STRING: Protein association networks - GitHub Pages · Y e a s t t w o-hy bri d T a n d e m a f f i...

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STRING: Protein association networks Lars Juhl Jensen

Transcript of STRING: Protein association networks - GitHub Pages · Y e a s t t w o-hy bri d T a n d e m a f f i...

Page 1: STRING: Protein association networks - GitHub Pages · Y e a s t t w o-hy bri d T a n d e m a f f i n i t y p u r i f i c a t i o n P r o t e i n-f r a g m e n t c o m p l e m n t

STRING: Protein association networks

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 1 Go to http://string-db.org/

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?

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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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Yeast two-hybrid

Tande

m affin

ity p

urifi

catio

nProtein-fragm

ent complem

entation assay

81003P_borkjensen.eps19 by 19

Nuclear

Hydrophobic

Transmembrane

Dis

orde

red

BasicAbundant

Cytosolic

Long

0.2

0.1

Bait

Bait

Reconstitutedenzyme

Co-purifiedproteins

Prey

Prey

Bait

TAP tag

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 details

Which 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 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?

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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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computer

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as smart as a dog

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teach it specific tricks

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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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expansion rules

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prefixes and suffixes

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CDC2

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hCdc2

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flexible matching

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spaces and hyphens

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cyclin dependent kinase 1

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cyclin-dependent kinase 1

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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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NLP Natural Language Processing

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part-of-speech tagging

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what you learned in school pronoun pronoun verb preposition noun

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semantic tagging

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grammatical analysis

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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

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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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summary

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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, 2015 string-db.org

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STITCH

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chemical networks

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

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COMPARTMENTS

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subcellular localization

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Binder et al., Database, 2014 compartments.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 4 Open http://tissues.jensenlab.org

Look up tissue associations for insulin (INS) Open http://diseases.jensenlab.org Search for insulin receptor (INSR) What is the strongest associated disease?

Inspect the underlying text-mining evidence


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