A  mul&  dimensional  approach  to  studying  mul$ple  sclerosis  pathogenesis  

Cell  cycle  

Cell  differen&a&on  

apoptosis   Cellular    process  

proteins  

pathways  

genomic  

expression  

Genotype  

Phenotype  low  

high  

Physiological/pathological    Process  (health/disease)  

autoimmunity   cancer  metabolic  

neurodegenera&ve  

infec&ous  

metagenomic  

Challenges  in  data  acquisi&on  and  analysis  in  the  biological  sciences    

•  Democra&za&on  of  equipment  •  Distributed  data  genera&on  •  Cheaper  per  byte  (?)  •  Individualized  analysis    •  PRP  will  enable  a  new  mode  of  collabora&ng  for  health  sciences.    

Current  projects    

•  Genomics  –  GWAS    –  WGA  –  RNA  expression  

•  Endophenotypes  –  Clinical    –  Imaging  

•  MRI  •  OCT  

•  Microbiome  –  Human    –  experimental  

•  Heterogeneous  networks  –  Drug  repurposing  –  Gene&c  architecture  

•  Genomics  –  GWAS  (80,000  samples  *  2M  variants)  -­‐>  160B  datapoints  

•  IMSGC:  Collabora&on  with  40+  labs  

–  WGA  (500  genomes)  -­‐>  125  Tb  •  Own  effort.  Poten&al  IMSGC  involvement    

–  RNA  expression  (~1000  samples)  -­‐>  10  Tb  •  Own  effort.  EPIC  Project  

•  Endophenotypes  –  Clinical  (24/7  monitoring,  wearables,  etc)  –  Imaging  

•  MRI  (5000)  •  OCT  (3000)  

•  Microbiome  (Collabora&on  with  R.  Knight)  –  Human  (4000  16S,  500  metagenomics)  –  Experimental  (?)  

•  Heterogeneous  networks  –  Drug  repurposing  –  Gene&c  architecture  of  diseases  

6M  edges  

50,000  nodes  

Current  data  transfer  speeds  

•  Data  transfer  frequency:  ad-­‐hoc  •  Within  building  (10  Gbps)  •  From  lab  to  QB3  cluster  (10Gbps)  – QB3  cluster  (~5000  cores)    

•  From  lab  to  EC2  (20Mbps)        

QB3  cluster