. 2719-22.
21. Albaum, S.P., et al., Qupe--a Rich Internet Application to take a step forward in the analysis of mass spectrometry-based quantitative proteomics experiments.
28
Bioinformatics, 2009. 25(23): p. 3128-34.
22. Linding, R., et al., NetworKIN: a resource for exploring cellular phosphorylation networks. Nucleic Acids Res, 2008. 36(Database issue): p. D695-9.
23. Miller, M.L., et al., Linear motif atlas for phosphorylation-dependent signaling. Sci Signal, 2008. 1(35): p. ra2.
24. Horn, H., et al., KinomeXplorer: an integrated platform for kinome biology studies.
Nat Methods, 2014. 11(6): p. 603-4.
25. Petsalaki, E., et al., SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets. Nucleic Acids Res, 2015.
43(W1): p. W276-82.
26. Cox, J. and M. Mann, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.
Nat Biotechnol, 2008. 26(12): p. 1367-72.
27. Olsen, J.V., et al., Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci Signal, 2010. 3(104): p. ra3.
28. Sharma, K., et al., Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep, 2014. 8(5): p. 1583-94.
29. Horai, H., et al., MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom, 2010. 45(7): p. 703-14.
30. Chatr-Aryamontri, A., et al., The BioGRID interaction database: 2015 update.
Nucleic Acids Res, 2015. 43(Database issue): p. D470-8.
31. Keshava Prasad, T.S., et al., Human Protein Reference Database--2009 update.
Nucleic Acids Res, 2009. 37(Database issue): p. D767-72.
32. Hermjakob, H., et al., IntAct: an open source molecular interaction database. Nucleic Acids Res, 2004. 32(Database issue): p. D452-5.
33. Rual, J.F., et al., Towards a proteome-scale map of the human protein-protein
29
interaction network. Nature, 2005. 437(7062): p. 1173-8.
34. Licata, L., et al., MINT, the molecular interaction database: 2012 update. Nucleic Acids Res, 2012. 40(Database issue): p. D857-61.
35. Schwartz, D. and S.P. Gygi, An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat Biotechnol, 2005.
23(11): p. 1391-8.
36. Hu, J., et al., PhosphoNetworks: a database for human phosphorylation networks.
Bioinformatics, 2014. 30(1): p. 141-2.
37. Kumar, L. and E.F. M, Mfuzz: a software package for soft clustering of microarray data. Bioinformation, 2007. 2(1): p. 5-7.
38. J., C., Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 1974. 3(3): p. 32-57.
39. Newton, K. and V.M. Dixit, Signaling in innate immunity and inflammation. Cold Spring Harb Perspect Biol, 2012. 4(3).
40. Yan, S., et al., Down-regulation of Cbl-b by bufalin results in up-regulation of DR4/DR5 and sensitization of TRAIL-induced apoptosis in breast cancer cells. J Cancer Res Clin Oncol, 2012. 138(8): p. 1279-89.
41. Yoav, B. and H. Yosef, Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, 1995.
57(1): p. 289-300.
42. Simpson, T.I., J.D. Armstrong, and A.P. Jarman, Merged consensus clustering to assess and improve class discovery with microarray data. BMC Bioinformatics, 2010.
11: p. 590.
43. Shannon, P., et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003. 13(11): p. 2498-504.
44. Enserink, J.M. and R.D. Kolodner, An overview of Cdk1-controlled targets and
30
processes. Cell Div, 2010. 5: p. 11.
45. Altomare, D.A. and A.R. Khaled, Homeostasis and the importance for a balance between AKT/mTOR activity and intracellular signaling. Curr Med Chem, 2012.
19(22): p. 3748-62.
46. Guo, Q., et al., PAK4 kinase-mediated SCG10 phosphorylation involved in gastric cancer metastasis. Oncogene, 2014. 33(25): p. 3277-87.
47. Collin de L'hortet, A., H. Gilgenkrantz, and J.E. Guidotti, EGFR: A Master Piece in G1/S Phase Transition of Liver Regeneration. Int J Hepatol, 2012. 2012: p. 476910.
48. Giri, D.K., et al., Endosomal transport of ErbB-2: mechanism for nuclear entry of the cell surface receptor. Mol Cell Biol, 2005. 25(24): p. 11005-18.
49. Renner, O., et al., Mst1, RanBP2 and eIF4G are new markers for in vivo PI3K activation in murine and human prostate. Carcinogenesis, 2007. 28(7): p. 1418-25.
50. Vadlakonda, L., M. Pasupuleti, and R. Pallu, Role of PI3K-AKT-mTOR and Wnt Signaling Pathways in Transition of G1-S Phase of Cell Cycle in Cancer Cells. Front Oncol, 2013. 3: p. 85.
51. Ashburner, M., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1): p. 25-9.
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FIGURES
Figure 1 Dynamic signaling represents what conditions the cell had undergone.
The example RAS-RAF-MEK-ERK pathway is activated by epidermal growth factor (EGF).
After the cell is simulated by EGF, kinases transfer a phosphate group from GTP to RAS protein. The signaling starts from RAS protein to extracellular-signal-regulated kinases (ERK). The phosphorylated ERK activates different types of transcription factors. The activated transcription factor starts downstream gene expression. If experiment on sequential time points under control and test conditions, it is highly possible to capture the dynamics of multiple proteins from MS data. These dynamics are the best interpreter what the cell had undergone.
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Figure 2 DynaPho interprets biological information on the downstream of analyzing.
The control and test samples are first prepared on multiple steps in order to get linear phosphorylation peptides as more as possible. The phosphorylation peptides are separated by liquid chromatography (LC) and then identified by mass spectrometry (MS). The spectrum of peptides and their corresponding proteins can be identified or mapped back by searching engine on the basis of spectral databases (for example MassBank). The raw data generated by the searching engine contains temporal labeling ratios, protein session names and phosphosite sequences, etc. Such raw data can be processed into the original data for DynaPho. DynaPho is located on the downstream of flow of MS data interpreting.
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Figure 3 The basic format of the upload file
The format accepted by DynaPho is a table prepared by processing the raw phosphosite data from MSQuant or manually generating from non-labeling datasets. Constraints on the dataset include more than fifteen phosphorylated events. Each one contains more than one uniprot accession name, more than seven amino acids on phosphorylation sequence and more than three labeling ratios. The uniprot accession name or phosphorylation sequences can be multiple in the same column and be separated by a semicolon (;).
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Figure 4 The architecture and workflow of Dyanpho
Users can start analyzing data from new upload file or the historical one. Each new upload file must be preprocessed first. Suggested analysis flow starts from statistical analysis, profile clustering, function enrichment, kinase activation profile and then interaction network.
Crossing analysis also exists in DynaPho, the result from profile clustering module can be further analyzed by function enrichment module.
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Figure 5 The analyzing flow of statistics module
The statistics module is composed of two separated analyses, one is the proportion of each phosphorylated site and the distribution chart of total labeling ratios, and the other one is plotting labeling ratio changes of interested phosphorylation events selected by users.
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Figure 6 The analyzing flow of profile clustering module
The flow consists of two steps, generating clustering number and clustering the co-expression phosphorylation events. The clustering number is determined either by users or the detection algorithm in Dyanpho. Fuzzy c-means clustering takes the clustering number as a parameter and clusters phosphorylation events whose trends are similar.
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Figure 7 The example of auto detection method for determining clustering number Parameter of the example is the same with defaults (inner z-scored S.D. is 1.1, variation threshold in specific time is 0.01 S.D. and the number threshold is 1%). There are five different trend profiles for five clusters labeled with different colors in the example.
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Figure 8 The analyzing flow of function enrichment module
The non-repeated protein session names are selected from temporal analyzing by fold change or standard deviation or from the cluster calculated by profile clustering module. Selected proteins and total uniprot proteins are analyzed by the hypergeometric test with the biological processes database of Gene Ontology. The function enrichment network and the dynamics of biological processes are further analyzed to present core and detailed functions.
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Figure 9 The analyzing flow of kinase activation profile module
All phosphorylation sequences are separated into 3 sets based on the center phosphosite. Each set is sent to motif-x separately and then DynaPho fetches the conserved motif information.
DynaPho further generates a PSSM table for each conserved motif. The correlation between PSSMs and PhosphoNetworks databases shows potential kinases. Conserved motifs are reduced into smaller clusters by clusterCons algorithm. Temporal profiles of both kinase activation and deactivation are generated by fisher’s exact test.
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Figure 10 The example of generating the PSSM table of each conserved motifs
The conserved motif, “.I….SP.K…”, is obtained from motif-x. The following 26 sequences are members contributing to the motif and each one is composed of 13 different amino acids.
Start from the center phosphosite, the number -6 to -1 and 1 to 6 are relative sequence positions on both sides of it. The x-axis in PSSM consists of total amino acids and y-axis is the relative position. The number in PSSM is the proportion of the amino acid in current position.
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Figure 11 The analyzing flow of interaction network module
Phosphorylation events are filtered by the standard deviation or the fold change. Map all phosphorylation sequences back into proteins and prevent repeated ones. These proteins construct a interaction network. If two proteins are not interacted in the specific time, DynaPho links both them with intermediary proteins which are connected to each one but are not significant expression in the current time.
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Figure 12 The number and proportion of each phosphorylation sites
The pie chart presents the proportion of three phosphosite, serine (S), threonine (T) and tyrosine (Y) with their numbers in the sequence pool. The dashed ‘-’ presents the number of phosphosites which are not S, T or Y in the sequence.
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Figure 13 The distribution of all labeling ratios in log2 scaled
The space of each column in x-axis is 0.2. Blue, green and orange respectively stand for ratios in 2 S.D., more than 2 S.D. and less than 3 S.D., and over 3.S.D. The plot is generated by R script with Plotly.
44
Biological process (GO) -log10 (adj. P)
gene expression 40.22
mRNA metabolic process 40.22
cell cycle process 37.92
microtubule-based process 35.42
RNA processing 31.04
cellular macromolecular complex assembly 30.92 cytoskeleton organization 30.07 mitotic cell cycle process 27.78 regulation of organelle organization 25.14
protein complex assembly 22.57
multi-organism cellular process 21.23 1,099 (7.475 %)
Biological process (GO) -log10 (adj. P)
cell cycle process 82.48
mitotic cell cycle process 67.10
RNA processing 62.59
microtubule-based process 58.10 cytoskeleton organization 50.07
mRNA metabolic process 49.92
chromosome organization 49.03
cellular macromolecular complex assembly 46.63
gene expression 42.10
protein complex assembly 40.05
mitotic cell cycle 38.28
1,746 (11.875 %)
Biological process (GO) -log10 (adj. P)
mitotic cell cycle process 63.37
cytoskeleton organization 41.92
chromosome organization 36.68
muscle cell cellular homeostasis 31.00 microtubule cytoskeleton organization 27.92 negative regulation of phosphorus
metabolic process
27.80 cellular macromolecular complex assembly 27.19
regulation of cell cycle 25.40
regulation of peptidyl-cysteine S-nitrosylation
25.37 1,010 (6.869 %)
45
Biological process (GO) -log10 (adj. P)
RNA processing 38.79
cell cycle process 35.04
chromosome organization 34.13
mRNA metabolic process 30.47
cellular response to DNA damage stimulus 30.17
gene expression 27.20
Biological process (GO) -log10 (adj. P)
mRNA metabolic process 117.77
chromosome organization 117.69
RNA processing 116.73
cell cycle process 112.33
mitotic cell cycle process 110.88
gene expression 98.87
chromatin organization 96.81
symbiosis, encompassing mutualism through parasitism
89.97 regulation of organelle organization 75.14
cytoskeleton organization 73.88
cellular macromolecular complex assembly 73.58 negative regulation of cellular
cellular response to DNA damage stimulus 52.07 transcription from RNA polymerase II
promoter
43.76 single-organism intracellular transport 43.31 6,394 (43.488 %)
46
Biological process (GO) -log10 (adj. P) regulation of peptidyl-cysteine S-nitrosylation 72.10 olfactory nerve structural organization 69.49 establishment of glial blood-brain barrier 68.86 regulation of skeletal muscle contraction by
regulation of release of sequestered calcium ion neurotransmitter receptor metabolic process 62.75 cardiac muscle cell action potential 62.26
nucleus localization 60.99
1,151 (7.828 %)
Biological process (GO) -log10 (adj. P)
cell cycle process 43.80
chromosome organization 33.98
cellular response to DNA damage stimulus 31.21
mRNA metabolic process 27.12
RNA splicing 25.77
gene expression 21.32
regulation of cell cycle 20.84
regulation of organelle organization 19.35 771 (5.244 %)
Biological process (GO) -log10 (adj. P) mitotic cell cycle process 74.06 cellular macromolecular complex assembly 71.74
RNA processing 71.13
muscle cell cellular homeostasis 68.61 regulation of peptidyl-cysteine S-nitrosylation 68.48 olfactory nerve structural organization 68.30 establishment of glial blood-brain barrier 67.59 regulation of skeletal muscle contraction by
regulation of release of sequestered calcium ion
67.26 1,386 (9.427 %)
47
T1 T2 T3 T4 T5 T6
mitosis G1 G1/S early S Late S G2
Figure 14 Co-expression clustering of dynamic phosphorylation profiles
Fuzzy c-means is a soft clustering algorithm, trends of phosphorylation events which are colored in the light green stand for the outlier of the cluster. On the contrary, ones which are colored in darker red mean the core of the cluster. The number under the plot presents the number of phosphorylation events in the cluster. The “adj. P” is the abbreviation of adjusted p-value.
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Figure 15 The summary of core functions over all cell cycle stages
The node and edge in the network respectively represents a GO term and the proportion of joint proteins. The size of each GO term is directly proportional to the background protein frequency. Current layout in the enrichment network analysis is implemented that parameter of similarity and style is respectively 0.3 and Cose.
49
A
Biological process (GO) -log10 (adj. P)
chromosome organization 37.35
mitotic cell cycle process 37.32
chromatin organization 26.32
cytoskeleton organization 24.80
negative regulation of RNA metabolic process 22.41
symbiosis, encompassing mutualism through parasitism 21.73
viral process 21.73
multi-organism cellular process 21.64
interspecies interaction between organisms 21.36
nuclear envelope organization 21.22
negative regulation of RNA biosynthetic process 21.01
mRNA transport 20.46
nucleic acid transport 19.79
mitotic nuclear envelope disassembly 19.44
negative regulation of cellular macromolecule biosynthetic process 19.34
RNA splicing 19.05
nucleobase-containing compound transport 19.02
negative regulation of gene expression 18.76
mRNA metabolic process 18.58
membrane disassembly 17.70
50
B
Biological process (GO) -log10 (adj. P)
regulation of peptidyl-cysteine S-nitrosylation 97.67
olfactory nerve structural organization 96.73
regulation of skeletal muscle contraction by regulation of release of sequestered calcium ion
96.73
establishment of glial blood-brain barrier 96.18
positive regulation of sodium ion transmembrane transporter activity 94.74
establishment of blood-nerve barrier 94.06
neurotransmitter receptor metabolic process 90.64
regulation of voltage-gated calcium channel activity 90.17 regulation of ryanodine-sensitive calcium-release channel activity 86.68 negative regulation of peptidyl-serine phosphorylation 85.52
nucleus localization 85.37
cardiac muscle cell action potential 85.11
myotube cell development 79.89
muscle cell cellular homeostasis 78.33
positive regulation of cell-matrix adhesion 74.82
muscle fiber development 71.91
skeletal muscle tissue development 70.07
regulation of intracellular transport 66.67
cellular protein complex assembly 50.32
cellular protein localization 45.40
51
C
Biological process (GO) -log10 (adj. P)
negative regulation of peptidyl-cysteine S-nitrosylation 102.36
olfactory nerve structural organization 101.33
regulation of skeletal muscle contraction by regulation of release of sequestered calcium ion
101.33 negative regulation of peptidyl-serine phosphorylation 100.88
establishment of glial blood-brain barrier 100.82
positive regulation of sodium ion transmembrane transporter activity 99.49
neurotransmitter receptor metabolic process 95.11
regulation of voltage-gated calcium channel activity 94.77 regulation of cardiac muscle contraction by regulation of the release of
sequestered calcium ion
94.04
nucleus localization 89.93
regulation of ryanodine-sensitive calcium-release channel activity 88.83
myotube cell development 84.43
muscle cell cellular homeostasis 82.85
positive regulation of cell-matrix adhesion 79.33
muscle fiber development 76.42
receptor metabolic process 72.28
regulation of intracellular transport 69.80
positive regulation of cell-substrate adhesion 68.77
regulation of ion transmembrane transport 62.10
cellular protein complex assembly 55.30
cellular macromolecular complex assembly 48.73
cellular protein localization 48.47
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D
Biological process (GO) -log10 (adj. P)
negative regulation of peptidyl-cysteine S-nitrosylation 87.48
olfactory nerve structural organization 86.66
regulation of skeletal muscle contraction by regulation of release of sequestered calcium ion
86.46
establishment of glial blood-brain barrier 86.15
positive regulation of sodium ion transmembrane transporter activity 84.45
neurotransmitter receptor metabolic process 80.45
regulation of voltage-gated calcium channel activity 79.73 regulation of cardiac muscle contraction by regulation of the release of
sequestered calcium ion 79.00
nucleus localization 77.60
myotube cell development 69.78
positive regulation of cell-matrix adhesion 66.78
muscle fiber development 61.79
cellular protein complex assembly 47.64
cellular protein localization 41.61
regulation of intracellular transport 38.35
mitotic cytokinesis 28.54
chromosome organization 28.49
negative regulation of microtubule depolymerization 20.11 metaphase/anaphase transition of mitotic cell cycle 20.10
mitotic chromosome condensation 19.57
negative regulation of organelle organization 16.51
DNA packaging 16.08
regulation of chromosome segregation 15.11
53
E
Biological process (GO) -log10 (adj. P)
mitotic cell cycle process 25.54
cell cycle process 19.60
multi-organism cellular process 16.63
symbiosis, encompassing mutualism through parasitism 16.63
viral process 16.63
interspecies interaction between organisms 16.48
ATP catabolic process 13.09
purine nucleoside monophosphate catabolic process 13.03 purine ribonucleoside monophosphate catabolic process 13.03
ribonucleoside monophosphate catabolic process 13.03
nucleoside monophosphate catabolic process 12.98
DNA conformation change 10.23
single-organism intracellular transport 10.01
54
F
Biological process (GO) -log10 (adj. P)
negative regulation of peptidyl-cysteine S-nitrosylation 118.64
olfactory nerve structural organization 117.50
regulation of skeletal muscle contraction by regulation of release of sequestered calcium ion
117.50 positive regulation of sodium ion transmembrane transporter activity 115.35
establishment of blood-nerve barrier 114.39
neurotransmitter receptor metabolic process 110.53
regulation of voltage-gated calcium channel activity 110.22 regulation of cardiac muscle contraction by regulation of the release of
sequestered calcium ion
109.43 negative regulation of peptidyl-serine phosphorylation 105.29
nucleus localization 104.91
cardiac muscle cell action potential 102.21
myotube cell development 99.01
muscle cell cellular homeostasis 97.33
positive regulation of cell-matrix adhesion 93.62
skeletal muscle tissue development 91.14
skeletal muscle tissue development 91.14