PTMD 2.0 - PTMs that associate with diseases

2. ActiveDriverDB: An online database developed to visualize and explore mutations affecting PTM sites in human proteins/genes (Krassowski, et al., 2018).

3. BioMuta: A knowledgebase of cancer-associated single-nucleotide variations for cancer biomarker discovery (Dingerdissen, et al., 2018).

4. PhosphoSitePlus: A comprehensive and well annotated resource of multiple PTMs in proteins (Hornbeck, et al., 2019).

(2) Variation and mutation

5. COSMIC: A comprehensive database contains cancer mutations and cancer mRNA expression data (Forbes, et al., 2014).

6. ICGC: A public resource that provides great information of cancer mutation and gene expression data (Zhang, et al., 2011).

7. TCGA: A well-known data resource for a huge number of cancer mutations detected from clinical samples. (Cancer Genome Atlas Research Network, 2017).

8. dbSNP: The NCBI database of single nucleotide polymorphisms (SNPs) (Sherry, et al., 2001).

9. Varcards: An integrated resource for maintaining coding variants in the human genome (Li, et al., 2018).

10. KinaseMD: A databases containing SNPs in kinases (Hu, et al., 2018).

11. IntOGen: An online database that integrated cancer genomic data, which including cancer mutations (Gundem, et al., 2010).

12. BioMuta: A knowledgebase of cancer-associated single-nucleotide variations for cancer biomarker discovery (Dingerdissen, et al., 2018).

13. GWASdb: A comprehensive resource of genetic variations derived from human GWASs that corresponding with human disease. (Li, et al., 2016).

14. ActiveDriverDB: An online database developed to visualize and explore mutations affecting post-translational modification (PTM) sites in human proteins/genes (Krassowski, et al., 2018).

(3) Disease-associated information

15. ActiveDriverDB: An online database developed to visualize and explore mutations affecting PTM sites corresponding with human disease (Krassowski, et al., 2018).

16. BioMuta: A cancer-associated single-nucleotide variations knowledgebases including different cancer types (Dingerdissen, et al., 2018).

17. OMIM: A resource of relations between curated human genes and phenotypes (Amberger, et al., 2015).

18. HGV&TB: An open resource for human genetic variants corresponding with tuberculosis (Sahajpal, et al., 2014).

19. MSV3d: A new database that contains mutations on known three-dimensional structures that corresponding with human monogenic disease of proteins (Nguyen, et al., 2012).

20. NECTAR: A database of disease-associated and functional proteins (Gong, et al., 2013).

21. ClinVar: A public resource to maintain relations between human variations and phenotypes with supporting evidence (Landrum, et al., 2018).

22. MSDD: Contains the relations among miRNAs, SNPs and human diseases (Yue, et al., 2018).

23. DiseaseEnhancer: A database of human disease-associated enhancers (Zhang, et al., 2018).

24. DisGeNET: An integrative resource of human disease-associated genes and variants (Furlong, et al., 2017).

25. GAAD: A gene and autoimmiune disease association database (Guanting Lu, et al., 2018).

(4) Protein-protein interaction

26. iRefIndex: An integrative resource of PPIs (Razick, et al., 2008).

27. Mentha: A well curated PPI database (Calderone, et al., 2013).

28. DifferentialNet: A novel database that provides differential protein-protein networks of human tissues (Basha, et al., 2018).

29. TIMBAL: A database containing protein-protein interactions and molecules that modulate PPIs (Higueruelo, et al., 2013).

30. MIST: A helpful resource for annotating gene-protein and protein-protein interactions (Hu, et al., 2018).

31. IID: A well curated PPI database (Kotlyar, et al., 2016).

32. HIPPIE: An integrated database of protein-protein interactions. (Lobato, et al., 2017).

33. PINA: A well curated PPI database (Cowley, et al., 2012).

34. HINT: A curated compilation of high-quality protein-protein interactions (Das, et al., 2012).

35. inBio^Map: A scored human protein-protein interaction network database (Li, et al., 2017).

36. STRING: A database of known and predicted protein-protein interactions, covers 9,643,763 proteins from 2,031 organisms (Szklarczyk, et al., 2019).

37. HPRD: The human protein reference database that contains a lot of annotations including PPIs (Goel, et al., 2012).

38. BioGRID: A public database that curated genetic, chemical interactions and proteins with a large number of PPIs (Oughtred, et al., 2019).

(5) Protein function

39. DrLLPS: A comprehensive data resource that contained known and computationally detected LLPS-associated proteins (Ning, et al., 2019).

40. THANATOS: A database of proteins and PTMs invovled in autophagy and cell death pathways (Deng, et al., 2018).

41. iEKPD: A database contains phosphorylation regulators including protein kinases, protein phosphatases and PPBD-containing proteins (Guo, et al., 2019).

42. iUUCD: A database contained ubiquitination associated enzymes including ubiquitin activating enzymes (E1s), ubiquitin-conjugating enzymes (E2s), ubiquitin-protein ligases (E3s), deubiquitinating enzymes (DUBs), ubiquitin/ubiquitin-like binding domains (UBDs) and ubiquitin-like domains (ULDs) (Zhou, et al., 2018).

43. CGDB: A database resource provides validated circadian genes collected from small-scale and high-throughput studies (Li, et al., 2016).

44. WERAM: A database of writers, erasers and readers of histone acetylation and methylation in eukaryotes (Xu, et al., 2016).

45. AmyPro: A data resource of experimentally identified amyloid precursor proteins and their amyloidogenic sequence regions (Varadi, et al., 2018).

46. MultitaskProtDB-II: A open access that contains multitasking and moonlighting proteins (Serrano, et al., 2018).

47. GPCRdb: A open resource that contains G protein-coupled receptors (GPCRs) and GPCR ligands with data on biological activities (Szekeres, et al., 2018).

48. MeDReaders: MeDReaders collected transcription factors that have methylated DNA binding activities (Wang, et al., 2018).

49. neXtProt: A human protein-centric knowledge platform (Gaudet, et al., 2017).

50. CORUM: The comprehensive resource of mammalian protein complexes (Giurgiu, et al., 2019).

51. HAMAP: A database for classification of protein families (Pedruzzi, et al., 2015).

52. CellMarker: A curated resource of cell biomarkers in human and mouse (Zhang, et al., 2019).

53. MoonDB: An updated database of extreme multifunctional and moonlighting proteins (Ribeiro, et al., 2019).

54. Membranome: Database for proteome-wide profiling of single-pass membrane proteins (Lomize, et al., 2018).

55. AnimalTFDB 3.0: A comprehensive resource for annotation and prediction of animal transcription factors (Hu, et al., 2019).

56. EuRBPDB: A comprehensive and user-friendly database for eukaryotic RNA binding proteins (RBPs) (Liao, et al., 2020).

57. ATtRACT: A database of RNA-binding proteins and associated motifs (Giudice, et al., 2016).

58. TISSUES 2.0: A database of tissue-specific expressions of mammalian genes (Palasca, et al., 2018).

59. PlantTFDB 4.0: A central hub for transcription factors and regulatory interactions in plants (Jin, et al., 2017).

60. PTMcode 2.0: A database containing post-translational regulation and protein-protein interaction (Minguez, et al., 2015).

(6) DNA & RNA element

61. miRTarBase: A resource for experimentally validated microRNA-target interactions (Chou, et al., 2018).

62. TargetScanHuman: TargetScan predicts biological targets of miRNAs by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA (Agarwal, et al., 2015).

63. miRWalk: The new version of miRWalk stores predicted data obtained with a maschine learning algorithm including experimentally verified miRNA-target interactions. (Sticht, et al., 2018).

64. miRcode: MiRcode provides "whole transcriptome" human microRNA target predictions based on the comprehensive GENCODE gene annotation, including >10,000 long non-coding RNA genes (Jeggari, et al., 2012).

65. LncRNADisease: LncRNADisease 2.0 collected more than 200 000 experimental supported lncRNA-disease associations (Bao, et al., 2019).

66. somamiR: SomamiR contains cancer somatic mutations in microRNAs (miRNA) with their target sites affectting the interactions between miRNAs and competing endogenous RNAs (ceRNA) (Bhattacharya, et al., 2015).

67. RISE: A database collecting expetimental RNA interactome. RNA-RNA interactions are important for RNA regulation and function. (Gong, et al., 2018).

68. RNAInter: A database of RNA interactome with annotation, containing great unique molecules and RNA-protein interactions (Lin, et al., 2019).

69. miRecords: A database of integrated animal miRNA-target interactions, including 1135 validated miRNA-target interactions (Xiao, et al., 2008).

70. circBase: A database for circular RNAs (circRNAs) providing their expression with supporting evidence (Glažar, et al., 2014).

71. RAIN: A database integrated ncRNA-RNA and ncRNA-protein association, also providing a confidence score for each interaction (Junge, et al., 2017).

72. UTRdb: A collection of sequences and regulatory motifs of the untranslated regions of eukaryotic mRNAs (Mignone, et al., 2005).

73. RegNetwork: An open resource that provides transcriptional networks of human and mouse (Liu, et al., 2015).

74. TRRUST: A reference resource of human and mouse transcriptional regulatory interactions (Han, et al., 2018).

75. YTRP: A database of the collection of transcription factor (TF)-gene regulatory pairs (Yang, et al., et al. 2014).

(7) Protein structural annotation

76. PDB: A leading resource of structural data of biological macromolecules (Berman, et al., 2000).

77. MMDB: A database links protein 3D structure data, the sequence data and classification resources with PubChem (Madej, et al., 2012).

78. SCOP: A database provides the framework for protein structure classification and annotation (Andreeva, et al., 2014).

79. DNAproDB: A database contains biochemical features and structural from structures of DNA-protein complexes (Sagendorf, et al., 2019).

80. MobiDB 3.0: A database of protein disorder and mobility annotations (Piovesan, et al., 2018).

(8) Chemical

81. CTD: The Comparative Toxicogenomics Database contains chemicals, genes, diseases, phenotypes, and exposures information of understanding human health (Davis AP, et al., 2020).

82. DrugBank: Contains 9,591 drug entries including 2,037 FDA-approved small molecule drugs, 241 FDA-approved protein/peptide drugs, 96 nutraceuticals and over 6,000 experimental drugs (Law, et al., 2014).

83. DGIdb 3.0: A database of drug-gene interactions and gene druggability information from papers (Kelsy, et al., 2018).

84. TTD: Contains 2,025 targets, including 364 successful, 286 clinical trial, 44 discontinued and 1,331 research targets, 17,816 drugs, including 1,540 approved, 1,423 clinical trial, 14,853 experimental drugs and 3,681 multi-target agents (Zhu, et al., 2012).

85. ADReCS-Target: A database collecting illustrating ADRs caused by drug interactions with molecules in organism and their variation (Huang, et al., 2018).

86. ECOdrug: A database contains drugs and conservation of their targets across different species (Verbruggen, et al., 2018).

87. GtoPdb: Providing pharmacological, chemical, genetic, functional and pathophysiological data on the targets of approved and experimental drugs (Harding, et al., 2018).

88. BindingDB: A public resource containing experimentally verified protein-protein and protein-small molecule interaction data (Gilson, et al., 2016).

89. DrugCentral: A drug information resource integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies (Ursu, et al., 2019).

90. PLIC: The annotation of protein-ligand interaction clusters (Anand, et al., 2014).

(9) mRNA expression

91. TCGA: TCGA is a public resource that cotains major cancer-causing genomic alterations and gene expressions, aming to provide a comprehensive cancer genomic profiles (Cancer Genome Atlas Research Network, 2017).

92. ArrayExpress: A public resource of microarray-based gene expression data, resulting from the implementation of the MAGE object model (Serra, et al., 2003).

93. BioXpress: A database of cancer-associated differentially expressed genes and microRNAs (Dingerdissen, et al., 2018).

94. COSMIC: Besides cancer mutations, COSMIC also contains cancer mRNA expression data (Forbes, et al., 2014).

95. The Human Protein Atlas: Besides the proteomic data, the Human Protein Atlas also provided RNA gene data for RNA levels in 64 cell lines and 37 tissues based on RNA-seq (Pontén, et al., 2011).

96. Human Proteome Map: Besides the proteomic data, the Human Proteome Map (HPM) also provides mRNA expression data (Kim, et al., 2014).

97. ICGC: Maintains a huge number of cancer mutations detected from clinical samples. (Zhang, et al., 2011).

98. TissGDB: A database containing tissue-specific gene expression data in cancer (Kim, et al., 2018).

99. TISSUES 2.0: A database of tissue-specific expressions of mammalian genes (Palasca, et al., 2018).

(10) Protein expression/Proteomics

100. Human Proteome Map: Containing proteins encoded by 17,294 genes that were detected in 30 histologically normal human samples (Kim, et al., 2014).

101. The Human Protein Atlas: Besides the proteomic data, the Human Protein Atlas also provided RNA gene data for RNA levels in 64 cell lines and 37 tissues based on RNA-seq (Pontén, et al., 2011).

(11) Subcellular localization

102. COMPARTMENTS: A database that provides annotation and visualization of protein subcellular localizations with confidence scores to the localization evidence (Binder, et al., 2014).

103. ComPPI: A cellular compartment-specific database for protein–protein interaction network analysis with eight subcellular localization (Veres, et al., 2014).

104. NLSdb: A database collecting experimentally nuclear and non-nuclear proteins with annotations and nuclear export signals (NES) and nuclear localization signals (NLS) (Bernhofer, et al., 2019).

105. MiCroKiTS: A database of midbody, centrosome, kinetochore, telomere and spindle subcellular localization (Huang, et al., 2014).

106. Translocatome: A database of manually curated data set of 213 human translocating proteins with several translocation mechanism, local compartmentalized interactome and involvement in signalling pathways and disease development (Mendik, et al., 2019).

(12) Biological pathway

107. SignaLink: An integrated resource to analyze signaling pathway cross-talks, transcription factors, miRNAs and regulatory enzymes (Fazekas, et al., 2013).

108. Reactome: Provides molecular details of signal transduction, transport, DNA replication, metabolism, and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model (Fabregat, et al., 2018).

109. KEGG: A database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (Kanehisa, et al., 2019).

110. PathBank: A comprehensive database of more than 110,000 annatated pathways of 10 model organisms (Wishart, et al., 2020).

(13) Domain annotation

111. InterPro: A database provides functional analysis of proteins by familiy classification and predicted domains (Mitchell, et al., 2019).

112. Pfam: A widely used database of protein families, containing 14,831 manually curated entries in the current release (Gebali, et al., 2019).

113. PIRSF: Reflects evolutionary relationships of full-length proteins and domains (Nikolskaya, et al., 2007).

114. PRINTS: A collection of diagnostic 'fingerprints' protein family integrated 2156 fingerprints, encoding 12 444 individual motifs (Attwood, et al., 2012).

115. PROSITE: Consists of documentation entries describing protein domains, families and functional sites, as well as associated patterns and profiles to identify them (Sigrist, et al., 2013).

(14) Physicochemical property

116. Compute pI/Mw: A tool for computing the theoretical pI and Mw of proteins (Wilkins, et al., 1999).

117. AAindex: A database of various indices for physicochemical and biochemical properties of amino acids and pairs of amino acids (Kawashima, et al., 2008).

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