Protein association networks with STRING

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Protein association networks with STRING Lars Juhl Jensen

Transcript of Protein association networks with STRING

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Protein associationnetworks with STRING

Lars Juhl Jensen

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interaction networks

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association networks

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guilt by association

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protein networks

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STRING

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9.6 million proteins

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common foundation

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Exercise 1Go to http://string-db.org/Query for human insulin receptor (INSR) using the search by name functionalityMake sure you are in evidence view(check the buttons below the network)Why are there multiple lines connecting the same to two proteins?

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curated knowledge

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(what we know)

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protein complexes

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3D structures

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pathways

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metabolic pathways

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Letunic & Bork, Trends in Biochemical Sciences, 2008

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signaling pathways

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very incomplete

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experimental data

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(what we measured)

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physical interactions

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Jensen & Bork, Science, 2008

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genetic interactions

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Beyer et al., Nature Reviews Genetics, 2007

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gene coexpression

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microarrays

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RNAseq

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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 detailsWhich types of experimental assays support the INSR–IRS1 interaction?

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predictions

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(what we infer)

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genomic context

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evolution

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gene fusion

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Korbel et al., Nature Biotechnology, 2004

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gene neighborhood

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Korbel et al., Nature Biotechnology, 2004

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phylogenetic profiles

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Korbel et al., Nature Biotechnology, 2004

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a real example

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Cell

Cellulosomes

Cellulose

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complications

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many databases

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different formats

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different identifiers

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variable quality

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not comparable

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not same species

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hard work

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parsers

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mapping files

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quality scores

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affinity purification

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von Mering et al., Nucleic Acids Research, 2005

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phylogenetic profiles

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score calibration

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gold standard

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von Mering et al., Nucleic Acids Research, 2005

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implicit weighting by quality

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common scale

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homology-based transfer

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orthologous groups

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Franceschini et al., Nucleic Acids Research, 2013

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missing most of the data

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Exercise 3(Continue from where exercise 2 ended)Change the network to the confidence viewChange 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?

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text mining

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>10 km

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too much to read

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exponential growth

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~40 seconds per paper

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named entity recognition

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comprehensive lexicon

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cyclin dependent kinase 1

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CDC2

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orthographic variation

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prefixes and suffixes

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CDC2

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hCdc2

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“black list”

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SDS

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information extraction

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co-mentioning

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counting

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within documents

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within paragraphs

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within sentences

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scoring scheme

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score calibration

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NLPNatural Language Processing

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part-of-speech tagging

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what you learned in schoolpronoun pronoun verb preposition noun

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semantic tagging

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grammatical analysis

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Gene and protein namesCue words for entity recognitionVerbs 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

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type and direction

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complex sentences

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anaphoric references

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it

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related resources

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association networks

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heterogeneous data

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common identifiers

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quality scores

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protein networks

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Szklarczyk et al., Nucleic Acids Research, 2015string-db.org

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STITCH

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chemical networks

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Kuhn et al., Nucleic Acids Research, 2014stitch-db.org

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COMPARTMENTS

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subcellular localization

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Binder et al., Database, 2014compartments.jensenlab.org

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TISSUES

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tissue expression

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tissues.jensenlab.org Santos et al., PeerJ, 2015

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DISEASES

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disease associations

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diseases.jensenlab.org Frankild et al., Methods, 2015

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Exercise 4Open http://tissues.jensenlab.orgLook up tissue associations for insulin (INS)Open http://diseases.jensenlab.orgSearch for insulin receptor (INSR)What is the strongest associated disease?Inspect the underlying text-mining evidence