bi lecture 17 integration of genetic interaction data

Transcription

1 bi lecture 17 integration of genetic interaction data

2 Genes interact to specify phenotype 20,000 genes 200,000,000 potential binary interactions (2 locus physiological epistasis)

3 How will we efficiently understand the interactions of ~20,000 genes, with ~200 million potential pairwise interactions? We first to need to use the information that exists

4 We can prioritize high resolution genetic interaction tests by knowledge mining 1 Full text information retrieval Hans-Michael Müller, Yuling Li, James Done, Kimberly Van Auken, Juancarlos Chan, Eimear Kenny,... 2 Predicting and Measuring Gene Interactions Weiwei Zhong

5 June 1979: 2 relevant papers S. Brenner (Genetics 1974) The genetics of Caenorhabditis elegans J. Sulston & R. Horvitz (Developmental Biology 1977) Post-embryonic cell lineages of the nematode, Caenorhabditis elegans June 2013: 13,000 C. elegans papers >400,000 relevant papers cancer: 2 million papers

6 Textpresso Literature Search Engine Scientists spend more time skimming for information than reading papers. Much information are details hidden in the full text, and are neither in the abstract nor captured in MeSH terms. We designed Textpresso to do automated skimming for researchers and database curators. The output can be used for more sophisticated Natural Language Processing.

7 Categories are bags of words FOXO HOXA1 pax2 PKD1 GENE PATHWAY precursor upstream cascade descendants Reporter Genes GFP, EGFP, YFP, lacz, CFP, Green Fluorescent Protein, reporter gene, dsred, mcherry wing denticle Drosophila anatomy MP2 neuron Michael Müller

8 Individual sentences in full text are marked up with Categories TEXTPRESSO CATEGORIES gene regulation process life stage gene anatomy egl-38 regulates lin-3 transcription in vulf in L3 larvae ARTICLE TEXT Software automatically marks up the whole corpus of papers with terms of categories, and index for rapid searching Michael Müller

9 What Arabidopsis genes are expressed in the meristem based on reporter genes? A.t. papers keyword: meristem categories: {Arabidopsis Genes} AND {Reporter Genes} Tracy Teal

10 Different than PubMed & Google Scholar: Sentence level analysis AND Ontology Full Text Sentence Ontology PubMed (-) - MeSH Taxonomy Google Scholar Textpresso + + Gene Ontology Customized Neuroscience Information Framework

11 Is a nicotinic receptor associated with Drugs of Abuse other than nicotine? 15,786 papers (now 101,000) keyword: nicotinic receptor NOT nicotine categories: {Prescription Drugs of Abuse}

12 Solving the problem with clever fly names Gene name forager ascute wee Washed eye abbreviation for as we We Very common English words! use italics from PDF ~70% Train system to recognize gene names by context ~85% Michael Müller, Arun Rangarajan

13 What reporter genes have been used with Drosophila genes to study human disease? full-text fly papers keyword: none categories: {Drosophila Gene} AND {Reporter Gene} AND {disease-h. sapiens}

14 Database curation: e.g. Gene-Gene Interactions Find all sentences that contain 2 gene names and 1 association or regulation word: 26,000 sentences out of 4,400 articles simple interface to check off sentences 100 sentences per hour output into database:

15 Prioritizing high resolution genetic interaction tests by knowledge mining 1 Full text information retrieval Hans-Michael Muller, Arun Rangarajan, Ruihua Fang, Tracy Teal, Kimberly Van Auken, Juancarlos Chan 2 Predicting and Measuring Gene Interactions Weiwei Zhong

16 Known information serves as a training set for a statistical model Training set 4775 Positive Interactions Genetic, Literature curation (1909) Yeast two-hybrid screen (2933) 3296 Negative Genetic Interactions cis doubles in genetic mapping Benchmark 5515 Positives: KEGG database 5000 Negatives: Randomly selected

17 Experiments that establish relationships between genes, proteins, cells and functions Zhong, W. et al. Development 2007;134:

18 Information about gene pairs that make gene interactions likely are combined in a score interaction fly orthologs GO expression phenotype microarray GO fly score worm gene pair expression phenotype microarray interaction worm score total score GO yeast orthologs localization phenotype microarray yeast score Ortholog mapping Scoring Score integration

19 Three methods of assigning orthology relationships

20 In Positive Training set In Negative Training set Interact in fly Do not interact in fly L = P(ν pos) 20/25 = = 4 P(ν neg) 10/50

21 Predictor L Sc physical interaction 5.8 Sc genetic interaction 14.5 Dm physical interaction 55 Dm genetic interaction 298

22 C. elegans expression L term usage (% of annotated genes associated with the term)

23 Scoring and score integration likelihood ratio p(v pos): probabilities of the predictor having value v if two genes interact p(v neg): probabilities of the predictor having value v if two genes do not interact L C. elegans expression sum the ln s of the L s score = lnl i n: number of predictors L i : likelihood ratio of each predictor n i= term usage (% of annotated genes associated with the term)

24 Score Integration-another way Logistic regression p: interacting probability n: number of predictors a i, c: constants L i : likelihood ratio of each predictor Performance with training set Performance with benchmark Accuracy: % of correct predictions Sensitivity: predicted positives / total positives

25 40% of predictions forlet-60 RAS are confirmed 87 genes have score >0.9; 17 confirmed from literature Inactivating genes on gain-of-function (gf) let-60 mutant by RNAi Assay vulva precursor cell (VPC) induction N2 let-60(gf) let-60(gf); tax-6(rnai) not Multivulva strong Multivulva weak Multivulva WT% Muv% average N let-60(gf) let-60(gf); tax-6(rnai)

26 let-60(gf) VPC Induction with various RNAi VPC induction index p< 0.01 p< 0.05 score from logistic regression 0 control tax-6 csn-5 qua-1 C01G8.9 pfn-3 nhr-41 C05D10.3 Y48G10A.3 dlg-1 tag-22 grd-11 W03F11.6 mig-15 taf-6.1 taf-1 lin-32 unc-55 Y59A8B.23 Y48G10A.3 wrt-8 sqv-7 wrt-4 evl-20 C07H6.3 glp-1 unc-59 grd-1 wrt-7 hog-1 cdc-25.3 che-1 mom-5 Y53C12C.1 rnt-1 cki-1 let-413 taf-4 tig-2 tag-117 psa-4 T24H10.7 lin-48 src-2 B R05G6.10 T18D3.7 grd-2 ZC84.3 cdc-42 cki-2 F59A2.4 K10H10.1 C04C3.3 F34D6.4 F34D10.2 C25H3.4 H27A23.1 Y54G11A.1 B M03C11.4 C41C4.8 M01F1.5 ZK945.8 ZK643.2 F26E4.12 C16A3.7 C53A3.2 H14N18.4 W02D3.6 F08A8.4 C37H5.3 F28H6.3 R10E11.3 R04B5.5 B C06A8.6 Score > 0.9 Score < hits (p<0.05) in 49 genes; 1 hit in 26 randomly selected genes Combined with literature, 29/66 (44%) predictions confirmed

27 Newly identified let-60 ras interactors (suppressors) tax-6 csn-5 qua-1 C01G8.9 C05D10.3 pfa-3 nhr-4 calcineurin COP-9 signalosome hedgehog-related protein SWI/SNF-related (eyelid) ABC transporter (white) profilin transcription factor Zhong & Sternberg (Science, 2006)

28 Interactions for EGF-receptor -Ras - MAPK Pathway lin-3 let-23 v1.4 worms, fly, yeast Has FALSE NEGATIVES gap-1 ksr-1 lip-1 sem-5 sos-1 let-60 lin-45 mek-2 mpk-1 MORE DATA, ORTHOLOGS v1.6 worms, fly, yeast mouse, human v1.4 & v1.6 v1.6

29

30 30

31 31

32 32

33 33

34 34

35 human genes 35

36 C. elegans Interactions (version 1.6) Input 4,726 known interactions among 2,713 genes Predict additional 18,863 for total of 23,589 interactions among 4,408 genes

37 for Drosophila

38 D. melanogaster interactions (v. 1.6) Input 4,180 known interactions among 1,262 genes, Predict 13,126 for 17,306 interactions among 6,044 genes

39 wormnet 39

40 wormnet 15,000 genes: 112,500,000 potential interactions 100K/100M=0.1% Search reports would be based on WormBase WS

41 CE-CC! Co-citation of worm gene CE-CX! Co-expression among worm genes CE-GN! Gene neighbourhoods of bacterial and archaeal orthologs of worm genes CE-GT! Worm genetic interactions CE-LC! Literature curated worm protein physical interactions CE-PG! Co-inheritance of bacterial and archaeal orthologs of worm genes CE-YH! High-throughput yeast 2-hybrid assays among worm genes DM-PI! Fly protein physical interactions HS-CC! Co-citation of human genes HS-CX! Co-expression among human genes HS-DC! Co-occurrence of domains among human proteins HS-LC! Literature curated human protein physical interactions HS-MS! human protein complexes from affinity purification/mass spectrometry HS-YH! High-throughput yeast 2-hybrid assays among human genes SC-CC! Co-citation of yeast genes SC-CX! Co-expression among yeast genes SC-DC! Co-occurrence of domains among yeast proteins SC-GT! Yeast genetic interactions SC-LC! Literature curated yeast protein physical interactions SC-MS! Yeast protein complexes from affinity purification/mass spectrometry SC-TS! Yeast protein interactions inferred from tertiary structures of complexes

42 Experiments that establish relationships between genes, proteins, cells and functions Zhong, W. et al. Development 2007;134: