• Embryonic (E9 – E12)– Primitive lung buds emerge from

ventral gut epithelium

• Pseudoglandular (E12-E15)– Stereo-specific branching of the lung

bronchi. Differentiation of epithelial cells to form prealveolate saccules

• Canalicular (E15-E17)– Formation of terminal sacs and

vasculature

• Saccular (E17 – Birth)– Expansion in the numbers of terminal

sacs and capillaries. Differentiation of Type I and II alveolar cells

• Alveolar (Birth-P30)– terminal sacs develop into mature

alveolar ducts and alveoli

Stages of Lung Development

http://www.cincinnatichildrens.org/research/div/pulmonary-biology/faculty-research/whitsett-lab/projects.htm

E = EmbryonicP = Postnatal

Transcriptional profiling to discover lung development genes in the mouse (C57BL/6J)

E11.5 E13.5 E14.5 E16.5 P5

Images from Malpel S, Development (2000) 127:3057-67

E = embryonicP = postnatal

Temporal gene expression patterns

Ernst et al. (2005) Bioinformatics 21:159.

• Used Short Time-series Expression Miner (STEM)• STEM first builds model expression profiles based on

the number of time points– Profiles are complete and distinct

• Clustering algorithm assigns each gene to the profile that most closely matches its expression pattern across the time series

• Permutation tests used to determine significance of the profiles

Ernst and Bar-Joseph. (2006) BMC Bioinformatics 7:191.

349312 196 141 431

320 139

Gene expression profiles in normal mouse lung development

http://www.cs.cmu.edu/~jernst/stem/

Number of genes that match the expression profile

Data shown for three time points:E14.5, E16.5, P5

Expression change plots for STEM profiles

Gene List Interpretation1110005A23Rik, 1700009P03Rik, 1700020C11Rik, 1810058I14Rik, 2210018M11Rik, 2610301G19Rik, 2810407C02Rik, 4931406I20Rik, 4932432K03Rik, 5730467H21Rik, 5830411E10Rik, 6330581L23Rik, 9030612M13Rik, AI848100, Abca3, Abcc4, Abcd1, Acad10, Acads, Acsbg1, Acsl5, Adam12, Adamts20, Adamts5, Adamts9, Adcy3, Akap2, Alas1, Aldh1a1, Aldh1l1, Aldoc, Alg14, Alg6, Amph, Aox3, Aplp2, Appbp2, Aqp5, Arf2, Arf4, Arhgap6, Art3, Atf6, Atm, Atp1b1, Atp6v0b, Atp6v1e1, Atp7a, Atp8a1, Atp8b2, B230118G17Rik, BC016495, Bbs4, Bcat1, Bcl2l2, Bclaf1, Bid, Bpgm, Bphl, Braf, Brunol4, Btbd4, Bzw1, C1qtnf3, C730048C13Rik, Cacna1d, Cadps2, Calm2, Camk2d, Camkk2, Cart1, Casp7, Cav1, Ccnb1, Ccni, Cd36, Cdc26, Cdca5, Cdkn1b, Cdkn1c, Cdkn3, Cdx2, Cebpg, Ches1, Cited1, Clca1, Clta, Clu, Cmpk, Cnot6, Cntn4, Col18a1, Col3a1, Col4a1, Col4a6, Col9a1, Cox6b2, Cpm, Cpne5, Crbn, Crls1, Cse1l, Ctnnb1, Ctps2, Ctse, Cul3, Cyp11a1, D11Ertd333e, D1Ertd161e, D230025D16Rik, D830007F02Rik, Daam2, Dab1, Dach1, Dapk2, Dcamkl1, Dhfr, Dhrs8, Dnajc15, Dtymk, Dusp4, Dyrk1a, E2f7, Eda2r, Ednra, Ell2, Elmo3, Enah, Enpep, Enpp2, Epb4.1, Eps8, Esm1, Etv5, Eya1, Fabp3, Fabp5, Fank1, Fath, Fblim1, Fbxl20, Fbxl3, Fbxw7, Fem1c, Fgfr2, Fhit, Fhl2, Fkbp6, Folr1, Foxp1, Frk, Fusip1, Fxyd6, Fzd9, Gas7, Gata2, Gdpd2, Gja1, Gpc3, Gpx3, Gstk1, Gstp1, H2-Aa, H3f3a, Hdac9, Hel308, Hesx1, Heyl, Hhip, Hif3a, Hipk2, Hist1h2bc, Hnrpf, Hook1, Hoxd8, Hsd17b12, Hsp90b1, Hspa1b, Htra3, Ifitm3, Ifnar2, Igf1, Igfbp2, Igfbp3, Igfbp7, Ing3, Ipo7, Itga4, Itgb1, Itpr2, Jarid1d, Kcnab1, Kcnb1, Kcnip1, Kcnip4, Kcnj16, Kcns2, Kdr, Keap1, Kif2a, Klf6, Klf7, Klk1, Krt2-7, Krt2-8, Lama5, Lass6, Lcn2, Lgals7, Lgtn, Lhx1, Lhx9, Lmo4, Lrrc16, Lrrk1, Lsp1, Lss, Ltf, Madd, Mafa, Man1a2, Mapk1, Mapre1, Masp1, Mef2c, Mlph, Mmp19, Mod1, Morf4l1, Morf4l2, Mrpl18, Mrpl44, Mt1, Mt2, Mtdh, Mterf, Mthfd1, Mtm1, Mtr, Mtx2, Myef2, Myl1, Mylc2b, Mylk, Myo1b, Myo5b, Narg1, Nedd9, Neo1, Nfe2l2, Npc1, Npepl1, Npr2, Nr2f2, Nrg1, Nusap1, Ogt, Otx2, Pak1, Pak3, Papss2, Pard6b, Parp1, Pbx3, Pcbd1, Pcmtd1, Pcsk5, Pctk1, Pctk3, Pdcd6ip, Pdia3, Pfdn4, Pftk1, Phb2, Phca, Phf8, Phka1, Pitx2, Pja1, Pja2, Pnck, Pomgnt1, Porcn, Ppargc1a, Ppfibp1, Ppih, Ppp1r16b, Prc1, Prcp, Prkag2, Prkar2b, Prkcd, Psmb3, Psrc1, Ptch1, Pten, Ptgds, Ptk2b, Ptp4a1, Ptp4a2, Ptp4a3, Ptpn13, Ptx3, Qscn6, Rab2b, Rab31, Rab3a, Rab3b, Rad51l3, Rec8L1, Ren2, Rims4, Rkhd3, Rnf11, Rnf20, Robo2, Rpl39, Rps6ka3, Runx1, Runx2, Rxrb, Ryr2, S100a6, S100a9, Sat1, Scd1, Scmh1, Scn3a, Scn7a, Scn8a, Scrn1, Sdk2, Sec24a, Sec61a2, Sema3a, Sept11, Serpina3g, Sesn3, Sf4, Sfrs1, Sgk3, Shb, Sin3b, Slc11a2, Slc16a10, Slc16a7, Slc18a2, Slc25a5, Slc26a1, Slc2a13, Slc38a5, Slc39a10, Slc41a2, Slc6a14, Slc6a15, Slc6a6, Slc7a4, Slc9a2, Smc2l1, Smg5, Snapap, Sncaip, Snrk, Soat1, Sorl1, Sox10, Sox11, Sox9, Spp1, Srp54, St3gal5, Star, Strbp, Stxbp1, Sulf1, Suv420h1, Sv2b, Sycp3, Syn2, Sypl, Tacc1, Tcea3, Tcf12, Tdgf1, Tesc, Tfrc, Tgfa, Tgfb2, Thap7, Timp1, Tinagl, Tm9sf3, Tmed7, Tmed9, Tmem23, Tmpo, Tmprss13, Tradd, Tram2, Trappc5, Trim23, Trim66, Tsc1, Tspyl2, Txndc10, Txndc2, Tyro3, Uchl5, Uhrf2, Usp12, Usp7, Utp15, Uty, Vcpip1, Vim, Vldlr, Yes1, Ywhaz, Zdhhc2, Zfhx1b, Zfp148, Zfp192, Zfp275, Zfp277, Zfp28, Zfp30, Zfp36, Zfp583, Zfp62, Zfp68

Nat Genet (2000) 25: 25-29

What is an Ontology?

“Ontologies provide controlled, consistent vocabularies to describe concepts and relationships, thereby enabling knowledge-sharing”

(Tom Gruber, Stanford University)

GO was started to facilitate comparing biological knowledge across model organisms

Describe molecular and cellular biology of genes & gene products (not about gene names!)

Need a practical solution for implementation & use

Want a unifying, expandable, organism-independent vocabulary www.geneontology.org

The GO vocabularies

• Molecular Function: What a product ‘does’, precise activity

• Biological Process Biological objective, accomplished via one or more ordered assemblies of

functions

• Cellular Component ‘is located in’ (‘is a subcomponent of’ )

Definitions are the core of any ontologyTerms <string>

Synonym (s)

ID <tied to definition, not term>

Definition

GO is a Structured Vocabulary

Transcriptional profiling to discover lung development genes in the mouse (C57BL/6J)

E11.5 E13.5 E14.5 E16.5 P5

Images from Malpel S, Development (2000) 127:3057-67

E = embryonicP = postnatal

Expression change plots for normal mouse lung development

312 genes up regulated over time during development

139 genes down regulated over time during development

1110005A23Rik, 1700009P03Rik, 1700020C11Rik, 1810058I14Rik, 2210018M11Rik, 2610301G19Rik, 2810407C02Rik, 4931406I20Rik, 4932432K03Rik, 5730467H21Rik, 5830411E10Rik, 6330581L23Rik, 9030612M13Rik, AI848100, Abca3, Abcc4, Abcd1, Acad10, Acads, Acsbg1, Acsl5, Adam12, Adamts20, Adamts5, Adamts9, Adcy3, Akap2, Alas1, Aldh1a1, Aldh1l1, Aldoc, Alg14, Alg6, Amph, Aox3, Aplp2, Appbp2, Aqp5, Arf2, Arf4, Arhgap6, Art3, Atf6, Atm, Atp1b1, Atp6v0b, Atp6v1e1, Atp7a, Atp8a1, Atp8b2, B230118G17Rik, BC016495, Bbs4, Bcat1, Bcl2l2, Bclaf1, Bid, Bpgm, Bphl, Braf, Brunol4, Btbd4, Bzw1, C1qtnf3, C730048C13Rik, Cacna1d, Cadps2, Calm2, Camk2d, Camkk2, Cart1, Casp7, Cav1, Ccnb1, Ccni, Cd36, Cdc26, Cdca5, Cdkn1b, Cdkn1c, Cdkn3, Cdx2, Cebpg, Ches1, Cited1, Clca1, Clta, Clu, Cmpk, Cnot6, Cntn4, Col18a1, Col3a1, Col4a1, Col4a6, Col9a1, Cox6b2, Cpm, Cpne5, Crbn, Crls1, Cse1l, Ctnnb1, Ctps2, Ctse, Cul3, Cyp11a1, D11Ertd333e, D1Ertd161e, D230025D16Rik, D830007F02Rik, Daam2, Dab1, Dach1, Dapk2, Dcamkl1, Dhfr, Dhrs8, Dnajc15, Dtymk, Dusp4, Dyrk1a, E2f7, Eda2r, Ednra, Ell2, Elmo3, Enah, Enpep, Enpp2, Epb4.1, Eps8, Esm1, Etv5, Eya1, Fabp3, Fabp5, Fank1, Fath, Fblim1, Fbxl20, Fbxl3, Fbxw7, Fem1c, Fgfr2, Fhit, Fhl2, Fkbp6, Folr1, Foxp1, Frk, Fusip1, Fxyd6, Fzd9, Gas7, Gata2, Gdpd2, Gja1, Gpc3, Gpx3, Gstk1, Gstp1, H2-Aa, H3f3a, Hdac9, Hel308, Hesx1, Heyl, Hhip, Hif3a, Hipk2, Hist1h2bc, Hnrpf, Hook1, Hoxd8, Hsd17b12, Hsp90b1, Hspa1b, Htra3, Ifitm3, Ifnar2, Igf1, Igfbp2, Igfbp3, Igfbp7, Ing3, Ipo7, Itga4, Itgb1, Itpr2, Jarid1d, Kcnab1, Kcnb1, Kcnip1, Kcnip4, Kcnj16, Kcns2, Kdr, Keap1, Kif2a, Klf6, Klf7, Klk1, Krt2-7, Krt2-8, Lama5, Lass6, Lcn2, Lgals7, Lgtn, Lhx1, Lhx9, Lmo4, Lrrc16, Lrrk1, Lsp1, Lss, Ltf, Madd, Mafa, Man1a2, Mapk1, Mapre1, Masp1, Mef2c, Mlph, Mmp19, Mod1, Morf4l1, Morf4l2, Mrpl18, Mrpl44, Mt1, Mt2, Mtdh, Mterf, Mthfd1, Mtm1, Mtr, Mtx2, Myef2, Myl1, Mylc2b, Mylk, Myo1b, Myo5b, Narg1, Nedd9, Neo1, Nfe2l2, Npc1, Npepl1, Npr2, Nr2f2, Nrg1, Nusap1, Ogt, Otx2, Pak1, Pak3, Papss2, Pard6b, Parp1, Pbx3, Pcbd1, Pcmtd1, Pcsk5, Pctk1, Pctk3, Pdcd6ip, Pdia3, Pfdn4, Pftk1, Phb2, Phca, Phf8, Phka1, Pitx2, Pja1, Pja2, Pnck, Pomgnt1, Porcn, Ppargc1a, Ppfibp1, Ppih, Ppp1r16b, Prc1, Prcp, Prkag2, Prkar2b, Prkcd, Psmb3, Psrc1, Ptch1, Pten, Ptgds, Ptk2b, Ptp4a1, Ptp4a2, Ptp4a3, Ptpn13, Ptx3, Qscn6, Rab2b, Rab31, Rab3a, Rab3b, Rad51l3, Rec8L1, Ren2, Rims4, Rkhd3, Rnf11, Rnf20, Robo2, Rpl39, Rps6ka3, Runx1, Runx2, Rxrb, Ryr2, S100a6, S100a9, Sat1, Scd1, Scmh1, Scn3a, Scn7a, Scn8a, Scrn1, Sdk2, Sec24a, Sec61a2, Sema3a, Sept11, Serpina3g, Sesn3, Sf4, Sfrs1, Sgk3, Shb, Sin3b, Slc11a2, Slc16a10, Slc16a7, Slc18a2, Slc25a5, Slc26a1, Slc2a13, Slc38a5, Slc39a10, Slc41a2, Slc6a14, Slc6a15, Slc6a6, Slc7a4, Slc9a2, Smc2l1, Smg5, Snapap, Sncaip, Snrk, Soat1, Sorl1, Sox10, Sox11, Sox9, Spp1, Srp54, St3gal5, Star, Strbp, Stxbp1, Sulf1, Suv420h1, Sv2b, Sycp3, Syn2, Sypl, Tacc1, Tcea3, Tcf12, Tdgf1, Tesc, Tfrc, Tgfa, Tgfb2, Thap7, Timp1, Tinagl, Tm9sf3, Tmed7, Tmed9, Tmem23, Tmpo, Tmprss13, Tradd, Tram2, Trappc5, Trim23, Trim66, Tsc1, Tspyl2, Txndc10, Txndc2, Tyro3, Uchl5, Uhrf2, Usp12, Usp7, Utp15, Uty, Vcpip1, Vim, Vldlr, Yes1, Ywhaz, Zdhhc2, Zfhx1b, Zfp148, Zfp192, Zfp275, Zfp277, Zfp28, Zfp30, Zfp36, Zfp583, Zfp62, Zfp68

A Brief Statistical Detour…

Consider a population of genes representing a diverse set of biological roles or themes shown below as different colors.

Diverse Biological Roles

Thanks to John Quackenbushhttp://compbio.dfci.harvard.edu/colon_cancer.html

Many algorithms can be applied to expression data to partition genes based on expression profiles over multiple conditions.

Many of these techniques work solely on expression data and disregard biological information.

John Q.

-What are the some of the predominant biological themes represented in the gene set and how should significance be assigned to a discovered biological theme?

Consider a particular gene set…

John Q.

Example:

Population Size: 40 genesGene Set Size: 12 genes

10 genes, shown in green, have a common biological theme (GO annotation) and 8 occur within the gene set.

John Q.

The frequency of the theme in the population is 10/40 = 25%

The frequency of the theme within the cluster is 8/12 = 67%

40

12

10

8

* 80% of the genes related to the theme in the populationended up within the relatively small cluster.

AND

Consider the Outcome

John Q.

Contingency Matrix

A 2x2 contingency matrix is typically used to capture the relationships between gene set membership and membership to a biological theme.

John Q.

out

in

Theme

outin

Gene Set

2

4 26

8

ContingencyMatrix

John Q.

Assigning Significance to the Findings

The Fisher’s Exact Test permits us to determine if there arenon-random associations between the two variables, expressionbased cluster membership and membership to a particular biological theme.

8 2

4 26

in out

in

out

Gene Set

Theme p .0002

( 2x2 contingency matrix )John Q.

Hypergeometric Distribution

a b

c d

a+c

a+b

b+d

c+d

!!!!!

)!()!()!()!(

)!()!(!

!!)!(

!!)!(

dcban

dbcadcba

dcban

dbdb

caca

The probability of any particularmatrix occurring by randomselection, given no associationbetween the two variables, is givenby the hypergeometric rule.

John Q.

Probability Computation

For our matrix, 8 2

4 26, we are not only

interested in getting the probability of getting exactly8 annotation hits in the cluster but rather the probabilityof having 8 or more hits. In this case the probabilities of each of the possible matrices is summed.

9 1

3 27

10 0

2 28

8 2

4 26

.0002207 + 7.27x10-6 + 7.79x10-8 .000228John Q.

http://proto.informatics.jax.org/prototypes/vlad-1.0.3/

Are there biological processes that are enriched in the up and down regulated gene sets during lung development?

Gene list goes here

Exclude annotations made based on “sketchy” evidence

http://proto.informatics.jax.org/prototypes/vlad-1.03

VisuaL Annotation Display (VLAD) at MGI

This is a graph of GO terms, NOT genes.

The deeper the color, the more significant the association of that GO term with the gene set being analyzed.

Nodes with no text are terms in the GO hierarchy that weren’t statistically significant in the analysis.

Some aspects of graph display can be controlled by the user.

http://www.geneontology.org/GO.tools.shtml#micro

There are MANY tools designed to help you with the functional analysis of gene lists…..

http://tolfalas.informatics.jax.org/vlad/