STRING: Protein association networks. Lars Juhl Jensen
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1 STRING: Protein association networks Lars Juhl Jensen
2 interaction networks
3 association networks
4 guilt by association
5
6 protein networks
7 STRING
8 9.6 million proteins
9 common foundation
10 Exercise 1 Go to Query for human insulin receptor (INSR) using the search by name functionality Make sure you are in evidence view" (check the buttons below the network) Why are there multiple lines connecting the same to two proteins?
11 curated knowledge
12 (what we know)
13 protein complexes
14 3D structures
15
16 pathways
17 metabolic pathways
18 Letunic & Bork, Trends in Biochemical Sciences, 2008
19 signaling pathways
20 very incomplete
21 experimental data
22 (what we measured)
23 physical interactions
24 Long Yeast two-hybrid Bait Prey Nuclear Hydrophobic Protein-fragment complementation assay Transmembrane Disordered Cytosolic TAP tag Co-purified proteins Bait Prey Reconstituted enzyme Basic Abundant Bait Tandem affinity purification Jensen & Bork, Science, 2008
25 genetic interactions
26 Beyer et al., Nature Reviews Genetics, 2007
27 gene coexpression
28 microarrays
29 RNAseq
30 Exercise 2 (Continue from where exercise 1 ended) Which types of evidence support the interaction between INSR and IRS1? Click on the interaction to view the popup, which has buttons linking to full details Which types of experimental assays support the INSR IRS1 interaction?
31 predictions
32 (what we infer)
33 genomic context
34 evolution
35 gene fusion
36 Korbel et al., Nature Biotechnology, 2004
37 gene neighborhood
38 Korbel et al., Nature Biotechnology, 2004
39 phylogenetic profiles
40 Korbel et al., Nature Biotechnology, 2004
41 a real example
42
43
44
45 Cell Cellulosomes Cellulose
46 complications
47 many databases
48 different formats
49 different identifiers
50 variable quality
51 not comparable
52 not same species
53 hard work
54 parsers
55 mapping files
56 quality scores
57 affinity purification
58 von Mering et al., Nucleic Acids Research, 2005
59 phylogenetic profiles
60
61 score calibration
62 gold standard
63 von Mering et al., Nucleic Acids Research, 2005
64 implicit weighting by quality
65 common scale
66 homology-based transfer
67 orthologous groups
68 Franceschini et al., Nucleic Acids Research, 2013
69 missing most of the data
70 Exercise 3 (Continue from where exercise 2 ended) Change the network to the confidence view Change the confidence cutoff to 0.9; any changes in proteins or interactions shown? Turn off all but experiments; what changes? Increase the number of interactors shown to 50; how many proteins do you get? Why?
71 text mining
72 >10 km
73 too much to read
74 exponential growth
75 ~40 seconds per paper
76 computer
77 as smart as a dog
78 teach it specific tricks
79
80
81 named entity recognition
82 comprehensive lexicon
83 cyclin dependent kinase 1
84 CDC2
85 orthographic variation
86 expansion rules
87 prefixes and suffixes
88 CDC2
89 hcdc2
90 flexible matching
91 spaces and hyphens
92 cyclin dependent kinase 1
93 cyclin-dependent kinase 1
94 black list
95 SDS
96 information extraction
97 co-mentioning
98 counting
99 within documents
100 within paragraphs
101 within sentences
102 scoring scheme
103
104
105 score calibration
106 NLP Natural Language Processing
107 part-of-speech tagging
108 what you learned in school pronoun pronoun verb preposition noun
109 semantic tagging
110 grammatical analysis
111 Gene and protein names Cue words for entity recognition Verbs for relation extraction [ nxexpr The expression of" [ nxgene the cytochrome genes" [ nxpg CYC1 and CYC7]]]" is controlled by" [ nxpg HAP1] Saric et al., Proceedings of ACL, 2004
112 type and direction
113 complex sentences
114 anaphoric references
115 it
116 summary
117 association networks
118 heterogeneous data
119 common identifiers
120 quality scores
121 protein networks
122 string-db.org Szklarczyk et al., Nucleic Acids Research, 2015
123 STITCH
124 chemical networks
125 stitch-db.org Kuhn et al., Nucleic Acids Research, 2014
126 COMPARTMENTS
127 subcellular localization
128 compartments.jensenlab.org Binder et al., Database, 2014
129 TISSUES
130 tissue expression
131 tissues.jensenlab.org Santos et al., PeerJ, 2015
132 DISEASES
133 disease associations
134 diseases.jensenlab.org Frankild et al., Methods, 2015
135 Exercise 4 Open Look up tissue associations for insulin (INS) Open Search for insulin receptor (INSR) What is the strongest associated disease? Inspect the underlying text-mining evidence
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