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The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
The Trans-NIH RNAi Initiative: Informatics
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The Trans-NIH RNAi Initiative : Informatics

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  • 1. The Trans‐NIH RNAi Ini0a0ve  Informa(cs  Rajarshi Guha 
  • 2. Mission  To establish a state of the art RNAi screening facility to perform genome-wide RNAi screens with investigators in the intramural NIH community. •  Gene func0on  •  Pathway analysis  •  Target ID  •  Compound MoA  •  Drug antagonist/ agonist 
  • 3. RNAi Informa0cs Infrastructure 
  • 4. RNAi Analysis Workflow  Raw and  GO  Processed  annota0ons  Pathways  Data  Interac0ons  • Summary  Normaliza0on  • Thresholding  Hit Triage  sta0s0cs  • Median  • Hypothesis  • GO seman0c  • Correc0ons  • Quar0le  tes0ng  similarity  • Background  • Sum of ranks  • Pathways  • Interac0ons  QC  Hit Selec0on  Follow‐up  Hit List 
  • 5. RNAi Informa0cs Toolset  • Local databases (screen data, pathways,  interac0ons, etc).  • Commercial pathway tools.   • Custom soUware for loading, analysis and  visualiza0on. 
  • 6. Back End Services   •  Currently all computa0onal analysis performed  on the backend  •  R & Bioconductor code  •  Custom R package (ncgcrnai) to support NCGC  infrastructure  –  Partly derived from cellHTS2  –  Supports QC metrics, normaliza0on, adjustments,  selec0ons, triage, (sta0c) visualiza0on, reports  •  Some Java tools for  –  Data loading  –  Library and plate registra0on 
  • 7. User Accessible Tools 
  • 8. User Accessible Tools 
  • 9. Challenge – siRNA Design &  Valida5on  •  We mostly depend on quality controls  implemented by vendor  –  siRNA design algorithms not a high priority  •  Always interested in extra filters that help us  get a reliable hit list  •  Would like to have measures of   –  Off‐target effects  –  Protein half lives 
  • 10. Challenge ‐ miRNA Target ID  •  Screened a set of 885 human miRNA’s  for CPT sensi0za0on  •  Iden0fied 23 sensi0zing miRNA’s  •  But, we don’t have target informa0on  –  Predic0ons aren’t par0cularly helpful  –  Poor overlap with siRNA hits   miRAnda  TargetScan  •  Link pathogenic  miRNA’s to human   targets 
  • 11. Challenge ‐ RNAi & Small  Molecule Screens  What targets mediate activity of siRNA and compound Given a set of siRNA hits and their targets, is there a •  Reuse pre-existing MLI data compound showing similar •  Develop new annotated libraries inhibition CAGCATGAGTACTACAGGCCA  TACGGGAACTACCATAATTTA  Target ID and validation Link RNAi generated pathway peturbations to small molecule activities. Could provide insight into polypharmacology •  Run parallel RNAi screen Goal: Develop systems level view of small molecule activity
  • 12. Challenge – RNAi Meta Analyses   •  Building up a collec0on of screens  –  Across cell lines, species, …  –  Not necessarily “designed”  •  What do we do with this?  –  Iden0fy consistent markers   –  Characterize differences between  cell lines   –  Extrapolate from gene knockdown to pathway  and higher level differences  –  Merge with gene expression data 
  • 13. The People  •  Scoh Mar0n  RNAi •  Pinar Tuzmen  •  Dac Trung Nguyen  Small Molecules •  Yuhong Wang 

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