1. “software” of life

2. Genomes to function

3. Lessons from genome projects Most genes have no known function Most genes w/ known function assigned from sequence-similarity matches to other organisms Need methods to experimentally assay gene activity on a genome-wide scale

4. Condition 1 RNA Condition 2 RNA gene enriched in condition 1 gene enriched in condition 2 17,997 genes 94% of genome Measure expression on genome-wide scale: DNA Microarrays

5. Global Analyses of Gene Expression Collect all microarrays from the world Gene activity across thousands of conditions conditions (~5k) genes (20k)

6. Digital Age of Biology Biologists drowning in data Bottleneck now is developing computational resources for discovery Think Genbank before BLAST...

7. Discovering Gene Function on a Global Scale Gene Networks Search Engines

8. Matt Weirauch Corey Powell Chad Chen Charlie Vaske Alex Williams Martina Koeva Gene Networks

9. link 2 genes together if they are co-activated in multiple organisms build networks from all the links discover function from a gene’s links understand bigger picture of gene regulation

10. Principle #1 Gene networks are “scale free”

11. Scale free – gene networks may arise from processes like expansion of WWW some links on the WWW

12. Principle #2 Genes self assemble into modular subcomponents

13. http://www.cse.ucsc.edu/~jstuart/multispecies

14. Principle #2 Genes self assemble into modular subcomponents

15. Principle #3 Coordinated activity is a signature of gene function proliferation transcription ribosome biogenesis ribosomal subunits respiration protein modification secretion fatty acid metab. tissue growth neuronal immune response development / hox genes cell polarity, cell structure Newly evolved

16. Proteasome “module” http://www.cse.ucsc.edu/~jstuart/multispecies

17. integratorsubunits Principle #4 Local network topology reports on gene function

18. top 3 integrators:

19. Integrators have more cis-regulatory complexity integrators subunits

20. integrators have different phenotypes

21. Current Directions for Gene Networks Gene isoform networks to capture alternative splicing Predict drug targets from synthetic lethal nets

22. Current Directions for Gene Networks Gene isoform networks to capture alternative splicing Predict drug targets from synthetic lethal nets (w/ Lokey Lab)

23. Matt Weirauch Corey Powell Chad Chen Charlie Vaske Alex Williams Martina Koeva Gene Isoform Networks

24. Most human genes (>60%) are alternatively spliced. Alternative splicing gives rise to different proteins from the same gene The particular variant expressed can be very important (e.g. sex determination in flies) The functional implications of alt. splicing in humans is still largely unexplored. Provides a higher resolution understanding of gene expression and its relationship to health & disease

25. Splicing Microarrays Measure particular subparts of the gene structure (e.g. exon-exon junctions) Data now available for human and mouse tissue compendiums Infer isoforms from expression of subparts across the tissues Identify isoform modules

26. A functional network of gene isoforms isoform patternsisoform network assemble into modules functional signatures global network design

27. Matt Weirauch Corey Powell Chad Chen Charlie Vaske Alex Williams Martina Koeva Search Engines

28. Search engines to discover gene function

29. identify every member of a pathway Retinoblastoma pathway

30. (slide from Art Owen)

31. gene recommender query search for regulating conditions

32. gene recommender search for regulating conditions query

33. gene recommender search for new candidates regulating conditions

34. query + “hits” gene recommender regulating conditions

35. Rb hda-1 lin-36 rba-2 lin-9 query Score experiments 1 Score genes 2 gene recommender procedure dpl-1 rba-2 K12D12.1 Rb R06C7.8 hda-1 B0464.6 R06F6.1 T16G12.5 F55A3.7 plk-1 lin-9 lin-36 hits

36. computational validation Score experiments 1 Score genes 2 hda-1 lin-36 rba-2 lin-9 query (no Rb) 1. rba-2 2. lin-9 3. dpl-1 4. R06C7.8 5. hda-1 6. B0464.6 7. R06F6.1 8. K12D12.1 9. T16G12.5 10. F55A3.7 11. plk-1 12. Rb 13. lin-36 hits

37. Searching 1 organism

38. H.sap query Ecdy hits Anim hits Opis hits Euk hits Cell hits Ortholog Map Ecdy Opis Euk Anim Cell H.sap hits H.sap A.tha hits H.pyl hits S.cer hits C.ele hits D.mel hits D.mel C.ele S.cer A.tha H.pyl Multiple Species Search Engine

39. Orthology Map cdk-4 mcm-5 mcm-7 n/a pcn-1 hda-1 … Cdk4 Mcm5 Mcm7 E2f Mus209 Rpd3 … C.ele D.mel MCM3 (8) MCM6 (9) MCM5 (28) HDAC1 (69) RBBP4 (86) RPA1 (428) BUB1 (1866)... GR H.sap hits CDK4 MCM5 MCM7 E2F1 PCNA HDAC1 … H.sap cell cycle query Anim Ecdy MCM3* (1) MCM6* (2) HDAC1* (3) MCM5* (4) RBBP4 (5)... Anim hits MCM6* (1) BUB1* (2) HDAC1* (3) MCM3* (4) RPA1 (5)... Ecdy hits H.sap Hdac1 Bub1 Mcm6 Rpa1 Mcm3... mcm-3 rpa-1 mcm-6 bub-1 rba-2 hda-1... H.sap BTPs of C.ele hits GR H.sap BTPs of D.mel hits GR HDAC1 (3) BUB1 (21) MCM6 (26) RPA1 (48) MCM3 (60)... MCM3 (6) RPA1 (9) MCM6 (15) BUB1 (24) RBBP4 (25) HDAC1 (114)...

40. Related genes sort to the top of the search lists

41. Multiple species search is more precise

44. immunological synapse Gene productComment CD8 antigenquery unknown tyrosine kinaselymphocyte specific T-cell receptor zetaquery CD2 antigenparticipates in T-cell activation CD4 antigen (p55)query unknown Src-like adaptor negative regulator of T-cell receptor signaling CD8 antigenquery unknown transcription factorT-cell specific paired box gene 8 (PAX8)new association

45. 17 34 2 11 4 21 28 14 42 12 36 26 2423 7 1 5 22 3 15571 15572

46. Search Engine Directions Search gene networks for pathway members –Incorporate multiple data sources in search –Faster than scanning raw data Discriminative search engines –E.g. identify genes coregulated with DNA damage genes more so than S-phase genes

47. Search Engine Directions Network Recommender –Search gene networks for pathway members –Incorporate multiple data sources in search –Faster than scanning raw data Discriminative search engines –E.g. identify genes coregulated with DNA damage genes more so than S-phase genes

48. Matt Weirauch Corey Powell Chad Chen Charlie Vaske Alex Williams Martina Koeva Network Recommender

49. coexpression synthetic lethal physical protein interactions

50. Iterative Propagation Algorithm 1.Given a set of genes in a pathway A 2.Score gene g based on how connected to predicted pathway members in network i S i (g) =  h w igh p(h) /  h w igh, where h ranges over neighbors of g in network i 3.Compute posterior each gene g in pathway Construct a positive distribution P(S i (g)| g in A) Construct a negative distribution P(S i (g)| g not in A) 4.Set p(g) = ∏ i P(g in A | S i (g))

51. Network Recommender Performance recall precision

52. Network Recommender Results

53. Network Recommender for cell cycle - physical protein interaction - gene coexpression

54. Supplemental Material

55. Genetic interactions