1. Lecture Outline Introduction Data mining sources: –GO, InterPro, KEGG, UniProt Tools to do the data mining: –FatiGO –FatiWISE

2. Data mining Microarray results Microarray experiments are done to answer a biological question Results generate sets of numbers (intensities) which are then clustered to find data points of interest These themselves don’t necessarily answer the research question, these need to be converted to biological information first

3. Purpose of data mining Validation of results –understanding why these genes are grouped together Using biological information to find significant associations of biological terms to sets of genes Understanding of the roles of the genes at the molecular level

4. Data mining (1) -AB02387 -SB07593 -AA00498 -AC008742 -AB083121 Add gene identifiers

5. Data mining (2) -AB02387 -SB07593 -AA00498 -AC008742 -AB083121 -RNA polymerase -Glycosyl hydrolase -Phosphofructokinase -Transcripiton factor -Glucose transporter Add gene descriptions

6. Data mining (3) -AB02387 -SB07593 -AA00498 -AC008742 -AB083121 -RNA polymerase -Glycosyl hydrolase -Phosphofructokinase -Transcripiton factor -Glucose transporter -GO0003456 -GO0006783 -GO0142291 -GO0054198 -GO0000234 Add GO terms

7. Data mining (4) -AB02387 -SB07593 -AA00498 -AC008742 -AB083121 -RNA polymerase -Glycosyl hydrolase -Phosphofructokinase -Transcripiton factor -Glucose transporter -GO0003456 -GO0006783 -GO0142291 -GO0054198 -GO0000234 Add functional annotation

8. Data mining (5) -AB02387 -SB07593 -AA00498 -AC008742 -AB083121 Store results in database -RNA polymerase -Glycosyl hydrolase -Phosphofructokinase -Transcripiton factor -Glucose transporter -GO0003456 -GO0006783 -GO0142291 -GO0054198 -GO0000234 Map onto pathways

9. Sources of biological information Free text: e.g. Medline –Using text processing tools Curated repositories: e.g. GO, KEGG, UniProt, InterPro etc. –Using data mining –Using tools e.g. FatiGO and FatiWISE

10. Free text mining Advantages: –Vast amounts of data –Many associated terms for each gene Disadvantages: –Synonyms and acronyms –Context information –Irrelevant terms –Need to divide into entities and relationships to structure text

11. Example of problems The Sch9 protein kinase regulates Hsp90- dependent signal transduction activity in the budding yeast Saccharomyces cerevisiae. This interaction was suppressed by decreased signaling through the protein kinase A (PKA) signal transduction pathway. Text is unstructured –needs to be divided into entities and relationships

12. Example of problems The Sch9 protein kinase regulates Hsp90- dependent signal transduction activity in the budding yeast Saccharomyces cerevisiae. This interaction was suppressed by decreased signaling through the protein kinase A (PKA) signal transduction pathway. Protein Verb Pathway Acronym –could be used elsewhere for different gene Organism Some problems overcome using stats & better detection of entities and relationships Negative term used

13. Curated repositories These have reliable annotation Annotation is standardised They are usually well structured However, they usually have less annotation Examples: GenBank, GO (FatiGO), UniProt, InterPro, KEGG (FatiWISE)

14. Gene Ontology (GO) http://www.geneontology.org Many annotation systems are organism-specific or different levels of granularity GO introduced standard vocabulary first used for mouse, fly and yeast, but now generic An ontology is a formal specification of terms and relationships between them

15. GO Ontologies Molecular function: tasks performed by gene product –e.g. G-protein coupled receptor Biological process: broad biological goals accomplished by one or more gene products –e.g. G-protein signaling pathway Cellular component: part(s) of a cell of which a gene product is a component; includes extracellular environment of cells –e.g nucleus, membrane etc.

16. GO relationships “is-a” e.g. mitochondrial membrane is a membrane “part of” e.g. nuclear membrane is part of nucleus DAG structure

17. Current Mappings to GO Consortium mappings -MGD, SGD, RGD, FlyBase, TAIR GOA (Gene Ontology Anotation): Swiss-Prot keywords EC numbers InterPro entries Manual mappings Unigene Medline ID mappings, etc. FatiGO Evidence codes NB

18. GO Slim “Slimmed down” version of GO ontologies Selection of high level terms covering all or most biological functions processes and cell locations Many different GO Slim’s available with different depths and detail Used to make comparisons between annotated gene/protein sets easier (each gene may be mapped to different granularity)

19. Applications of GO slim

20. GO consortium page

21. UniProt annotation Protein sequence database from EMBL translations and direct sequencing Structured into specific fields e.g. description, comments, feature table, keywords Each field may have controlled vocabulary or specific syntax Swiss-Prot is well annotated, TrEMBL is not, and may have less structured text

22. Example Swiss- Prot entry Annotation

23. KEGG Kyoto Encyclopedia of Genes and Genomes –Molecular interaction networks in biological processes -PATHWAY database –Genes and proteins -GENES/SSDB/KO databases –Chemical compounds and reactions - COMPOUND/GLYCAN/REACTION databases Includes most organisms and info on orthologues

24. Example KEGG entry

25. InterPro Integrates protein signature databases e.g. Pfam, PROSITE, Prints etc. Classifies proteins into families and domains and lists all UniProt proteins belonging to each Provides annotation on the family/domain and links to 3D structure, GO, Enzyme Classification Used to functionally characterise a protein

26. Example InterPro entry

27. FatiGO Connecting microarray results with these biological data sources –answers questions e.g do my differentially expressed genes have different functions? FatiGO is used to extract relevant GO terms for a group of genes with respect to a set of reference genes (the rest) Can be used to list proportions of GO terms in a set of genes http://fatigo.bioinfo.cnio.es

28. FatiGO data sources Uses tables of correspondences between genes and their GO terms (human, mouse, Drosophila, yeast, worm and UniProt proteins –curated if possible) Uses genes from GenBank, UniProt (Swiss- Prot/TrEMBL), Ensembl etc. Problem in lack of standardisation of names –use EBI xrefs to link them, and for other databases they use their own gene IDs For GO associations they include GO evidence codes, e.g. IEA

29. Using the GO hierarchy Different levels in the GO hierarchy can be chosen, depending on specificity required FatiGO suggest using level 3 –questionable? Deeper you go (more specific) –fewer genes annotated to the terms Once level is set, for each gene FatiGO moves up hierarchy until set level is reached –increases no. of terms mapped to this level –easier to find relevance in different distributions of GO terms Repeated genes are counted once

30. How FatiGO works Given two sets of genes, and selected GO level Retrieves GO terms for each gene on correct level Applies Fisher’s exact test for 2x2 contingency tables for comparing 2 sets of genes (to get p-values) Extracts GO terms with significantly different distributions After correcting for multiple testing, provides adjusted p- values for 3 tests: –Step-down minP method (Westfall and Young) –FDR independent (Benjamini & Hochberg) –FDR arbitrary dependent (Benjamini & Yekutieli )

31. Testing sets of GO terms Gene set 1 Gene set 2 Transport 60% Regulation 20% Transport 20% Significantly higher distribution in 1 than 2 Same distribution 62 71 80 Set 1Set 2 Observed difference and possible stronger differences

32. Multiple testing P-value: is the probability, under the null hypothesis of obtaining the observed result or a more extreme result than one observed Testing multiple null hypotheses (one per GO term) that there is no difference in the frequency of terms in each set For 1 test, type I error rate (probability of rejecting a true null hypothesis) is 0.05, but for multiple tests this increases - Family wise error rate (probability that one or more of rejected nulls are true ) Multiple testing allows controlling of Family Wise Error Rate (FWER) and False discovery rate (FDR)

33. Step down min-P method Controls FWER Procedure with a test statistic equivalent to Fisher's exact test for 2x2 contingency tables No. of random permutations set at 10000 Examines how many of the permuted p-values are smaller than the one under consideration Adjusted p-value for hypothesis H is level of entire test set procedure at which H would be rejected, given values of all test statistics involved

34. Controlling False Discovery Rate Tends to be more liberal than controlling FWER Controlling expected no. of false rejections (Type 1 errors) among rejected hypotheses Consider the proportions of erroneous rejections to the total number of rejections. Average value of proportion = FDR FDR can be dependent on or independent of test statistics, FatiGO gives: adjusted p-value using the FDR method of Benjamini & Hochberg –control of FDR under independence adjusted p-value using the FDR method of Benjamini & Yekutieli –control of FDR under arbitrary dependent structures

35. Using FatiGO -Input Search for Unigene cluster ID, or specific gene IDs Input results from SotaTree or Pomelo Or input Excel or text file with list of gene or protein IDs, each on a new line Input reference set of genes Select GO ontology and level (inclusive) Select whether multiple test should include adjusted p- values for minP test

36. FatiGO interface (1)

37. FatiGO interface (2)

38. FatiGO output FatiGO returns four columns: the unadjusted p-value (p- value from Fisher’s exact test without adjusting for multiple comparisons) and adjusted p-values based on the three methods Results are ordered by increasing value of the adjusted p- value, facilitating the selection of GO terms with the most significant differences. P-value of 0.01-0.05 –some evidence, 0.01-0.001 –strong evidence and < 0.001 –very strong evidence against null

39. Query set Reference set FatiGO example output Unadjusted p-value FRD (indep) adjusted FDR (depend) adjusted

41. Link to AmiGO

42. Other features of FatiGO You can input a list of genes and extract the GO terms sorted by percentages You can use GO results as a way to find differentially expressed genes –see if after correcting for multiple testing, some GO terms are overrepresented (provides more resolution where p-value has no meaning)

43. Percentages of GO terms within a set of genes

44. FatiWISE Data mining to retrieve additional biological info on InterPro motifs, KEGG pathways and Swiss- Prot keywords Uses Fishers exact test for 2x2 contingency tables for comparing two sets of genes and finding significantly different distributions Corrects for multiple testing to get adjusted p-value Can get stats for one set of genes or compare 2 sets

45. FatiWISE input and output Data sources: KEGG, InterPro, UniProt Input: –one or two sets of genes –Selection of organism (for pathway) Output: –Unadjusted p-value –Step-down min P adjusted p-value –FDR (arbitrary dependent) adjusted p-value

46. FatiWISE interface

47. FatiWISE InterPro output

48. FatiWISE KEGG output

49. FatiWISE keyword output

50. Summary Data mining is used to bring the biology into results Curated data sources are the best for this, due to structure and controlled vocabulary FatiGO and FatiWISE are simple web tools enabling data mining on 1 or 2 sets of genes Exercises: http://cbio.uct.ac.za/courses/MicroDM/

51. Websites for Annotation Webgestalt: http://genereg.ornl.gov/webgestalt/login.php http://genereg.ornl.gov/webgestalt/login.php Fatigo: http://babelomics.bioinfo.cipf.es/http://babelomics.bioinfo.cipf.es/

52. Websites for Sequence Analysis and Motif Finding Martview: http://www.ensembl.org/Multi/martviewhttp://www.ensembl.org/Multi/martview TOUCAN: http://homes.esat.kuleuven.be/~saerts/software/tutorial1/TOUCAN _Tutorial_Overview.html http://homes.esat.kuleuven.be/~saerts/software/tutorial1/TOUCAN _Tutorial_Overview.html SeqVista: http://zlab.bu.edu/SeqVISTA/tutorials/motif.htmhttp://zlab.bu.edu/SeqVISTA/tutorials/motif.htm Mitra: http://fluff.cs.columbia.edu:8080/domain/mitra.htmlhttp://fluff.cs.columbia.edu:8080/domain/mitra.html Spex: http://ep.ebi.ac.uk/EP/SPEXS/http://ep.ebi.ac.uk/EP/SPEXS/ Gene Expression Analysis: http://geneontology.org/GO.tools.microarray.shtml http://geneontology.org/GO.tools.microarray.shtml