Download presentation
Presentation is loading. Please wait.
Published byRoger Hodges Modified over 9 years ago
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
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.