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From  Data  to  Knowledge:   Extrac4ng  Biological  Insight  from  Diverse  Data  Sources   Stephen  D.  Turner,  Ph.D.   Bioinforma4cs  Core  Director   [email protected]   bioinforma4cs.virginia.edu  

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GWAS:  One  gene,  one  enzyme,  one  func4on?   Manolio  TA.  N  Engl  J  Med  2010;363:166-­‐176.   Genome.gov/GWAStudies   October  9,  2013   bioinforma4cs.virginia.edu  

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DNA  Varia4on:  Limita4ons   October  9,  2013   bioinforma4cs.virginia.edu                      GWAS  DOES  NOT  INFORM:     •  Which  gene  is  affected   •  How  gene  func4on  is  perturbed   •  How  biological  processes  are  altered  

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One  gene,  one  enzyme,  one  func4on?   Jeong,  H.  et  al..  (2001)  Nature  411:41–42.   Ptacek,  J.  et  al.  (2005)  Nature  438:679–684.   Guimera  and  Amaral.  (2005).  Nature  433:895-­‐900.   Tong,  A.H.  et  al.  (2001).  Science  294:2364-­‐2368.   Zhu  X.  et  al.  (2007).  Genes  &  Dev  21:1010-­‐1024.   October  9,  2013   bioinforma4cs.virginia.edu  

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Distribu4on  of  Disease  Genes   Diseases  connected  if  same   gene  implicated  in  both.   Genes  connected  if  implicated   in  the  same  disorder.   Goh  et  al.  (2007).  PNAS  104:8685.  

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Distribu4on  of  Disease  Genes   Protein-­‐protein  interac4ons   Genes  connected  if  implicated   in  the  same  disorder.   Overlay  with  PPI  data   Goh  et  al.  (2007).  PNAS  104:8685.  

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Distribu4on  of  Disease  Genes   Protein-­‐protein  interac4ons   Genes  connected  if  implicated   in  the  same  disorder.   Overlay  with  PPI  data   Genes  contribuCng  to  a  common   disease  interact  through  protein-­‐ protein  interacCons.   Goh  et  al.  (2007).  PNAS  104:8685.  

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Distribu4on  of  Disease  Genes   Seebacher  and  Gavin  (2011).  Cell  144:1000-­‐1001   Goh  et  al.  (2007).  PNAS  104:8685.   k  =  degree        =  #  interac4on  partners   •  “EssenCal”  genes   -­‐  Encode  hubs   -­‐  Are  expressed  globally     •  “Non-­‐essenCal”  disease  genes   -­‐  Do  not  encode  hubs   -­‐  Tissue  specific  expression   Nonrandom  placement  of     disease  genes  in  interactome!  

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Interactome  Mapping  &  Data  Integra4on   Vidal  et  al,  Cell  2011.   October  9,  2013   bioinforma4cs.virginia.edu  

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Data  Integra4on:  Gene4c  Varia4on  &  Gene  Expression   +   Are  DNA  variants   that  are  associated   with  disease  also   associated  with  gene   expression  levels?   October  9,  2013   bioinforma4cs.virginia.edu  

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Data  Integra4on:  Gene  expression  +  DNA  Binding   October  9,  2013   bioinforma4cs.virginia.edu   Gene  expression  arrays  +  ChIP-­‐Seq  

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Data  Integra4on:  4  Dimensions   Schadt  et  al.  2009.  Network  view  of   disease  and  compound  screening.   Nat  Rev  Drug  Discovery  8:286.   Probabilis4c  Bayesian   Network  Integra4ng:   1.  Gene4c  varia4on   2.  Gene  expression   3.  Protein-­‐protein   interac4ons   4.  Transcript  factor  binding   October  9,  2013   bioinforma4cs.virginia.edu  

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Data  Integra4on:  6  Dimensions   October  9,  2013   bioinforma4cs.virginia.edu   1.  Metabolite  concentra4ons   2.  RNA  expression   3.  DNA  Varia4on   4.  DNA-­‐protein  binding   5.  Protein-­‐protein  interac4on   6.  Protein-­‐metabolite  interac4on     •  Metabolites  linked  to  DNA  variants  (MetQTLs)   •  MetQTLs  co-­‐localize  with  eQTLs   •  Using  a  Bayesian  network   –  Nodes:  DNA  varia4on,  gene  expresion,  metabolite  concentra4on   –  Priors:  Protein-­‐DNA  binding,  protein-­‐protein  interac4on,  metabolite-­‐protein  interac4on   –  Edges:  Inferred  rela4onships  à  mechanism   Zhu  J,  …  Schadt  EE.  2012.  S4tching  together  Mul4ple   Data  Dimensions  Reveals  Interac4ng  Metabolomic   and  Transcriptomic  Networks  that  Modulate  Cell   Regula4on.  PLoS  Biol.   Infer  causality   Special  Session  4:     BioinformaCc  IntegraCon  of  Diverse  Experimental  Data  Sources   Today,  room  201A,  2:30-­‐4:25pm     Part  D  (4:00-­‐4:25):     SCtching  together  mulCple  data  dimensions…  

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Data  Integra4on:  Mouse  Cis-­‐Regulatory  Map   •  RNA-­‐Seq  and  ChIP-­‐Seq  for  6  DNA-­‐ binding  factors  *  19  cell  types   –  ChIP:  PolII,  H3K4me3,  H3K4me1,   H3K27ac,  P300,  CTCF   –  Adult  Tissues:  bone  marrow,   cerebellum,  cortex,  heart,  intes4ne,   kidney,  liver,  lung,  olfactory  bulb,   placenta,  spleen,  tes4s,  thymus   –  Embryonic  Tissues:  brain,  heart,  limb,   liver   –  Cell  lines:  mESCs,  MEFs   •  Found  300,000  cis-­‐reg  features   –  11%  mouse  genome   –  70%  conserved  non-­‐coding  sequence   October  9,  2013   bioinforma4cs.virginia.edu   Shen  et  al.  A  map  of  the  cis-­‐ regulatory  sequences  in  the   mouse  genome.  Nature,  July   2012.  

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Data  Integra4on:  Epigenome  &  Transcriptome   •  Zhang  JA,  Mortazavi  A,  Williams  BA,  Wold  BJ,  Rothenberg  EV.  Dynamic  Transforma4ons  of   Genome-­‐wide  Epigene4c  Marking  and  Transcrip4onal  Control  Establish  T  Cell  Iden4ty.  Cell  2012.   •  ChIP-­‐Seq  +  RNA-­‐Seq  in  sequen4al  T-­‐cell  developmental  stages   •  Changes  in  gene  expression  co-­‐occur  w/  histone  modifica4on  at  cis-­‐regulatory  sites.   October  9,  2013   bioinforma4cs.virginia.edu  

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Summary   •  Data  is  cheap  and  diverse.   –  Gene4c  varia4on:  GWAS,  next-­‐gen  sequencing   –  Gene  expression:  Microarray,  RNA-­‐seq   –  Proteomics:  Y2H,  CoAP/MS   •  Cellular  components  interact  in  a  network  with  other  cellular   components.   •  Disease  is  the  result  of  an  abnormality  in  that  network.   •  Integrate  mul4ple  data  types,  understand  network,   understand  disease.   October  9,  2013   bioinforma4cs.virginia.edu  

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Thank  you   Web:    bioinforma4cs.virginia.edu   E-­‐mail:  [email protected]   Blog:    www.GesngGene4csDone.com   Twiter:  twiter.com/gene4cs_blog   October  9,  2013   bioinforma4cs.virginia.edu