Development	  of	  a	  Screening	  Informa3cs	  System	  at	  the	  	                                                     ...
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Development of a Screening Informatics System at the UNM Center for Molecular Discovery

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Development of a Screening Informatics System at the UNM Center for Molecular Discovery

  1. 1. Development  of  a  Screening  Informa3cs  System  at  the     UNM  Center  for  Molecular  Discovery   Jeremy Yang, Oleg Ursu, Stephen Mathias, Cristian Bologa, Anna Waller, Annette Evangelisti, Gergely Záhoransky-Köhalmi, and Tudor Oprea University of New Mexico, Albuquerque, New Mexico, USA ACS National Meeting, San Diego, March 25-29, 2012 What  is  screening  informa3cs?   Major  challenges   Automa3ng  when  every  assay  is  special  •   Informa=cs  in  support  of  screening  for  biomolecular  discovery,  usually   •   New  methodology,  such  as  high-­‐content  and  mul=plex  bioassays   Flow  cytometry  generates  mul=ple  fluorescence  measurements  per  sample  pharma  discovery:  Acquisi=on,  processing    and  storage  of  bioassay  data   •   More  data,  internal  and  external    for  use  during  projects  and  for  retrospec=ve  analyses.       and  per  target,  where  mul=plex  =  mul=-­‐target.  Even  “singleplex”  assays   •   New  privacy  and  collabora=on  models   may  employ  mul=ple  posi=ve  and  nega=ve  control  targets.  Assays  can  differ  •    Searching   over   molecules,   assays,   ac=vi=es,   targets,   etc.     I/O   &   •   Advances  in  cheminforma=cs  and  bioinforma=cs  methodology  integra=on  in  conformance  with  contractual,  legal/regulatory,  business,   greatly  in  raw  data  outputs  and  analysis  protocols  to  calculate  a  “response”   •   Development  concurrent  with  ongoing  projects  and  deadlines   represen=ng  a  biological  outcome  (e.g.  binding  to  a  target).  In  some  cases,  and  scien=fic  requirements.       requiring  con=nually  opera=onal  system.   it  may  seem  more  appropriate  to  conceive  an  API  (programming  interface)  •    Applica=ons   and   interfaces   suited   to   trans-­‐disciplinary   audience  (biology,  chemistry,  pharmacology,  medicine,  etc.).   to  recode  each  assay  analysis  rather  than  an  informa=cs  system,  flexible  but   No  shrink-­‐wrapped  solu3ons   generally  constant,  and  in  fact,  our  solu=on  combines  elements  of  both.     Why  screening  informa3cs?    Due  to  the  complexity  of  modern  screening  informa=cs,  and  in   par=cular  our  novel,  highly  versa=le  mul=plex  flow-­‐cytometry  plasorm   MicroSoP  Excel,  not  going  away  soon   (patented,  and  commercialized  as  HyperCyt),  there  cannot  be  a  shrink-­‐ Excel  remains  an  important  tool  for   wrapped  solu=on  providing  all  needed  func=onality  for  all  possible   scien=fic  data  processing,  analysis  and   experiments.   visualiza=on,  at  UNMCMD  and   elsewhere.    But  it  has  fundamental   limita=ons  and  drawbacks,  esp.  data  One   of   the   primary   mo=va=ons   for   cheminforma=cs   has   been   drug   Solu3on:  hybrid,  agile  system  of  apps  &  APIs   and  code  access  and  version  control.  discovery   which   involves   bioassay   screening   and   increasingly,   high-­‐ Heterogeneous  so8ware  components  from  (1)  commercial  vendors,  throughput  screening  (HTS).         (2)  open  source  projects,  and  (3)  custom  code  developed  at  UNM.         E.g.  Bcl-­‐2  assay  analysis   worksheets,  UNMCMD,   2007  (PubChem  AID=1693).   Screening  Informa3cs  ≠  Cheminforma3cs  !!    Cheminforma=cs   is   a  key  part   of   screening   informa=cs   but  biology  is  primary.    Plates,  wells,  samples,  and  measurements  are  physically  real  and  informa=cally  authorita=ve  while  structure  data  is  a  model  which   Custom  code:  Using  the  right  tools  for  the  tasks  may   be   incorrect   or   imprecise.     Chem-­‐   and   bio-­‐   contexts   must   be   Custom    so8ware  development  has  included:  Oracle  SQL  w/  AEI,    Excel  integrated   for   successful   system.     E.g.   EC50   =   1.7µM   is   about   a   sample,   macros,  Perl,  Java,  Python,  NCBI  EntrezU=ls  apps,  custom  PP  protocols,  a  well,  a  plate,  an  assay,  a  biological  system…  eventually  we  hope  about   Intro  to  Flow  Cytometry  at  UNMCMD   Prism  batch  code,  and  more.    Interfaces  include  command  line  apps,  web  a  lead  compound.     apps,  and  in-­‐house  APIs  for  rapid  development.     UNMCMD    specialized  for  flow  cytometry   AEVA  (Assay  Explora3on,  Viewing  &  Analysis)  web  app     Mul=plexed!   AEI  &  Pipeline  Pilot  &  customiza3on   A8er  licensing  the  Accelrys  Accord  Enterprise  Informa=cs  (AEI)  and   Pipeline  Pilot  (PP)  so8ware  in  2009,  efforts  began  to  configure  and   customize  AEI/PP.    Accelrys-­‐UNMCMD  consulta=on,  customiza=on   and  training,  revealed  (1)  what  components  could  be  used  with  minor   Accurate  data  acquisi3on  key  pre-­‐requisite     configura=on  efforts,  and  (2)  scope  of  required  custom  coding.    This   experience  was  essen=al  and  decisive  in  the  evolu=onary  design   Screening  informa=cs  depends  on  accurate  measurements  with   process.   addi=onal  informa=cs  challenges,  such  as  “binning”,  i.e.  correla=ng   fluorescence  data  to  wells  and  substances.       Conclusion   The  good  news  is  that  advances  in  so8ware  and  informa=cs  provide   choices  of  solu=ons  and  opportuni=es  to  effec=vely  manage  screening.  The   complexity  of  the  so8ware    landscape  is  truly  both  a  challenge  and   opportunity.    It  is  hoped  that  our  experiences  will  be  helpful  to  others   similarly  tasked  with  designing  and  implemen=ng  a  screening  informa=cs   system.   PP  protocol,  via  WebPort,  to  generate  PubChem  compliant  depositor  upload.   References:   1. Flow  Cytometry  Shi8ing  Gears,  Gene=c  Eng  &  Biotech  News,  Nov  15,  2011  (Vol.  31,  No.  20)  ,   Hit  Defini3on:  various  assays,  various  methods   hJp://www.genengnews.com/gen-­‐ar=cles/flow-­‐cytometry-­‐shi8ing-­‐gears/3913.   2. Edwards  BS,  Young  SM,  Saunders  MJ,  Bologa  C,  Oprea  TI,  Ye  RD,  Prossnitz  ER,  Graves  SW,  Sklar  LA.    High-­‐ • Response:  >(ac=va=on)  or  <(inhibi=on)  cutoff     throughput  flow  cytometry  for  drug  discovery.    Expert  Opin.  Drug  Discov.  2,  685-­‐696,  2007.   3. Haynes  MK,  Strouse  JJ,  Waller  A,  Leitão  A,  Curpan  RF,  Bologa  C,  Oprea  TI,  Prossnitz  ER,  Edwards  BS,  Sklar  LA,   • SD:  >(ac=va=on)  or  <(inhibi=on)  cutoff  SDs  from  plate  mean.     Thompson  TA.  Detec=on  of  intracellular  granularity  induc=on  in  prostate  cancer  cell  lines  by  small  molecules   • Custom:  custom  func=on  specified  for  assays  with  "special  needs“.   using  the  HyperCyt®  high  throughput  flow  cytometry  system.  J.  Biomol.  Screening,  14,  596-­‐609,  2009   4. The  NIHs  Molecular  Libraries  Program  -­‐  Whats  Next?  |  SLAS  Electronic  Laboratory  Neighborhood,  hJp:// c/o  Anna  Waller,  UNMCMD  HyperViewSession_20110603   Custom  may  include  counter-­‐targets,  mul=ple  +/-­‐  controls,  etc.,  etc.   www.eln.slas.org/story/1/52-­‐the-­‐nihs-­‐molecular-­‐libraries-­‐program-­‐whats-­‐next.   Powered by: Mesa OpenEye OpenBabel SciTouch

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