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Motivation
Your research is valuable
All advances in knowledge are incremental, with
each new idea ultimately building on ...
Losing data at a rapid rate
up to 80% unavailable after 20 years
2
http://www.nature.com/news/scientists-losing-data-at-a-...
Data valuation
 Information is infinitely shareable without any loss of
value
 Reuse increases the value derived from th...
WHAT ONTOLOGIES ARE
eye
 what kinds of
things exist?
 what are the
relationships
between
these things?
ommatidium
sense organeye disc
is_a
p...
October 25, 2016
Ontology defined
 The science of what is: of the kinds and
structures of the objects, and their properti...
WHY ARE ONTOLOGIES
NEEDED
Ontologies help with decision
making
handy ontology tells us what’s there…
Where
should I
eat…?
Ontologies don’t just organize data; they
also facilitate inference,
and that creates new knowledge, often
unconsciously i...
What a 5 year old child (or a computer) will likely infer
about the world from this helpful ontology…
Flag of fresh juice
...
Information retrieval is not straightforward
 18-day pregnant females
 female (lactating)
 individual female
 worker c...
October 25, 2016
Motivation is to represent biology
accurately
 Inferences and decisions we make are
based upon what we k...
Annotation bottleneck
 Even the best research will be for naught if
data can never be found again.
 An active lab can ea...
HOW TO BUILD ONTOLOGIES
Ontologies must be shared
 Communities form scientific theories
 that seek to explain all of the existing evidence
 and...
October 25, 2016
Ontologies must be used
 Usage feeds back on ontology development and
improves the ontology
 It improve...
Why do we need rules for good
ontology?
 Ontologies must be intelligible
 To humans (for annotation) and
 To machines (...
October 25, 2016
First Rule: Univocity
 Terms (including those describing
relations) should have the same meanings
on eve...
October 25, 2016
Glucose
synthesis
GluconeogenesisGlucose
synthesis
?
The Challenge of Univocity:
People call the same thi...
Comparison is difficult, especially across species or across
databases that each use one of these different variants
Disam...
October 25, 2016
Bud initiation? How is a
computer to know?
= tooth bud initiation
= cellular bud initiation
= flower bud initiation
Include plain “bud initiation” as a synonym for e...
October 25, 2016
Second Rule: Positivity
 Complements of classes are not
themselves classes.
 Terms such as ‘non-mammal’...
October 25, 2016
The Challenge of Positivity
Some organelles are membrane-bound.
A centrosome is not a membrane bound orga...
October 25, 2016
Positivity
 Note the logical difference between
 “non-membrane-bound organelle” and
 “not a membrane-b...
October 25, 2016
Third Rule: Objectivity
 Which classes exist is not a function of our
biological knowledge.
 Terms such...
Objectivity
 How can we annotate when we know that
we don’t have any information?
 Annotate to root nodes and use the ND...
October 25, 2016
GPCRs with unknown ligands
Annotate
to this
Ontologies are graphs, where the nodes (terms in the
ontology ) are connected by edges (relationships
between the terms)
i...
Reasoning is critical
 Prokaryotic and
Eukaryotic cell are
declared disjoints
 Fungal cell is a
Eukaryotic cell
 Spore ...
Reasoning is critical
Solution: clarify spore
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022006
33
Pro...
October 25, 2016
Fifth Rule: Intelligibility of Definitions
 The terms used in a definition should be
simpler (more intel...
October 25, 2016
Sixth Rule: Keep it Real
 When building or maintaining an ontology,
always think carefully at how classe...
October 25, 2016
The Rules
1. Univocity: Terms should have the same meanings
on every occasion of use
2. Positivity: Terms...
Natural Language Computable Ontology
+ Large existing body of information
+ Highly expressive
- Ambiguous (making it diffi...
ONTOLOGIES AND BIOLOGY
Without rigor, we won’t—know what we
know, or where to find it, or what we can infer
from it.
GENOME ANNOTATION
Apollo
Once a genome is sequenced…
 What are the parts? (sequence features)
 Protein coding genes (coding sequence)
 Non codin...
ComputeCrawler
RepeatMasker
Genscan
FgenesH
Grail
Blast
Sim4
Genewise
Lap
CGTGTGCGCAGGGGGATATGCGGCGCATATTGTGTTGAAGAGATGCGC...
DNA on a linear coordinate
Little boxes
de novo predictions
protein alignments
transcript alignments
full length cDNAs
APOLLO
annotation editing environment
BECOMING ACQUAINTED WITH APOLLO
Color by CDS frame,
toggle strands, set color
scheme...
Coordinate transforms:
Curator ‘ligation’
Coordinate transforms:
intron folding
Alterations: whether experimental artifacts or
natural differences
Substitutions
Alterations: whether experimental artifacts or
natural differences
Insertions
Alterations: whether experimental artifacts or
natural differences
Deletions
Alterations: whether experimental artifacts or
natural differences
Impact
Instructions54 |
APOLLO ON THE WEB
instructions
Username:
user.number@example.com
Password:
usernumber
Email Password Ser...
GCGAAGTGCCAACTTCTACACACACAAAG
GCGAAGTGCCAACTTCTACACACACAAAG
For example – ontologically described
genotypes/variants
intri...
FUNCTIONAL ANNOTATION
Phylogenetic Annotation Inferencing Tool
— PAINT
Evolutionary history is the
natural way to organize and
analyze biological data
Ancestral inference
• Integration at points of common ancestry
• Infer “hidden” character of living organisms
• Explicitly...
What is transitive annotation?
 Related genes have a common function because their common
ancestor had that function.
 N...
61
• Green indicates experimental
• Black dot indicates direct
experimental data.
dot indicates a more
general functional ...
• PAINTed nodes –
• 3 steps carried out by
curator
• Gain & Loss of function
• Inferred By Descendants
• Experimental anno...
PGM1
subfamily
PGM5
subfamily
Curated active site information from CDD (cd03085)
phosphoglucomutase
Duplication
event
http://questfororthologs.org/
FUNCTIONAL ANNOTATION
Noctua for Building Models of Biology
Motivation: multi-scale knowledge
models of mechanistic biology
Bai, J. P. F., & Abernethy, D. R. (n.d.). Systems Pharmaco...
A data model for causal ontology
annotations: “LEGO”
Activity
GO:nnnnnnn
What: <molecule>
A data model for causal ontology
annotations: “LEGO”
Activity
GO:nnnnnnn
What: <molecule>
Where: GO/CL/Uberon
A data model for causal ontology
annotations: “LEGO”
Activity
GO:nnnnnnn
What: <molecule>
Where: GO/CL/Uberon
Activity
GO:...
A data model for causal ontology
annotations: “LEGO”
Activity
GO:nnnnnnn
What: <molecule>
Where: GO/CL/Uberon
Activity
GO:...
Process
GO:nnnnnnn
A data model for causal ontology
annotations: “LEGO”
Activity
GO:nnnnnnn
What: <molecule>
Where: GO/CL/...
A data model for causal ontology
annotations: “LEGO”
GTPase activity
GO:0003924
What: TEM1 S000004529
Where: spindle pole
...
Exit from mitosis
GO:0010458
A data model for causal ontology
annotations: “LEGO”
GTPase activity
GO:0003924
What: TEM1 S0...
http://noctua.berkeleybop.org/
Collaborative
Editing!
RDF/OWL
Semantic
Representation
Pathway data
-Reasoning
-Linked data...
Diabetes mockup example
https://vimeo.com/channels/Noctua
Annotation Systems & Implementation Issues - Suzanna Lewis
Annotation Systems & Implementation Issues - Suzanna Lewis
Annotation Systems & Implementation Issues - Suzanna Lewis
Annotation Systems & Implementation Issues - Suzanna Lewis
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Annotation Systems & Implementation Issues - Suzanna Lewis

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EMBL-ABR Best Practice Workshop Series: The Data Life-cycle

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Annotation Systems & Implementation Issues - Suzanna Lewis

  1. 1. Motivation Your research is valuable All advances in knowledge are incremental, with each new idea ultimately building on earlier knowledge such as you are gathering.
  2. 2. Losing data at a rapid rate up to 80% unavailable after 20 years 2 http://www.nature.com/news/scientists-losing-data-at-a-rapid-rate-1.14416
  3. 3. Data valuation  Information is infinitely shareable without any loss of value  Reuse increases the value derived from the original investment  By combining data, their value increases  The more these assets are used, the more additional knowledge can be gathered (data science)  As a corollary, unshared or insufficiently documented information is less valuable  The more accurate and complete the information is, the more useful, and therefore valuable, it is Moody and Walsh 1999
  4. 4. WHAT ONTOLOGIES ARE
  5. 5. eye  what kinds of things exist?  what are the relationships between these things? ommatidium sense organeye disc is_a part_of develops from A biological ontology is:  A machine interpretable representation of some aspect of biological reality
  6. 6. October 25, 2016 Ontology defined  The science of what is: of the kinds and structures of the objects, and their properties and relations in every area of reality.  The classification of entities and the relations between them.  Defined by a scientific field's vocabulary and by the canonical formulations of its theories.  Seeks to solve problems which arise in these domains.
  7. 7. WHY ARE ONTOLOGIES NEEDED
  8. 8. Ontologies help with decision making handy ontology tells us what’s there… Where should I eat…?
  9. 9. Ontologies don’t just organize data; they also facilitate inference, and that creates new knowledge, often unconsciously in the user. (Presumable) country of origin Type of cuisine
  10. 10. What a 5 year old child (or a computer) will likely infer about the world from this helpful ontology… Flag of fresh juice ‘Frozen Yogurt’ cuisine in search of a national identity? Where delicatessen food hails from from… Fresh Juice is a national cuisine…
  11. 11. Information retrieval is not straightforward  18-day pregnant females  female (lactating)  individual female  worker caste (female)  2 yr old female  female (pregnant)  lgb*cc females  sex: female  400 yr. old female  female (outbred)  mare  female, other  adult female  female parent  female (worker)  female child  asexual female  female plant  monosex female  femal  femlale  diploid female  female(gynoecious)  remale  metafemale  f  femele  semi-engorged female  sterile female  famale  female, pooled  sexual oviparous female  normal female  femail  femalen  sterile female worker  sf  female  females  strictly female  vitellogenic replete female  female - worker  females only  tetraploid female  worker  female (alate sexual)  gynoecious  thelytoky  hexaploid female  female (calf)  healthy female  female (gynoecious)  female (f-o)  hen  probably female (based on morphology)  castrate female  female with eggs  ovigerous female  3 female  cf.female  female worker  oviparous sexual females  female (phenotype)  cystocarpic female  female, 6-8 weeks old  worker bee  female mice  dikaryon  female, virgin  female enriched  female, spayed  dioecious female  female, worker  pseudohermaprhoditic female Courtesy of N. Silvester and S. Orchard, European Nucleotide Archive, EMBL- EBI
  12. 12. October 25, 2016 Motivation is to represent biology accurately  Inferences and decisions we make are based upon what we know of the biological reality.  An ontology is a computable representation of this underlying biological reality.  Enables a computer to reason over the data in (some of) the ways that we do.
  13. 13. Annotation bottleneck  Even the best research will be for naught if data can never be found again.  An active lab can easily generate 10-100GB of data per month, and it is very difficult to manage on this scale.  Must be annotated at the rate at which it is generated  And the data must be integrated with other data  Furthermore, the effort put into generating this data will be utterly wasted if the curated data cannot be reliably computed upon.
  14. 14. HOW TO BUILD ONTOLOGIES
  15. 15. Ontologies must be shared  Communities form scientific theories  that seek to explain all of the existing evidence  and can be used for prediction  The computable representation must also be shared  Thus ontology development is inherently collaborative October 25, 2016
  16. 16. October 25, 2016 Ontologies must be used  Usage feeds back on ontology development and improves the ontology  It improves even more when these data are used to answer research questions  There will be fewer problems in the ontology and more commitment to fixing remaining problems when important research data is involved that scientists depend upon
  17. 17. Why do we need rules for good ontology?  Ontologies must be intelligible  To humans (for annotation) and  To machines (for searching, reasoning and error-checking)  Makes it easier to find the most accurate term(s) to use  Avoids annotation errors  Makes it easier for new curators to learn and understand  Makes it easier to combine with other ontologies and terminologies  Makes automatic reasoning possible for searching & inference  Bottom line:  Following basic rules makes more useful ontologies
  18. 18. October 25, 2016 First Rule: Univocity  Terms (including those describing relations) should have the same meanings on every occasion of use.  In other words, they should refer to the same kinds of entities in reality
  19. 19. October 25, 2016 Glucose synthesis GluconeogenesisGlucose synthesis ? The Challenge of Univocity: People call the same thing by different names
  20. 20. Comparison is difficult, especially across species or across databases that each use one of these different variants Disambiguation  Use a single term, and plenty of synonyms  Gluconeogenesis  Synonyms:  Glucose synthesis  Glucose biosynthesis  Glucose formation  Glucose anabolism
  21. 21. October 25, 2016 Bud initiation? How is a computer to know?
  22. 22. = tooth bud initiation = cellular bud initiation = flower bud initiation Include plain “bud initiation” as a synonym for each of these terms Classification rule: Disambiguation
  23. 23. October 25, 2016 Second Rule: Positivity  Complements of classes are not themselves classes.  Terms such as ‘non-mammal’ or ‘non- membrane’ do not designate genuine classes.
  24. 24. October 25, 2016 The Challenge of Positivity Some organelles are membrane-bound. A centrosome is not a membrane bound organelle, but it still may be considered an organelle.
  25. 25. October 25, 2016 Positivity  Note the logical difference between  “non-membrane-bound organelle” and  “not a membrane-bound organelle”  The latter includes everything that is not a membrane bound organelle!
  26. 26. October 25, 2016 Third Rule: Objectivity  Which classes exist is not a function of our biological knowledge.  Terms such as ‘unknown’ or ‘unclassified’ or ‘unlocalized’ do not designate biological natural kinds.
  27. 27. Objectivity  How can we annotate when we know that we don’t have any information?  Annotate to root nodes and use the ND (no data) evidence code  Similar strategies can be used for any situation more specific information is not yet known October 25, 2016
  28. 28. October 25, 2016 GPCRs with unknown ligands Annotate to this
  29. 29. Ontologies are graphs, where the nodes (terms in the ontology ) are connected by edges (relationships between the terms) is-a part-of Fourth Rule: Use defined relationships mitochondrial membrane chloroplast Cell membrane Chloroplast membrane
  30. 30. Reasoning is critical  Prokaryotic and Eukaryotic cell are declared disjoints  Fungal cell is a Eukaryotic cell  Spore is a Fungal cell and a Prokaryotic cell Satisfiable? http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022006 32 Prokaryotic Cell Eukaryotic Cell Fungal Cell Spore disjoint
  31. 31. Reasoning is critical Solution: clarify spore http://www.plosone.org/article/info:doi/10.1371/journal.pone.0022006 33 Prokaryotic Cell Eukaryotic Cell Fungal Cell disjoint Actinomycete Type Spore Mycetozoa Type Spore
  32. 32. October 25, 2016 Fifth Rule: Intelligibility of Definitions  The terms used in a definition should be simpler (more intelligible) than the term to be defined  otherwise the definition provides no assistance  to human understanding  for machine processing
  33. 33. October 25, 2016 Sixth Rule: Keep it Real  When building or maintaining an ontology, always think carefully at how classes (types, kinds, species) relate to instances in reality
  34. 34. October 25, 2016 The Rules 1. Univocity: Terms should have the same meanings on every occasion of use 2. Positivity: Terms such as ‘non-mammal’ or ‘non- membrane’ do not designate genuine classes. 3. Objectivity: Terms such as ‘unknown’ or ‘unclassified’ or ‘unlocalized’ do not designate biological natural kinds. 4. Single Inheritance: No class in a classification hierarchy should have more than one is_a parent on the immediate higher level 5. Intelligibility of Definitions: The terms used in a definition should be simpler (more intelligible) than the term to be defined 6. Basis in Reality: When building or maintaining an ontology, always think carefully at how classes relate to instances in reality 7. Distinguish Universals and Instances
  35. 35. Natural Language Computable Ontology + Large existing body of information + Highly expressive - Ambiguous (making it difficult and unreliable to compute on) - Less expressive + Logical + Precise How to best describe biology?
  36. 36. ONTOLOGIES AND BIOLOGY Without rigor, we won’t—know what we know, or where to find it, or what we can infer from it.
  37. 37. GENOME ANNOTATION Apollo
  38. 38. Once a genome is sequenced…  What are the parts? (sequence features)  Protein coding genes (coding sequence)  Non coding RNAs (rRNA, snoRNA, tRNA, microRNA antisense RNA)  Promoters and regulatory regions  Transposons  Recombination hotspots, origins of replication  Centromeres & telomeres  …
  39. 39. ComputeCrawler RepeatMasker Genscan FgenesH Grail Blast Sim4 Genewise Lap CGTGTGCGCAGGGGGATATGCGGCGCATATTGTGTTGAAGAGATGCGCTGCATTTCGCGATGCCGATTAGGNCACAGGGAA
  40. 40. DNA on a linear coordinate Little boxes
  41. 41. de novo predictions
  42. 42. protein alignments
  43. 43. transcript alignments full length cDNAs
  44. 44. APOLLO annotation editing environment BECOMING ACQUAINTED WITH APOLLO Color by CDS frame, toggle strands, set color scheme and highlights. Upload evidence files (GFF3, BAM, BigWig), add combination and sequence search tracks. Query the genome using BLAT. Navigation and zoom. Search for a gene model or a scaffold. Get coordinates and “rubber band” selection for zooming. Login User-created annotations. Annotator panel. Evidence Tracks Stage and cell-type specific transcription data. http://genomearchitect.org/web_apollo_user_guide
  45. 45. Coordinate transforms: Curator ‘ligation’
  46. 46. Coordinate transforms: intron folding
  47. 47. Alterations: whether experimental artifacts or natural differences Substitutions
  48. 48. Alterations: whether experimental artifacts or natural differences Insertions
  49. 49. Alterations: whether experimental artifacts or natural differences Deletions
  50. 50. Alterations: whether experimental artifacts or natural differences Impact
  51. 51. Instructions54 | APOLLO ON THE WEB instructions Username: user.number@example.com Password: usernumber Email Password Server Begin at user.one@example.com userone 1 1 user.two@example.com usertwo 2 1 user.three@example.com userthree 3 1 user.four@example.com userfour 4 1 user.five@example.com userfive 5 1 user.six@example.com usersix 1 7 user.seven@example.com userseven 2 7 user.eight@example.com usereight 3 7 user.nine@example.com usernine 4 7 user.ten@example.com userten 5 7 user.eleven@example.com usereleven 1 1 user.twelve@example.com usertwelve 2 1 user.thirteen@example.com userthirteen 3 1 user.fourteen@example.com userfourteen 4 1 user.fifteen@example.com userfifteen 5 1 user.sixteen@example.com usersixteen 1 7 user.seventeen@example.com userseventeen 2 7 user.eightteen@example.com usereighteen 3 7 user.nineteen@example.com usernineteen 4 7 user.twenty@example.com usertwenty 5 7 user.twentyone@example.com usertwentyone 1 1 user.twentytwo@example.com usertwentytwo 2 1 user.twentythree@example.com usertwentythree 3 1 user.twentyfour@example.com usertwentyfour 4 1 user.twentyfive@example.com usertwentyfive 5 1 user.twentysix@example.com usertwentysix 1 7 user.twentyseven@example.com usertwentyseven 2 7 user.twentyeight@example.com usertwentyeight 3 7 user.twentynine@example.com usertwentynine 4 7 Server URL 1 http://ec2-52-63-181-136.ap-southeast- 2.compute.amazonaws.com/apollo/ 2 http://ec2-52-64-198-214.ap-southeast- 2.compute.amazonaws.com/apollo/ 3 http://ec2-52-62-166-89.ap-southeast- 2.compute.amazonaws.com/apollo/ 4 http://ec2-52-64-182-170.ap-southeast- 2.compute.amazonaws.com/apollo/ 5 http://ec2-52-63-255-136.ap-southeast- 2.compute.amazonaws.com/apollo/
  52. 52. GCGAAGTGCCAACTTCTACACACACAAAG GCGAAGTGCCAACTTCTACACACACAAAG For example – ontologically described genotypes/variants intrinsic genotype genomic variation complementgenomic background = + CGTAGC CGTACC apchu745/+; fgfa8ti282/ti282(AB) genomic variation complement variant single locus complement variant allele sequence alteration has_part has_part apchu745/+ apchu745 hu745 has_part has_part has_part has_part X AACGTACCGACGCTCGCTACGGGCGTATC (AB) apchu745/+; fgf8ati282/ti282 apchu745/+; fgf8ati282/ti282 GCGAAGTGCCAACTTCTACACACACAAAG GCGAAGTGCCAACTTCTACACACACAAAG AACGTAGCGACGCTCGCTACGGGCGTATC AACGTACCGACGCTCGCTACGGGCGTATC X ACAC X X X X AACGTAGCGACGCTCGCTACGGGCGTATC X ACAC X X X X X
  53. 53. FUNCTIONAL ANNOTATION Phylogenetic Annotation Inferencing Tool — PAINT
  54. 54. Evolutionary history is the natural way to organize and analyze biological data
  55. 55. Ancestral inference • Integration at points of common ancestry • Infer “hidden” character of living organisms • Explicitly leverage evolutionary relationships E.c. A.t. MTHFR1 A.t. MTHFR2 D.d. S.p. S.c. MET13 D.m. A.g. S.p. S.c. MET12 C.e. D.r. G.g. H.s. MTHFR R.n. M.m. divergence Biochemistry: purification and assay Genetics: mutant phenotypes
  56. 56. What is transitive annotation?  Related genes have a common function because their common ancestor had that function.  Not just an inference about one gene. It is also an inference for  The most recent common ancestor (MRCA)  Continuous inheritance since the MRCA  Potential inheritance by other descendants of the MRCA Gene in Yeast Gene in Mouse Function X Gene in Opisthokont MRCA Function X Function X Gene in Zebrafish Function X Function X Gene in Human Function X Function X
  57. 57. 61 • Green indicates experimental • Black dot indicates direct experimental data. dot indicates a more general functional class inferred from ontology Red indicates NOT function for the gene All nodes have persistent identifiers which are retained across different builds of the protein family trees. cholinesterase carboxylic ester hydrolase Evolutionary event type: duplication speciation
  58. 58. • PAINTed nodes – • 3 steps carried out by curator • Gain & Loss of function • Inferred By Descendants • Experimental annotations provide evidence • Inferred by Ancestry • Propagation to unannotated leaves carboxylic ester hydrolase Node with loss of function Gaudet, P., et al. (2011). Phylogenetic- based propagation of functional annotations within the Gene Ontology consortium. Briefings in Bioinformatics, 12(5), 449–62. doi:10.1093/bib/bbr042 Node with gain of function- cholinesterase
  59. 59. PGM1 subfamily PGM5 subfamily Curated active site information from CDD (cd03085) phosphoglucomutase Duplication event
  60. 60. http://questfororthologs.org/
  61. 61. FUNCTIONAL ANNOTATION Noctua for Building Models of Biology
  62. 62. Motivation: multi-scale knowledge models of mechanistic biology Bai, J. P. F., & Abernethy, D. R. (n.d.). Systems Pharmacology to Predict Drug Toxicity : Integration Across Levels of Biological Organization ∗, 451–473. doi:10.1146/annurev-pharmtox-011112-140248
  63. 63. A data model for causal ontology annotations: “LEGO” Activity GO:nnnnnnn What: <molecule>
  64. 64. A data model for causal ontology annotations: “LEGO” Activity GO:nnnnnnn What: <molecule> Where: GO/CL/Uberon
  65. 65. A data model for causal ontology annotations: “LEGO” Activity GO:nnnnnnn What: <molecule> Where: GO/CL/Uberon Activity GO:nnnnnnn What: <molecule> Where: GO/CL/Uberon Relationship RO:nnnnnnn
  66. 66. A data model for causal ontology annotations: “LEGO” Activity GO:nnnnnnn What: <molecule> Where: GO/CL/Uberon Activity GO:nnnnnnn What: <molecule> Where: GO/CL/Uberon Relationship RO:nnnnnnn Evidence: ECO, SEPIO Source: PMID, ORCID, ...
  67. 67. Process GO:nnnnnnn A data model for causal ontology annotations: “LEGO” Activity GO:nnnnnnn What: <molecule> Where: GO/CL/Uberon Activity GO:nnnnnnn What: <molecule> Where: GO/CL/Uberon Relationship RO:nnnnnnn
  68. 68. A data model for causal ontology annotations: “LEGO” GTPase activity GO:0003924 What: TEM1 S000004529 Where: spindle pole GO:0000922 GTPase inhibitor activity GO:0005095 What: BFA1 S000003814 Where: spindle pole GO:0000922
  69. 69. Exit from mitosis GO:0010458 A data model for causal ontology annotations: “LEGO” GTPase activity GO:0003924 What: TEM1 S000004529 Where: spindle pole GO:0000922 GTPase inhibitor activity GO:0005095 What: BFA1 S000003814 Where: spindle pole GO:0000922
  70. 70. http://noctua.berkeleybop.org/ Collaborative Editing! RDF/OWL Semantic Representation Pathway data -Reasoning -Linked data Gene sets Building causal models of biology using ontologies
  71. 71. Diabetes mockup example
  72. 72. https://vimeo.com/channels/Noctua

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