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Towards Incidental Collaboratories For Experimental Data

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Talk at 3D virtual cell meeting, San Diego, CA, December 13-14 2012

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Towards Incidental Collaboratories For Experimental Data

  1. 1. Towards  Incidental  Collaboratories  For   Experimental  Data  Thanks:    Maryann  Martone,  Anita  Bandrowski,   Anita  de  Waard  NIF,  UCSD   VP  Research  Data  Collabora>ons  Nathan  Urban,  Shreejoy  Thripathy,  CMU   Elsevier  RDS,  Jericho,  VT,  USA  Ed  Hovy,  Gully  Burns,  ISI/CMU;  Phil  Bourne,  UCSD  
  2. 2. Problem:  a  rose  is  not  a  rose:  •  “…there  was  significant  variability  of  the   injected  venom  composi>on  from   specimen  to  specimen,  in  spite  of  their   common  biogeographic  origin.”   Jose  A.  Rivera-­‐Or>z,  Herminsul  Cano,  Frank  Marí,  Intraspecies  variability  of  the   injected  venom  of  Conus  ermineus,  doi:10.1016/j.pep>des.2010.11.014  •  “…Strains  DV-­‐3/84  DV-­‐7/84  (group  3)   showed  76.6%  similarity  to  each  other  and   were  similar  to  all  other  strains  at  the   67.6%  level.”   Zofia  Dzierżewicz  et  al.,  Intraspecies  variability  of  Desulfovibrio  desulfuricans   strains  determined  by  the  gene>c  profiles,  FEMS  Microbiology  Leeers,  Volume   219,  Issue  1,  14  February  2003,  Pages  69–74,  doi:10.1016/ S0378-­‐1097(02)01199-­‐0     =>  A  specimen  is  not  a  species!  
  3. 3. Problem:  gene  expression  varies  with:  Age:  “SIRT1-­‐Associated  genes  are  deregulated  in  the  aged  brain”   Philipp  Oberdoerffer  et  al.,  SIRT1  RedistribuJon  on  ChromaJn  Promotes  Genomic  Stability  but  Alters  Gene  Expression   during  Aging,  Cell,  Volume  135,  Issue  5,  28  November  2008,  Pages  907–918,  doi:10.1016/j.cell.2008.10.025  Smell:  “…major  urinary  proteins  […]  mediate  the  pregnancy  blocking  effects  of  male  urine”   P.A.  Brennan,  et  al,  PaOerns  of  expression  of  the  immediate-­‐early  gene  egr-­‐1  in  the  accessory  olfactory  bulb  of  female   mice  exposed  to  pheromonal  consJtuents  of  male  urine,  Neuroscience,  Volume  90,  Issue  4,  June  1999,  P  1463–1470,   doi:10.1016/S0306-­‐4522(98)00556-­‐9  Hunger:  “Out  of  the  ~30K  genes,  about  10K  are  differen>ally  expressed  in  liver  cells  when  an  animal  is  in  different  states  of  sa>ety.“   Zhang  F,  Xu  X,  Zhou  B,  He  Z,  Zhai  Q  (2011)  Gene  Expression  Profile  Change  and  Associated  Physiological  and   Pathological  Effects  in  Mouse  Liver  Induced  by  Fas>ng  and  Refeeding.     PLoS  ONE  6(11):  e27553.  doi:10.1371/journal.pone.002755    Light:  “Longer-­‐term  enrichment  training  also  altered  the  mRNA  levels  of  many  genes  associated  with  structural  changes  that  occur  during  neuronal  growth.”   Cailoeo  C.,  et  al.  (2009)  Effects  of  Nocturnal  Light  on  (Clock)  Gene  Expression  in  Peripheral  Organs:  A  Role  for  the   Autonomic  Innerva>on  of  the  Liver.  PLoS  ONE  4(5):  e5650.  doi:10.1371/journal.pone.0005650:       =>  Knowing  genes  is  not  knowing     how  they  are  expressed!  
  4. 4. Problem:  no  man  (or  mouse)  is  an  island…    •  “We  found  the  diversity  and  abundance  of  each  habitat’s   signature  microbes  to  vary  widely  even  among  healthy   subjects,  with  strong  niche  specializa>on  both  within   and  among  individuals.”   The  Human  Microbiome  Project  Consor>um,  Structure,  func>on  and  diversity  of  the  healthy   human  microbiome,  Nature  486,  207–214  (14  June  2012)  doi:10.1038/nature11234  •  “Coloniza>on  of  an  infant’s  gastrointes>nal  tract  begins   at  birth.  The  acquisi>on  and  normal  development  of  the   neonatal  microflora  is  vital  for  the  healthy  matura>on  of   the  immune  system.”     Mackie  RI,  Sghir  A,  Gaskins  HR.,  Developmental  microbial  ecology  of  the  neonatal   gastrointes>nal  tract.  Am  J  Clin  Nutr.  1999  May;69(5):1035S-­‐1045S   =>  An  animal  is  an  ecosystem!  
  5. 5. Interac>ons  create  more  complexity:     •  Compu>ng  cancer:  “No  amount  of  informa,on  about   what  happens  inside  a  single  cell  can  ever  tell  you   what  a  ,ssue  is  going  to  do,”  [Glazier]  said.  “Much  of   the  informa>on  and  complexity  of  >ssues  and  life  is   embedded  in  the  way  cells  talk  to  each  other  and  the   extracellular  environment.”     •  Megadata:“These  complex  emergent  systems  are   impossible  to  understand,”,”[we]  founded  Applied   Proteomics  to  create  a  protein  diagnos>c  that  reveals   not  just  where  a  cancer  is,  but  how  it  interacts  with   the  body..”   Nature  Special  Issue  Vol.  491  No.  7425   ‘Physical  Scien>sts  Take  On  Cancer’  :    =>  The  whole  is  more  than  the  sum  of  its  parts!  
  6. 6. Big  problems  in  biology:  •  Interspecies  variability  >  A  specimen  is  not  a  species!  •  Gene  expression  variability  >    Knowing  genes  is  not     knowing  how  they  are  expressed!  •  Microbiome  >    An  animal  is  an  ecosystem!  •  Systems  biology  >  Whole  is  more  than  the  sum  of  its  parts!  •  Models  vs.  experiment  >  Are  we  talking  about  the  same   things?  In  a  way  we  can  all  use?    •  Dynamics  >  Life  is  not  in  equilibrium!           Life  is  complicated!   Reduc>onism  doesn’t   work  for  living  systems.   hep://en.wikipedia.org/wiki/File:Duck_of_Vaucanson.jpg  
  7. 7. Sta>s>cs  to  the  rescue!    With  enough  observa>ons,  trends  and  anomalies  can  be  detected:  •   “Here  we  present  resources  from  a  popula>on  of  242   healthy  adults  sampled  at  15  or  18  body  sites  up  to  three   >mes,  which  have  generated  5,177  microbial  taxonomic   profiles  from  16S  ribosomal  RNA  genes  and  over  3.5   terabases  of  metagenomic  sequence  so  far.”     The  Human  Microbiome  Project  Consor>um,  Structure,  func>on  and  diversity  of   the  healthy  human  microbiome,  Nature  486,  207–214  (14  June  2012)  doi:10.1038/ nature11234  •  “The  large  sample  size  —  4,298  North  Americans  of   European  descent  and  2,217  African  Americans  —  has   enabled  the  researchers  to  mine  down  into  the  human   genome.”     Nidhi  Subbaraman,  Nature  News,  28  November  2012,  High-­‐resolu>on  sequencing   study  emphasizes  importance  of  rare  variants  in  disease.    
  8. 8. Enable  ‘incidental  collaboratories’:  •  Collect:  store  data  at  the  level  of  the  experiment:   –  Accessible  through  a  single  interface   –  Add  enough  metadata  to  know  what  was  done/seen  •  Connect:  allow  analyses  over:     –  Similar  experiment  types     –  Experiments  done  with/on  similar  biological  ‘things’     (species,  strains,  systems,  cells  etc.)   –  In  a  way  that  can  be  used  by  modelers!    •  Keep:   –  Long-­‐term  preserva>on  of  data  and  so}ware       –  Fulfill  Data  Management  Plan  requirements   –  Allow  ‘gated’  access  when  and  to  whom  researcher  wants  
  9. 9. Problem:  biological  research  is  quite  insular  •  Biology  is  small:  size  10^-­‐5  –  10^2  m,   scien>st  can  work  alone  (‘King’  and   ‘subjects’).    •  Biology  is  messy:  it  doesn’t  happen   Prepare   behind  a  terminal.    •  Biology  is  compe>>ve:  many     Ponder   Observe   people  with  similar  skill  sets,     Communicate   vying  for  the  same  grants       Analyze  •  In  summary:  the  structure  of  biological   research  does  not  inherently  promote   collabora>on  (vs.,  for  instance,  big   physics  or  astronomy).  
  10. 10. Let’s  look  at  a  typical  lab:  •  How  to  get  the  right     an>body  IDs    •  And  messy  bits      •  From  the  lab  notebook    •  Into  the  PI’s  command     center?  
  11. 11. Objec>ons  and  rebueals  re.  data  sharing  Objec,on:   Rebu=al:  “But  our  lab  notebooks  are  all  on   Develop  smart  phone/tablet  apps  for  data  paper”   input  “I  need  to  see  a  direct  benefit  from   Develop  ‘data  manipula,on  dashboard’  for  something  I  spend  my  >me  on”   PI  to  allow  beeer  access  to  full     experimental  output  for  his/her  lab  “I  want  things  to  be  peer  reviewed   Allow  reviewers  access  to  experimental  before  I  expose  them”   database  before  publica>on  (of  data  or     paper)  “I  don’t  really  trust  anyone  else’s   Add  a  social  networking  component  to  this  data  –  well,  except  for  the  guys  I   data  repository  so  you  know  who  (to  the  went  to  Grad  School  with…”     individual)  created  that  data  point.    “I  am  afraid  other  people   =>  Reward  system  moves  from  a  might  scoop  my  discoveries”   compe,,on  to  a  ‘shared  mission’  
  12. 12. Data  sharing  enables  collaboratories:  Labs  go  from  being   Prepare  informa>on  islands  to  being  ‘sensors  in  a  network’   Observa>ons  ‘Conglomera>on  of   Analyze   Communicate   Observa>ons  evidence’  can  happen  Allow  place  to  share   Think   Observa>ons  nega>ve  data  –  reproducing  experiments.   Prepare   Prepare   Analyze   Communicate   Analyze   Communicate  
  13. 13. So  we  can  do  joint  experiments:  Across  labs,  experiments:  track  reagents  and  how  they  are  used   Observa>ons   Observa>ons   Observa>ons   Prepare   Prepare   Analyze   Communicate   Analyze   Communicate  
  14. 14. So  we  can  do  joint  experiments:  Compare  outcome  of  interac>ons  with  these  en>>es   Observa>ons   Observa>ons   Observa>ons   Prepare   Prepare   Analyze   Communicate   Analyze   Communicate  
  15. 15. So  we  can  do  joint  experiments:  Build  a  ‘virtual  reagent  spectrogram’  by  comparing    how  different  en>>es     Observa>ons  interacted  in  different  experiments   Observa>ons   Observa>ons   Prepare   Prepare   Analyze   Communicate   Analyze   Communicate  
  16. 16. A  single  environment  to  perform,  store,   share  and  report  on  experiments:   metadata   1.  Store  metadata  on  all  materials   metadata   metadata   2.  Track  the  methods  while  doing  them   3.  Write  papers  that  ‘wrap  around’  this   metadata   4.  Don’t  ‘send’  your  papers  –  just   metadata   expose  them  to  the  outside  world   5.  Invite  reviews;  open  data  to   trusted  par>es,  at  trusted  >me   Rats  were  subjected  to  two   6.  Allow  apps/tools  to  integrate   grueling  tests   (click  on  fig  2  to  see  underlying   data).  These  results  suggest     that  the  neurological  pain  pro-­‐   Calculate,  coordinate…     Review   Revise   Compile,  comment,   Edit   compare…  
  17. 17. Elsevier  Research  Data  Services:  1.  Help  increase  the  amount  of  data  shared  from   the  lab,  enabling  incidental  collaboratories  2.  Help  increase  the  value  of  the  data  shared  by   increasing  annota>on,  normaliza>on,   provenance  enabling  enhanced  interoperability  3.  Help  measure  and  deliver  credit  for  shared   data,  the  researchers,  the  ins>tute,  and  the   funding  body,  enabling  more  sustainable   pla€orms  
  18. 18. Plans  with  CMU/Neuroelectro.org:  •  Do  a  pilot  in  Q3  2013,  using:   – 7”  Tablets  for  data  input   – Can  we  link  to  barcodes  for  AB-­‐s,  scan  on  tablet   (so  we  can  include  the  batch’s  provenance?)   – Links  to  local  so}ware  to  connect  to  runs   – Dashboard  for  the  PI  to  keep  track/play  with   experiments     – Gated  exports  to     •  Neuroelectro.org   •  NIF   – Address  NSF  Data  Management  Plan  requirements?    
  19. 19. In  summary:  •  Life  is  complicated!    •  We  need  to  connect  experiments  •  To  do  so,  overcome  technical  barriers  and   social  barriers  (more  difficult)  •  Maybe  3D  VC  can  help  define  a  common   mission?       a.dewaard@elsevier.com  

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