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How will we efficiently understand the interactions of ~20,000 genes, with ~200 million potential pairwise interactions? Minimally, we need to use the information that exists 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 Jan 2008: >200,000 relevant papers Prioritizing high resolution genetic interaction tests by knowledge mining 1 Full text information retrieval Hans-Michael Muller, Arun Rangarajan, Tracy Teal, Kimberly Van Auken, Juancarlos Chan QuickTi me™ and a T IFF (Uncom pressed) decom pressor are needed to see t his pict ure. QuickT ime ™an d a TIFF ( Uncomp res sed) deco mpre ssor ar e need ed to see this pictur e. 2 Predicting Gene Interactions from information available in public databases Weiwei Zhong Textpresso Literature Search Engine www.textpresso.org 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 Language Processing. Natural Can we do better than PubMed and Google Scholar? Full Text Sentence PubMed (-) - Google Scholar + - Textpresso + + Ontology MeSH Taxonomy Gene Ontology Customized Neuroscience Information Framework Categories are “bags of words” FOXO HOXA1 GENE pax2 PKD1 PATHWAY precursor upstream cascade descendants denticle Reporter Genes GFP, EGFP, YFP, lacZ, CFP, Green Fluorescent Protein, reporter gene, dsRed, mCherry wing Drosophila anatomy MP2 neuron Individual sentences in full text are marked up with Categories TEXTPRESSO CATEGORIES regulation gene process gene life stage anatomy egl-38 regulates lin-3 transcription in vulF in L3 larvae ARTICLE TEXT Automatically mark up the whole corpus of papers with terms of categories, and index for rapid searching What Arabidopsis genes are expressed in the meristem based on reporter genes? www.textpresso.org/arabidopsis 14,930 A.t. papers Is a nicotinic receptor associated with Drugs of Abuse other than nicotine? www.textpresso.org/neuroscience QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. 15,786 papers The problem with clever fly names Gene name forager ascute wee Washed eye abbreviation for as we We use italics from PDF ~70% Train system to recognize gene names by context ~85% Michael Müller, Arun Rangarajan What reporter genes have been used with Drosophila genes to study human disease? www.textpresso.org/fly 20,099 full-text fly papers QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. 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 Prioritizing high resolution genetic interaction tests by knowledge mining 1 Full text information retrieval Hans-Michael Muller, Arun Rangarajan, Tracy Teal, Kimberly Van Auken, Juancarlos Chan QuickTi me™ and a T IFF (Uncom pressed) decom pressor are needed to see t his pict ure. QuickT ime ™an d a TIFF ( Uncomp res sed) deco mpre ssor ar e need ed to see this pictur e. 2 Predicting Gene Interactions from information available in public databases Weiwei Zhong Training Set 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 Algorithm fly orthologs interaction GO expression phenotype microarray fly score worm gene pair GO expression phenotype microarray worm score yeast orthologs interaction GO localization phenotype microarray yeast score Ortholog mapping Scoring Score integration total score Scoring and score integration likelihood ratio p(v | pos) L p(v | neg ) 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 C. elegans expression sum the logs of the L’s 7 6 n score ln Li 5 L 4 i1 3 n: number of predictors Li: likelihood ratio of each predictor 2 1 0 0 5 10 15 20 25 term usage (% of annotated genes associated with the term) QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. lin-3 let-23 sem-5 sos-1 gap-1 let-60 lin-45 ksr-1 mek-2 lip-1 mpk-1 v1.4 & v1.6 v1.6 Testing let-60 ras Interactors 87 genes have score >0.9; 17 confirmed from literature Inactivating genes on a gain-of-function (gf) let-60 mutant by RNAi Assay vulva precursor cell (VPC) induction N2 not Multivulva let-60(gf) strong Multivulva let-60(gf); tax-6(RNAi) weak Multivulva WT% Muv% average 100 0 3.0 let-60(gf) 0 100 4.3 let-60(gf); tax-6(RNAi) 40 60 3.4 N2 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 B0353.1 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 B0035.16 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 B0491.1 C06A8.6 VPC induction index let-60(gf) VPC Induction Under Various RNAi 6 5 Score > 0.9 p< 0.01 Score < 0.6 p< 0.05 4 3 2 1 0 12 hits (p<0.05) in 49 genes; 1 hit in 26 randomly selected genes Combined with literature, 29/66 (44%) predictions confirmed let-60 ras interactors (suppressors) tax-6 calcineurin csn-5 COP-9 signalosome qua-1 hedgehog-related protein C01G8.9 SWI/SNF-related (eyelid) C05D10.3 ABC transporter (white) pfa-3 profilin nhr-4 transcription factor C. elegans Interactions Input 4,726 known interactions among 2,713 genes Predict additional 18,863 for total of 23,589 interactions among 4,408 genes QuickTime™ and a decompressor are needed to see this picture. for Drosophila QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. QuickTime™ and a TIFF (LZW) decompressor are needed to see this picture. D. melanogaster interactions Input 4,180 known interactions among 1,262 genes, Predict 13,126 for 17,306 interactions among 6,044 genes QuickTime™ and a decompressor are needed to see this picture. Automated, Quantitative Phenotyping locomotion morphology generative graphics plate demographics (Weiwei Zhong) sexual behavior Chris Cronin: movement analysis BMC-Genetics 2005 E. Fontaine, A. Whittaker, Joel Burdick Prioritizing high resolution genetic interaction tests by knowledge mining 1 Full text information retrieval Hans-Michael Muller, Arun Rangarajan, Tracy Teal, Kimberly Van Auken, Juancarlos Chan QuickTi me™ and a T IFF (Uncom pressed) decom pressor are needed to see t his pict ure. QuickT ime ™an d a TIFF ( Uncomp res sed) deco mpre ssor ar e need ed to see this pictur e. 2 Predicting Gene Interactions from information available in public databases Weiwei Zhong