Data and text mining applied to the computational study of protein interaction networks
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1 Data and text mining applied to the computational study of protein interaction networks Miguel Andrade Faculty of Biology, Johannes Gutenberg University Institute of Molecular Biology Mainz, Germany
2 PubMed
3 PubMed
4 PubMed
5 MedlineRanker Jean-Fred Fontaine Noun weights: linear naive Bayesian classifier Split-Laplace smoothing scheme to counteract class skew. Ranks MEDLINE according to a topic Fontaine et al. (2009) Nucleic Acids Research
6 MedlineRanker Jean-Fred Fontaine Ranks MEDLINE according to a topic Fontaine et al. (2009) Nucleic Acids Research
7 MedlineRanker Jean-Fred Fontaine Abstract scores: sum of the weights of its nouns. P-values: proportion of abstracts with a higher score within 10,000 recent abstracts.
8 Génie Ranks a set of genes from a whole genome according to a topic Human Fontaine et al. (2011) Nucleic Acids Research
9 Génie P-values for genes: one-sided Fisher s exact test comparing the number of selected abstracts to a set of 10,000 randomly selected abstracts (P<0.01 by default). FDR: Benjamini and Hochberg
10 Alkemio Ranks chemicals according to a topic Fontaine et al. (2014) Nucleic Acids Research
11 Alkemio
12 PESCADOR Adriano Barbosa Extract interactions and filter by concepts Barbosa-Silva et al. (2010) BMC Bioinformatics Barbosa-Silva et al. (2011) BMC Bioinformatics
13 PESCADOR
14 Co-occurrences types PESCADOR Type 1 Term + [Biointeraction] + Term Type 2 [Biointeraction] +Term + Term + [Biointeraction] Type 3 Term + Term Type 4 co-occurrence in abstract
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18 HIPPIE Martin Schaefer Schaefer et al (2012) PLoS One Schaefer et al (2013) PLoS Comp Biol
19 HIPPIE Human Integrated Protein- Protein Interaction reference Expert knowledge on experimental reliability Extraction of: Publications Experimental techniques Orthologous interacting protein pairs Databases Automated retrieval of experimental human PPIs and associated meta data Scoring function: Σ(#studies; #techniques; #species) HIPPIE (>90,000 human, experimental PPIs) 19
20 Scoring function: Σ(#studies; #orthologs; #techniques) Score = ws ss + wo so + wt st with ws +wo + wt = 1 s i (0) = 0 and s i ( ) = 1 Optimization: IntAct dataset (28,073 interactions). 109 studies with >10 interactions and more than two PPIs found in multiple studies.. Grid search in the range [0, 3] for the aiand in the range [0, 1] for the wi (step width of 0.1 for both) as= 2.3, ao =1.6, at = 0.2, ws= 0.6, wo = 0.1, wt = 0.3 max(f) = 1.023
21 HIPPIE web frontend
22 HIPPIE web frontend
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28 Function of Martin Schaefer Human Dog Mouse Opossum Chicken Frog Zebrafish Trout Fugu Stickleback Lancelet Capitella Limpet Nematostella Trichoplax Ciona intestinalis Ciona savignyi D. melanogaster D. mojavensis D. sechellia D. erecta D. yakuba D. grimshawi D. pseudoobscura D. persimilis D. ananassae D. willistoni D. virilis in Huntingtin Schaefer et al (2012) Nucleic Acids Res.
29 partners human TFs long non
30 human yeast partners TFs long non TFs long non
31 86 human proteins N-terminal 13 proteins with near polyp 109 regions 54 overlap/near coiled coil coiled coil C-terminal polyp 12 C-term
32 86 human proteins N-terminal 13 proteins with near polyp 40 human /coiled-coil proteins (no polyp) coiled coil 36 N-term C-terminal polyp 12 C-term
33 86 human proteins interacting proteins 49 interactions with another protein (p-value = ) coiled coil coiled coil polyp polyp P-value: simulation, comparison to random set with similar or higher degree
34 86 human proteins Non- interacting proteins coiled coil coiled coil polyp Enrichment (p-value < 2.2e-16) P-value: Fisher s exact test
35 protein unbound N-terminal coiled coil disordered polyp C-terminal
36 protein unbound N-terminal protein protein X coiled coil disordered polyp coiled coil polyp C-terminal
37 protein unbound N-terminal protein bound protein X coiled coil disordered polyp coiled coil polyp C-terminal
38 ATXN1Q82 NT is toxic ATXN1Q82 NT aggregates Spyros Petrakis Petrakis et al. (2012) PLoS Genetics
39 interactors that change ATXN1Q82 NT toxicity
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41 Normal protein CC disordered CC partner
42 Normal protein CC disordered CC partner
43 Normal protein Toxic protein CC disordered CC partner alpha-helix
44 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix
45 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix beta-aggregates
46 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix beta-aggregates
47 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix increased beta-aggregates non-cc partner
48 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix increased beta-aggregates non-cc partner
49 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix increased beta-aggregates non-cc partner
50 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix increased beta-aggregates non-cc partner
51 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix increased beta-aggregates non-cc partner
52 Normal protein Toxic protein CC disordered beta-aggregates CC partner alpha-helix increased beta-aggregates non-cc partner
53 HTT network 509 proteins 1319 interactions Brain network 88 proteins 113 interactions Erich Wanker Caudate nucleus network 66 proteins 84 interactions Matthias Futschik HD dysreg 14 proteins 13 interactions David Fournier Stroedicke et al. (2015) Genome Research
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55 CRMP1 MED15
56 Computational Biology and Data Mining group Enrique Muro Jean-Fred Fontaine Katerina Taṡkova Sweta Talyan Marie Gebhardt (MDC) Jonas Ibn-Salem Desirée Kaufmann Pablo Mier Gregorio Alanis
57 Thank you!
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1 Supplementary Figures
Supplementary Figures D. simulans (dsim) D. sechellia (dsec) D. melanogaster (dmel) S. cerevisiae (scer) S. paradoxus (spar) S. mikatae (smik) S. bayanus (sbay) S. castellii (scas) C. glabrata (cgla) K.
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