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Data	
  Visualiza+on	
  &	
  Annota+on	
  
8th	
  Interna+onal	
  Biocura+on	
  Conference	
  
24	
  April	
  2015	
  |	
  Beijing,	
  China	
  
	
  
Rama	
  Balakrishnan	
  
Saccharomyces	
  Genome	
  Database	
  
Gene	
  Ontology	
  Consor5um	
  
Stanford	
  University,	
  CA,	
  USA	
  
Image	
  by	
  Mar5n	
  Krzywinski.	
  Lim	
  et	
  al.	
  Genome	
  Biol	
  (2015)	
  16:18	
  
Monica	
  Munoz-­‐Torres	
  
Berkeley	
  Bioinforma5cs	
  Open-­‐Source	
  Projects	
  
Lawrence	
  Berkeley	
  Na5onal	
  Lab,	
  CA,	
  USA	
  
Outline	
  
1.  Introduc5on	
  
– Goals	
  
– Examples	
  of	
  genome	
  visualiza5on	
  tools	
  
2.  Panelists	
  
– Lorna	
  Richardson:	
  	
  
eMouseAtlas	
  and	
  Image	
  Informa5cs	
  
– Justyna	
  Szostak:	
  
Curated	
  Causal	
  Biological	
  Network	
  Models	
  	
  
3.  Discussion	
  
– Featuring	
  you!	
  
2	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
2	
  
3	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
Goals	
  of	
  the	
  workshop	
  
1.  To	
  learn	
  about	
  tools	
  available	
  for	
  human	
  interpreta5on	
  of	
  
genomic	
  data,	
  specifically	
  in	
  the	
  context	
  of	
  annota5on.	
  
	
  
2.  To	
  open	
  a	
  space	
  for	
  discussion:	
  genomic	
  data	
  are	
  ever	
  more	
  
abundant	
  and	
  heterogeneous,	
  with	
  widely	
  varied	
  sources,	
  
produc5on	
  techniques,	
  and	
  intrinsic	
  experimental	
  error.	
  
–  How	
  do	
  we	
  analyze	
  these	
  data?	
  
–  What	
  is	
  the	
  best	
  way	
  to	
  interpret	
  the	
  stories	
  the	
  data	
  are	
  telling	
  us?	
  
–  How	
  to	
  put	
  these	
  together	
  (overlay)	
  visually?	
  
–  Developers:	
  what	
  is	
  the	
  best	
  way	
  to	
  disseminate	
  and	
  contribute	
  code	
  to	
  
make	
  tool	
  development	
  easier?	
  
Then	
  and	
  Now	
  
Figures 	
   	
  	
  	
  49	
  
Tables 	
   	
  	
  	
  27	
  
References 	
  452	
  
	
  
4	
  
Figures 	
   	
  1	
  
Tables 	
   	
  0	
  
References 	
  6	
  
	
  
4	
  
Genomic	
  Data:	
  	
  
Heterogeneous	
  &	
  Abundant	
  
•  Structural:	
  gene	
  models,	
  transcriptomes,	
  
RNAseq,	
  differen5al	
  expression,	
  etc.	
  
•  Func5onal:	
  gene	
  ontology,	
  interac5ons,	
  
phenotypes,	
  SNPs,	
  complexes,	
  protein	
  
abundance,	
  diseases,	
  images,	
  etc.	
  
•  Some	
  examples	
  .	
  .	
  .	
  	
  
5	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
5	
  
Genomic	
  differences	
  
6	
  
Gene	
  structure,	
  ideograms,	
  maps	
  
7	
  
Most	
  of	
  the	
  curated	
  data	
  is	
  text	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
8	
  
Visualizing	
  interac5on	
  data	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
9	
  
Overlaying	
  curated	
  data	
  
hgp://string-­‐db.org	
  
Complexes,	
  interac5ons,	
  and	
  more	
  
Complex	
  SGD_GO:0005955	
  
calcineurin	
  complex	
  subunits	
  
	
  
Interac5ons	
  
hgp://3drepertoire.russelllab.org/	
   Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
11	
  
Phenotypes	
  and	
  diseases	
  
hgp://monarchini5a5ve.org/	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
12	
  
Sequence	
  varia5ons	
  
Sequence	
  varia5on	
  in	
  	
  
various	
  strains	
  of	
  	
  
S.	
  cerevisiae	
  
13	
  
Molecular	
  Model	
  
Edi5ng	
  Environment	
  
Noctua	
  –	
  prototype	
  from	
  GOC	
  
	
  
-­‐	
  Each	
  node	
  (box)	
  is	
  a	
  func5on	
  or	
  
process.	
  	
  
-­‐	
  Other	
  nodes	
  are	
  folded	
  in	
  as	
  OWL	
  
expressions.	
  
-­‐	
  Users	
  may	
  add	
  and	
  drag	
  elements	
  
-­‐	
  Supports	
  real	
  5me	
  collabora5on	
  
14	
  
Understanding	
  the	
  Data	
  
Much	
  of	
  the	
  interpreta+on	
  requires	
  human	
  
judgment.	
  Visualiza+on	
  improves	
  our	
  
understanding	
  and	
  increases	
  our	
  chances	
  of	
  
extrac+ng	
  meaningful	
  conclusions.	
  
15	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
15	
  
Cancer	
  
miRNome	
  
revealed	
  survival	
  
differences	
  in	
  
diffuse	
  large	
  B-­‐
cell	
  lymphoma	
  
pa5ents	
  
16	
  
Lim	
  et	
  al.	
  Genome	
  Biol	
  
16:18	
  (2015)	
  	
  
Circos	
  
17	
  
ENCODE	
  
Circular	
  Genome	
  Data	
  
Visualiza+on	
  
-­‐	
  Human	
  placenta	
  
transcriptome	
  
-­‐	
  Pancrea5c	
  expression	
  db	
  
-­‐	
  Wall-­‐sized	
  High-­‐res	
  display	
  
for	
  compara5ve	
  analys.	
  of	
  CNV	
  
-­‐	
  Chromosomal	
  transloca5ons	
  
-­‐	
  Variant	
  iden5fica5on	
  in	
  
mul5ple	
  sclerosis	
  
-­‐	
  Sorghum	
  seedling	
  
development	
  under	
  Low	
  Temp	
  
condi5ons	
  
-­‐	
  Etc.,	
  etc.,	
  etc…	
  	
  
18	
  
Visualizing	
  sequencing	
  data	
  
19	
  
Nielsen	
  et	
  al.	
  Nature	
  Met	
  Suppl	
  7:	
  3s.	
  (2010)	
  	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
Genome	
  Browsers	
  
20	
  
Nielsen	
  et	
  al.	
  Nature	
  Met	
  Suppl	
  7:	
  3s.	
  (2010)	
  	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
Compara5ve	
  Genomics	
  Visualiza5on	
  
21	
  
Nielsen	
  et	
  al.	
  Nature	
  Met	
  Suppl	
  7:	
  3s.	
  (2010)	
  	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
Communica5ng	
  Complex	
  Data	
  
Focus	
  on	
  meaning	
  instead	
  of	
  structure—anchor	
  the	
  figure	
  to	
  
relevant	
  biology	
  rather	
  than	
  to	
  methodological	
  details.	
  
1)  What	
  are	
  the	
  interes5ng	
  findings,	
  and	
  what	
  representa5on	
  
would	
  communicate	
  them	
  clearly?	
  	
  
	
  
22	
  
2)  Forgo	
  conven5onal	
  
approaches	
  to	
  displaying	
  
mul5dimensional	
  data.	
  
Beger	
  to	
  project	
  the	
  data	
  
onto	
  familiar	
  visual	
  
paradigms,	
  such	
  as	
  a	
  
protein	
  network	
  or	
  
pathway,	
  to	
  saliently	
  show	
  
biological	
  effects	
  in	
  a	
  
func5onal	
  context.	
  
Krzywinski	
  and	
  Savig.	
  	
  
Nature	
  Methods	
  10:7,	
  595	
  (2013)	
  
Storytelling	
  
•  Relate	
  your	
  data	
  using	
  the	
  age-­‐old	
  custom	
  of	
  
telling	
  a	
  story.	
  
– Stories	
  have	
  the	
  capacity	
  to	
  delight	
  and	
  surprise	
  
and	
  to	
  spark	
  crea5vity	
  by	
  making	
  meaningful	
  
connec5ons	
  between	
  data	
  and	
  the	
  ideas,	
  interests	
  
and	
  lives	
  of	
  your	
  readers.	
  
23	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
23	
  
Open-­‐source:	
  	
  
dissemina5on	
  &	
  contribu5ons	
  
•  Gene5c	
  &	
  genomic	
  informa5on	
  is	
  more	
  valuable	
  
when	
  shared	
  
•  Promote	
  and	
  encourage	
  Open	
  Science:	
  transparency,	
  
reproducibility,	
  data	
  provenance.	
  E.g.	
  Open	
  
Bioinforma5cs	
  Founda5on	
  hgp://open-­‐bio.org	
  	
  
•  Public	
  repositories	
  make	
  solware	
  easily	
  accessible	
  
and	
  allow	
  collabora5ve	
  efforts,	
  e.g.	
  GitHub	
  
24	
  
hgps://github.com/	
  
25	
  
Biocura5on	
  2015	
  
Data	
  Visualiza5on	
  &	
  Annota5on	
  
8th	
  Interna5onal	
  
Biocura5on	
  Conference	
  
Our	
  Panelists	
  
1.  Lorna	
  Richardson:	
  	
  
eMouseAtlas	
  and	
  Image	
  Informa5cs	
  
	
  
2.  Justyna	
  Szostak:	
  
Curated	
  Causal	
  Biological	
  Network	
  Models	
  	
  

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Data Visualization And Annotation Workshop at Biocuration 2015

  • 1. Data  Visualiza+on  &  Annota+on   8th  Interna+onal  Biocura+on  Conference   24  April  2015  |  Beijing,  China     Rama  Balakrishnan   Saccharomyces  Genome  Database   Gene  Ontology  Consor5um   Stanford  University,  CA,  USA   Image  by  Mar5n  Krzywinski.  Lim  et  al.  Genome  Biol  (2015)  16:18   Monica  Munoz-­‐Torres   Berkeley  Bioinforma5cs  Open-­‐Source  Projects   Lawrence  Berkeley  Na5onal  Lab,  CA,  USA  
  • 2. Outline   1.  Introduc5on   – Goals   – Examples  of  genome  visualiza5on  tools   2.  Panelists   – Lorna  Richardson:     eMouseAtlas  and  Image  Informa5cs   – Justyna  Szostak:   Curated  Causal  Biological  Network  Models     3.  Discussion   – Featuring  you!   2   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   2  
  • 3. 3   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   Goals  of  the  workshop   1.  To  learn  about  tools  available  for  human  interpreta5on  of   genomic  data,  specifically  in  the  context  of  annota5on.     2.  To  open  a  space  for  discussion:  genomic  data  are  ever  more   abundant  and  heterogeneous,  with  widely  varied  sources,   produc5on  techniques,  and  intrinsic  experimental  error.   –  How  do  we  analyze  these  data?   –  What  is  the  best  way  to  interpret  the  stories  the  data  are  telling  us?   –  How  to  put  these  together  (overlay)  visually?   –  Developers:  what  is  the  best  way  to  disseminate  and  contribute  code  to   make  tool  development  easier?  
  • 4. Then  and  Now   Figures        49   Tables        27   References  452     4   Figures    1   Tables    0   References  6     4  
  • 5. Genomic  Data:     Heterogeneous  &  Abundant   •  Structural:  gene  models,  transcriptomes,   RNAseq,  differen5al  expression,  etc.   •  Func5onal:  gene  ontology,  interac5ons,   phenotypes,  SNPs,  complexes,  protein   abundance,  diseases,  images,  etc.   •  Some  examples  .  .  .     5   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   5  
  • 8. Most  of  the  curated  data  is  text   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   8  
  • 9. Visualizing  interac5on  data   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   9  
  • 10. Overlaying  curated  data   hgp://string-­‐db.org  
  • 11. Complexes,  interac5ons,  and  more   Complex  SGD_GO:0005955   calcineurin  complex  subunits     Interac5ons   hgp://3drepertoire.russelllab.org/   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   11  
  • 12. Phenotypes  and  diseases   hgp://monarchini5a5ve.org/   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   12  
  • 13. Sequence  varia5ons   Sequence  varia5on  in     various  strains  of     S.  cerevisiae   13  
  • 14. Molecular  Model   Edi5ng  Environment   Noctua  –  prototype  from  GOC     -­‐  Each  node  (box)  is  a  func5on  or   process.     -­‐  Other  nodes  are  folded  in  as  OWL   expressions.   -­‐  Users  may  add  and  drag  elements   -­‐  Supports  real  5me  collabora5on   14  
  • 15. Understanding  the  Data   Much  of  the  interpreta+on  requires  human   judgment.  Visualiza+on  improves  our   understanding  and  increases  our  chances  of   extrac+ng  meaningful  conclusions.   15   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   15  
  • 16. Cancer   miRNome   revealed  survival   differences  in   diffuse  large  B-­‐ cell  lymphoma   pa5ents   16   Lim  et  al.  Genome  Biol   16:18  (2015)    
  • 17. Circos   17   ENCODE   Circular  Genome  Data   Visualiza+on   -­‐  Human  placenta   transcriptome   -­‐  Pancrea5c  expression  db   -­‐  Wall-­‐sized  High-­‐res  display   for  compara5ve  analys.  of  CNV   -­‐  Chromosomal  transloca5ons   -­‐  Variant  iden5fica5on  in   mul5ple  sclerosis   -­‐  Sorghum  seedling   development  under  Low  Temp   condi5ons   -­‐  Etc.,  etc.,  etc…    
  • 18. 18  
  • 19. Visualizing  sequencing  data   19   Nielsen  et  al.  Nature  Met  Suppl  7:  3s.  (2010)     Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference  
  • 20. Genome  Browsers   20   Nielsen  et  al.  Nature  Met  Suppl  7:  3s.  (2010)     Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference  
  • 21. Compara5ve  Genomics  Visualiza5on   21   Nielsen  et  al.  Nature  Met  Suppl  7:  3s.  (2010)     Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference  
  • 22. Communica5ng  Complex  Data   Focus  on  meaning  instead  of  structure—anchor  the  figure  to   relevant  biology  rather  than  to  methodological  details.   1)  What  are  the  interes5ng  findings,  and  what  representa5on   would  communicate  them  clearly?       22   2)  Forgo  conven5onal   approaches  to  displaying   mul5dimensional  data.   Beger  to  project  the  data   onto  familiar  visual   paradigms,  such  as  a   protein  network  or   pathway,  to  saliently  show   biological  effects  in  a   func5onal  context.   Krzywinski  and  Savig.     Nature  Methods  10:7,  595  (2013)  
  • 23. Storytelling   •  Relate  your  data  using  the  age-­‐old  custom  of   telling  a  story.   – Stories  have  the  capacity  to  delight  and  surprise   and  to  spark  crea5vity  by  making  meaningful   connec5ons  between  data  and  the  ideas,  interests   and  lives  of  your  readers.   23   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   23  
  • 24. Open-­‐source:     dissemina5on  &  contribu5ons   •  Gene5c  &  genomic  informa5on  is  more  valuable   when  shared   •  Promote  and  encourage  Open  Science:  transparency,   reproducibility,  data  provenance.  E.g.  Open   Bioinforma5cs  Founda5on  hgp://open-­‐bio.org     •  Public  repositories  make  solware  easily  accessible   and  allow  collabora5ve  efforts,  e.g.  GitHub   24   hgps://github.com/  
  • 25. 25   Biocura5on  2015   Data  Visualiza5on  &  Annota5on   8th  Interna5onal   Biocura5on  Conference   Our  Panelists   1.  Lorna  Richardson:     eMouseAtlas  and  Image  Informa5cs     2.  Justyna  Szostak:   Curated  Causal  Biological  Network  Models