1 / 17

Lab

Lab. Bio-Trac 40 (Protein Bioinformatics) October 8, 2009 Zhang-Zhi Hu, M.D. Associate Professor Department of Oncology Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center. http://www.geneontology.org/. GO search and display tool. Leaf node.

zita
Download Presentation

Lab

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lab Bio-Trac 40 (Protein Bioinformatics) October 8, 2009 Zhang-Zhi Hu, M.D. Associate Professor Department of Oncology Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center

  2. http://www.geneontology.org/

  3. GO search and display tool Leaf node GO term (GO:0006366): mRNA transcription from RNA polymerase II promoter

  4. Human p53 – GO annotation (UniProtKB:P04637) GO:0006289:nucleotide-excision repair [PMID:7663514; evidence:IMP] http://pir.georgetown.edu/cgi-bin/ipcEntry?id=P04637

  5. GO annotation of gene products • Science basis of the GO: trained experts use the experimental observations from literature to associate GO terms with gene products (to annotate the entities represented in the gene/protein databases) • Enabling data integration across databases and making them available to semantic search http://www.geneontology.org/GO.current.annotations.shtml Human, mouse, plant, worm, yeast …

  6. GO Slim http://www.geneontology.org/GO.slims.shtml (http://www.geneontology.org/)

  7. Functional profiling ID Mapping Batch gene/protein retrieval and profiling Enter ID, gi # Information matrix http://pir.georgetown.edu/pirwww/search/idmapping.shtml

  8. Entrez Gene list http://pir.georgetown.edu/pirwww/workshop/entrez_gene_list ID mapping http://pir.georgetown.edu/pirwww/search/idmapping.shtml http://www.uniprot.org/ Batch retrieval http://pir.georgetown.edu/pirwww/search/batch.shtml http://www.uniprot.org/ UniProt Accession/ID

  9. Example on ERa correlated genes profiles in NCI60 cancer cell lines Negative correlation (-0.38 to -0.72): 8540 55862 6197 57001 79780 2119 10643 301 1075 10020 2950 10950 161742 56913 2597 2182 84002 55529 2876 30011 4082 3628 11047 3945 1121 136 27350 6566 64114 10253 57102 66005 23708 10280 51375 57140 5621 4212 274 23318 51665 7048 4691 136895 7076 51202 57181 79650 79971 9334 26031 55432 51279 135112 11132 29934 2869 9859 10494 10487 11252 7804 7167 5216 817 1503 2135 50813 3801 5915 23645 2118 6711 1841 4735 123920 83879 112970 126282 6386 11097 55624 5876 55869 7171 64175 805 4904 24137 231 8634 10908 10096 200316 8078 2787 2534 25798 10477 1716 ER positive correlation (NCI60): >0.53 2099 199920 1826 163589 25833 3249 80129 114770 222 5241 5618 246 27202 2001 202333 149428 3263 9687 51705 165679 25803 11254 27232 54714 5630 29760 123872 8608 284570 29993 10877 150000 154313 93233 9543 124975 54014 133874 57535 163720 26154 7499 220047 91181 283987 146779 83657 57419 6506 158067 197257 56155 400500 50604 5304 64218 166979 6549 375775 257177 5733 85320 94160 643037 83659 2925 6013 85004 79624 27151 157739 55259 51764 90506 252884 284485 51380 6478 140628 56914 148327 57127 11248 9073 55503 866 1747 203328 146562 128153 83715 55892 1113 2625 10023 11189 9869 863 11281 4477 ER positive correlation (NCI60): >0.53 Entrez Gene ID ER negative correlation : (-0.38 to -0.72) ID Mapping GO Profiling Entrez Gene ID

  10. KEGG Metabolic & Regulatory Pathways • KEGG is a suite of databases and associated software, integrating our current knowledge on molecular interaction networks, the information of genes and proteins, and of chemical compounds and reactions. (http://www.genome.ad.jp/kegg/pathway.html) Transforming Growth Factor (TGF) beta signaling

  11. BioCarta Cellular Pathways (http://www.biocarta.com/index.asp) Transforming Growth Factor (TGF) beta signaling [Homo sapiens]

  12. Transforming Growth Factor (TGF) beta signaling [Homo sapiens] (http://reactome.org/cgi-bin/eventbrowser?DB=gk_current&FOCUS_SPECIES=Homo%20sapiens&ID=170834&) Reactome: events and objects (including modified forms and complex) Event ->REACT_6879.1: Activated type I receptor phosphorylates R-SMAD directly [Homo sapiens] Object -> REACT_7364.1: Phospho-R-SMAD [cytosol] Event -> REACT_6760.1: Phospho-R-SMAD forms a complex with CO-SMAD [Homo sapiens] Object -> REACT_7344.1: Phospho-R-SMAD:CO-SMAD complex [cytosol] Event -> REACT_6726.1: The phospho-R-SMAD:CO-SMAD transfers to the nucleus Object -> REACT_7382.2: Phospho-R-SMAD:CO-SMAD complex [nucleoplasm] ……

  13. PID Transforming Growth Factor beta signaling

  14. Transforming Growth Factor (TGF) beta signaling PID Reactome ~26 proteins in PID are not defined in Reactome, while only 2 in Reactome not defined in PID

  15. iProXpress: Integrative analysis of proteomic and gene expression data Data MS spectrum Peptide ident. Protein ident. http://pir.georgetown.edu/iproxpress/ Information Function Pathway Family Categorize Statistics Association Knowledge

  16. Organelle proteome data sets iProXpress– Pathway Profiling ER Mit • Protein information matrix: extensive annotations including protein name, family classification, function, protein-protein interaction, pathway… • Functional profiling: iterative categorization, sorting, cross-dataset comparison, coupled with manual examination. Mit ER KEGG pathway

  17. DNA synthesis DNA repair dGTP X GTP dGDPGDP ATP X dATP ADPdADP Ribonucleoside diphosphate reductase subunit M2 (RRM2) 1.17.4.1 1.17.4.1 http://www.genome.jp/kegg-bin/show_pathway?hsa00230+6241 Purine metabolic pathway

More Related