invited talk, Aston Business School, Aston University 2009

1. Knowledge Management in a Knowledge Based
Discipline
Robert Stevens
BioHealth Informatics Group
University of Manchester
Robert.Stevens@manchester.ac.uk

2. Introduction
• How do we do (molecular)biology
• Managing stamp albums
• A knowledge based discipline
• Representing knowledge computationally
• Ontologies that define what entities are in the
domain
• Describing biological knowledge ontologically
• Using ontologies and is it enough?

3. Ernest Rutherford
“All science is either physics or stamp collecting”
Image: http://en.wikipedia.org/wiki/File:Ernest_Rutherford2.jpg

4. Mathematical Sciences

5. Laws in Biology
Charles Darwin
Image: http://en.wikipedia.org/wiki/File:Charles_Darwin_01.jpg
On The Origin of Species - 1859

6. Classic and Modern Biology
Genotype Phenotype
Modern biology
Classic biology

7. Central Dogma
Image: http://cellbio.utmb.edu/CELLBIO/DNA-RNA.jpg

8. Speed of sequencing
• First human genome
– 10+ years to produce
– Cost $500 million
– Huge international effort
• Now done in 10 weeks
– (for $399)
– http://tinyurl.com/genomecost
– http://www.23andme.com

9. 1000+ databases
• according to Nucleic Acids Research

10. PubMed: 2 papers per minute
• ~700,000 individual papers
• Grows at 2 papers per minute (see http://
blogs.bbsrc.ac.uk for details)

11. Uniprot:- A protein database?
Ι∆ ΠΡΙΟ_ΗΥΜΑΝ ΣΤΑΝ∆ΑΡ∆; ΠΡΤ; 253 ΑΑ.
ΑΧ Π04156;
∆Τ 01−ΝΟς−1986 (Ρελ. 03, Χρεατεδ)
∆Τ 01−ΝΟς−1986 (Ρελ. 03, Λαστ σεθυενχε υπδατε)
∆Τ 20−ΑΥΓ−2001 (Ρελ. 40, Λαστ αννοτατιον υπδατε)
∆Ε Μαϕορ πριον προτειν πρεχυρσορ (ΠρΠ) (ΠρΠ27−30) (ΠρΠ33−35Χ) (ΑΣΧΡ).
ΓΝ ΠΡΝΠ.
ΟΣ Ηοµο σαπιενσ (Ηυµαν).
ΟΧ Ευκαρψοτα; Μεταζοα; Χηορδατα; Χρανιατα; ςερτεβρατα; Ευτελεοστοµι;
ΟΧ Μαµµαλια; Ευτηερια; Πριµατεσ; Χαταρρηινι; Ηοµινιδαε; Ηοµο.
ΟΞ ΝΧΒΙ_ΤαξΙ∆=9606;
ΡΝ [1]
ΡΠ ΣΕΘΥΕΝΧΕ ΦΡΟΜ Ν.Α.
ΡΞ ΜΕ∆ΛΙΝΕ=86300093; ΠυβΜεδ=3755672;
ΡΑ Κρετζσχηµαρ Η.Α., Στοωρινγ Λ.Ε., Ωεσταωαψ ∆., Στυββλεβινε Ω.Η.,
ΡΑ Πρυσινερ Σ.Β., ∆εαρµονδ Σ.ϑ.;
ΡΤ ∀Μολεχυλαρ χλονινγ οφ α ηυµαν πριον προτειν χ∆ΝΑ.∀;
ΡΛ ∆ΝΑ 5:315−324(1986).
ΡΝ [2]
ΡΠ ΣΕΘΥΕΝΧΕ ΟΦ 8−253 ΦΡΟΜ Ν.Α.
ΡΞ ΜΕ∆ΛΙΝΕ=86261778; ΠυβΜεδ=3014653;
ΡΑ Λιαο Ψ.−Χ.ϑ., Λεβο Ρ.ς., Χλαωσον Γ.Α., Σµυχκλερ Ε.Α.;
ΡΤ ∀Ηυµαν πριον προτειν χ∆ΝΑ: µολεχυλαρ χλονινγ, χηροµοσοµαλ µαππινγ,
ΡΤ ανδ βιολογιχαλ ιµπλιχατιονσ.∀;
ΡΛ Σχιενχε 233:364−367(1986).
ΡΝ [3]
ΡΠ ΣΕΘΥΕΝΧΕ ΟΦ 58−85 ΑΝ∆ 111−150 (ςΑΡΙΑΝΤ ΑΜΨΛΟΙ∆ ΓΣΣ).
ΡΞ ΜΕ∆ΛΙΝΕ=91160504; ΠυβΜεδ=1672107;
ΡΑ Ταγλιαϖινι Φ., Πρελλι Φ., Γηισο ϑ., Βυγιανι Ο., Σερβαν ∆.,
ΡΑ Πρυσινερ Σ.Β., Φαρλοω Μ.Ρ., Γηεττι Β., Φρανγιονε Β.;
ΡΤ ∀Αµψλοιδ προτειν οφ Γερστµανν−Στραυσσλερ−Σχηεινκερ δισεασε (Ινδιανα
ΡΤ κινδρεδ) ισ αν 11 κδ φραγµεντ οφ πριον προτειν ωιτη αν Ν−τερµιναλ
ΡΤ γλψχινε ατ χοδον 58.∀;
ΡΛ ΕΜΒΟ ϑ. 10:513−519(1991).
ΡΝ [4]
ΡΠ ΣΤΡΥΧΤΥΡΕ ΒΨ ΝΜΡ ΟΦ 118−221.
ΡΞ ΜΕ∆ΛΙΝΕ=20359708; ΠυβΜεδ=10900000;
ΡΑ Χαλζολαι Λ., Λψσεκ ∆.Α., Γυντερτ Π., ϖον Σχηροεττερ Χ., Ριεκ Ρ.,
ΡΑ Ζαην Ρ., Ωυετηριχη Κ.;
ΡΤ ∀ΝΜΡ στρυχτυρεσ οφ τηρεε σινγλε−ρεσιδυε ϖαριαντσ οφ τηε ηυµαν πριον
ΡΤ προτειν.∀;
ΡΛ Προχ. Νατλ. Αχαδ. Σχι. Υ.Σ.Α. 97:8340−8345(2000).
ΧΧ −!− ΦΥΝΧΤΙΟΝ: ΤΗΕ ΦΥΝΧΤΙΟΝ ΟΦ ΠΡΠ ΙΣ ΝΟΤ ΚΝΟΩΝ. ΠΡΠ ΙΣ ΕΝΧΟ∆Ε∆ ΙΝ ΤΗΕ
ΧΧ ΗΟΣΤ ΓΕΝΟΜΕ ΑΝ∆ ΙΣ ΕΞΠΡΕΣΣΕ∆ ΒΟΤΗ ΙΝ ΝΟΡΜΑΛ ΑΝ∆ ΙΝΦΕΧΤΕ∆ ΧΕΛΛΣ.
ΧΧ −!− ΣΥΒΥΝΙΤ: ΠΡΠ ΗΑΣ Α ΤΕΝ∆ΕΝΧΨ ΤΟ ΑΓΓΡΕΓΑΤΕ ΨΙΕΛ∆ΙΝΓ ΠΟΛΨΜΕΡΣ ΧΑΛΛΕ∆
ΧΧ ∀ΡΟ∆Σ∀.
ΧΧ −!− ΣΥΒΧΕΛΛΥΛΑΡ ΛΟΧΑΤΙΟΝ: ΑΤΤΑΧΗΕ∆ ΤΟ ΤΗΕ ΜΕΜΒΡΑΝΕ ΒΨ Α ΓΠΙ−ΑΝΧΗΟΡ.
ΧΧ −!− ΠΟΛΨΜΟΡΠΗΙΣΜ: ΤΗΕ ΦΙςΕ ΤΑΝ∆ΕΜ ΟΧΤΑΠΕΠΤΙ∆Ε ΡΕΠΕΑΤΣ ΡΕΓΙΟΝ ΙΣ ΗΙΓΗΛΨ
ΧΧ ΥΝΣΤΑΒΛΕ. ΙΝΣΕΡΤΙΟΝΣ ΟΡ ∆ΕΛΕΤΙΟΝΣ ΟΦ ΟΧΤΑΠΕΠΤΙ∆Ε ΡΕΠΕΑΤ ΥΝΙΤΣ ΑΡΕ
ΧΧ ΑΣΣΟΧΙΑΤΕ∆ ΤΟ ΠΡΙΟΝ ∆ΙΣΕΑΣΕ.
ΦΤ ΣΙΓΝΑΛ 1 22
ΦΤ ΧΗΑΙΝ 23 230 ΜΑϑΟΡ ΠΡΙΟΝ ΠΡΟΤΕΙΝ.
ΦΤ ΠΡΟΠΕΠ 231 253 ΡΕΜΟςΕ∆ ΙΝ ΜΑΤΥΡΕ ΦΟΡΜ (ΒΨ ΣΙΜΙΛΑΡΙΤΨ).
ΦΤ ΛΙΠΙ∆ 230 230 ΓΠΙ−ΑΝΧΗΟΡ (ΒΨ ΣΙΜΙΛΑΡΙΤΨ).
ΦΤ ΧΑΡΒΟΗΨ∆ 181 181 Ν−ΛΙΝΚΕ∆ (ΓΛΧΝΑΧ...) (ΠΡΟΒΑΒΛΕ).
ΦΤ ∆ΙΣΥΛΦΙ∆ 179 214 ΒΨ ΣΙΜΙΛΑΡΙΤΨ.
ΦΤ ∆ΟΜΑΙΝ 51 91 5 Ξ 8 ΑΑ ΤΑΝ∆ΕΜ ΡΕΠΕΑΤΣ ΟΦ Π−Η−Γ−Γ−Γ−Ω−Γ−
ΦΤ Θ.
ΦΤ ΡΕΠΕΑΤ 51 59 1.
ΦΤ ΡΕΠΕΑΤ 60 67 2.
ΦΤ ΡΕΠΕΑΤ 68 75 3.
ΦΤ ΡΕΠΕΑΤ 76 83 4.
ΦΤ ΡΕΠΕΑΤ 84 91 5.
ΦΤ ΙΝ ΠΑΤΙΕΝΤΣ ΩΗΟ ΗΑςΕ Α ΠΡΠ ΜΥΤΑΤΙΟΝ ΑΤ
ΦΤ ΧΟ∆ΟΝ 178: ΠΑΤΙΕΝΤΣ ΩΙΤΗ ΜΕΤ ∆ΕςΕΛΟΠ ΦΦΙ,
ΦΤ ΤΗΟΣΕ ΩΙΤΗ ςΑΛ ∆ΕςΕΛΟΠ Χϑ∆).
ΦΤ /ΦΤΙδ=ςΑΡ_006467.
ΦΤ ςΑΡΙΑΝΤ 171 171 Ν −> Σ (ΙΝ ΣΧΗΙΖΟΑΦΦΕΧΤΙςΕ ∆ΙΣΟΡ∆ΕΡ).
ΦΤ /ΦΤΙδ=ςΑΡ_006468.
ΦΤ ςΑΡΙΑΝΤ 178 178 ∆ −> Ν (ΙΝ ΦΦΙ ΑΝ∆ Χϑ∆).
ΦΤ /ΦΤΙδ=ςΑΡ_006469.
ΦΤ ςΑΡΙΑΝΤ 180 180 ς −> Ι (ΙΝ Χϑ∆).
ΦΤ /ΦΤΙδ=ςΑΡ_006470.
ΦΤ ςΑΡΙΑΝΤ 183 183 Τ −> Α (ΙΝ ΦΑΜΙΛΙΑΛ ΣΠΟΝΓΙΦΟΡΜ
ΦΤ ΕΝΧΕΠΗΑΛΟΠΑΤΗΨ).
ΦΤ /ΦΤΙδ=ςΑΡ_006471.
ΦΤ ςΑΡΙΑΝΤ 187 187 Η −> Ρ (ΙΝ ΓΣΣ).
ΦΤ /ΦΤΙδ=ςΑΡ_008746.
ΦΤ ςΑΡΙΑΝΤ 188 188 Τ −> Κ (ΙΝ ΕΟΑ∆; ∆ΕΜΕΝΤΙΑ ΑΣΣΟΧΙΑΤΕ∆ ΤΟ
ΦΤ ΠΡΙΟΝ ∆ΙΣΕΑΣΕΣ).
ΦΤ /ΦΤΙδ=ςΑΡ_008748.
ΦΤ ςΑΡΙΑΝΤ 188 188 Τ −> Ρ.
ΦΤ /ΦΤΙδ=ςΑΡ_008747.
ΦΤ ςΑΡΙΑΝΤ 196 196 Ε −> Κ (ΙΝ Χϑ∆).
ΦΤ /ΦΤΙδ=ςΑΡ_008749.
ΦΤ /ΦΤΙδ=ςΑΡ_006472.
ΣΘ ΣΕΘΥΕΝΧΕ 253 ΑΑ; 27661 ΜΩ; 43∆Β596ΒΑΑΑ66484 ΧΡΧ64;
ΜΑΝΛΓΧΩΜΛς ΛΦςΑΤΩΣ∆ΛΓ ΛΧΚΚΡΠΚΠΓΓ ΩΝΤΓΓΣΡΨΠΓ ΘΓΣΠΓΓΝΡΨΠ ΠΘΓΓ
ΓΓΩΓΘΠ ΗΓΓΓΩΓΘΠΗΓ ΓΓΩΓΘΠΗΓΓΓ ΩΓΘΠΗΓΓΓΩΓ ΘΓΓΓΤΗΣΘΩΝ ΚΠΣΚΠΚΤΝ
ΜΚ ΗΜΑΓΑΑΑΑΓΑ ςςΓΓΛΓΓΨΜΛ ΓΣΑΜΣΡΠΙΙΗ ΦΓΣ∆ΨΕ∆ΡΨΨ ΡΕΝΜΗΡΨΠΝΘ ςΨ
ΨΡΠΜ∆ΕΨΣ ΝΘΝΝΦςΗ∆Χς ΝΙΤΙΚΘΗΤςΤ ΤΤΤΚΓΕΝΦΤΕ Τ∆ςΚΜΜΕΡςς ΕΘΜΧΙΤΘΨ
ΕΡ ΕΣΘΑΨΨΘΡΓΣ ΣΜςΛΦΣΣΠΠς ΙΛΛΙΣΦΛΙΦΛ
ΙςΓ
//
ΧΧ −!− ∆ΙΣΕΑΣΕ: ΠΡΠ ΙΣ ΦΟΥΝ∆ ΙΝ ΗΙΓΗ ΘΥΑΝΤΙΤΨ ΙΝ ΤΗΕ
ΧΧ ΒΡΑΙΝ ΟΦ ΗΥΜΑΝΣ ΑΝ∆ ΑΝΙΜΑΛΣ ΙΝΦΕΧΤΕ∆
ΧΧ ΩΙΤΗ ΝΕΥΡΟ∆ΕΓΕΝΕΡΑΤΙςΕ ∆ΙΣΕΑΣΕΣ ΚΝΟΩΝ ΑΣ
ΧΧ ΤΡΑΝΣΜΙΣΣΙΒΛΕ ΣΠΟΝΓΙΦΟΡΜ ΕΝΧΕΠΗΑΛΟΠΑΤΗΙΕΣ ΟΡ ΠΡΙΟΝ Χ
Χ ∆ΙΣΕΑΣΕΣ,ΛΙΚΕ: ΧΡΕΥΤΖΦΕΛ∆Τ−ϑΑΚΟΒ ∆ΙΣΕΑΣΕ (Χϑ∆),
ΧΧ ΓΕΡΣΤΜΑΝΝ−ΣΤΡΑΥΣΣΛΕΡ ΣΨΝ∆ΡΟΜΕ (ΓΣΣ), ΦΑΤΑΛ
ΧΧ ΦΑΜΙΛΙΑΛ ΙΝΣΟΜΝΙΑ (ΦΦΙ) ΑΝ∆ ΚΥΡΥ ΙΝ ΗΥΜΑΝΣ;
ΧΧ ΣΧΡΑΠΙΕ ΙΝ ΣΗΕΕΠ ΑΝ∆ ΓΟΑΤ; ΒΟςΙΝΕ ΣΠΟΝΓΙΦΟΡΜ
ΧΧ ΕΝΧΕΠΗΑΛΟΠΑΤΗΨ (ΒΣΕ) ΙΝ ΧΑΤΤΛΕ; ΤΡΑΝΣΜΙΣΣΙΒΛΕ
ΧΧ ΜΙΝΚ ΕΝΧΕΠΗΑΛΟΠΑΤΗΨ (ΤΜΕ); ΧΗΡΟΝΙΧ ΩΑΣΤΙΝΓ
ΧΧ ∆ΙΣΕΑΣΕ (ΧΩ∆) ΟΦ ΜΥΛΕ ∆ΕΕΡ ΑΝ∆ ΕΛΚ; ΦΕΛΙΝΕ
ΧΧ ΣΠΟΝΓΙΦΟΡΜ ΕΝΧΕΠΗΑΛΟΠΑΤΗΨ (ΦΣΕ) ΙΝ ΧΑΤΣ ΑΝ∆
ΧΧ ΕΞΟΤΙΧ ΥΝΓΥΛΑΤΕ ΕΝΧΕΠΗΑΛΟΠΑΤΗΨ (ΕΥΕ) ΙΝ
ΧΧ ΝΨΑΛΑ ΑΝ∆ ΓΡΕΑΤΕΡ ΚΥ∆Υ. ΤΗΕ ΠΡΙΟΝ ∆ΙΣΕΑΣΕΣ
ΧΧ ΙΛΛΥΣΤΡΑΤΕ ΤΗΡΕΕ ΜΑΝΙΦΕΣΤΑΤΙΟΝΣ ΟΦ ΧΝΣ
ΧΧ ∆ΕΓΕΝΕΡΑΤΙΟΝ: (1) ΙΝΦΕΧΤΙΟΥΣ (2)
ΧΧ ΣΠΟΡΑ∆ΙΧ ΑΝ∆ (3) ∆ΟΜΙΝΑΝΤΛΨ ΙΝΗΕΡΙΤΕ∆ ΦΟΡΜΣ.
ΧΧ ΤΜΕ, ΧΩ∆, ΒΣΕ, ΦΣΕ, ΕΥΕ ΑΡΕ ΑΛΛ ΤΗΟΥΓΗΤ ΤΟ
ΧΧ ΟΧΧΥΡ ΑΦΤΕΡ ΧΟΝΣΥΜΠΤΙΟΝ ΟΦ ΠΡΙΟΝ−ΙΝΦΕΧΤΕ∆
ΧΧ ΦΟΟ∆ΣΤΥΦΦΣ.
∆Ρ ΕΜΒΛ; Μ13667; ΑΑΑ19664.1; −.
∆Ρ ΕΜΒΛ; Μ13899; ΑΑΑ60182.1; −.
∆Ρ ΕΜΒΛ; ∆00015; ΒΑΑ00011.1; −.
∆Ρ ΠΙΡ; Α05017; Α05017.
∆Ρ ΠΙΡ; Α24173; Α24173.
∆Ρ ΠΙΡ; Σ14078; Σ14078.
∆Ρ Π∆Β; 1Ε1Γ; 20−ϑΥΛ−00.
∆Ρ Π∆Β; 1Ε1ϑ; 20−ϑΥΛ−00.
∆Ρ Π∆Β; 1Ε1Π; 20−ϑΥΛ−00.
∆Ρ Π∆Β; 1Ε1Σ; 21−ϑΥΛ−00.
∆Ρ Π∆Β; 1Ε1Υ; 20−ϑΥΛ−00.
∆Ρ Π∆Β; 1Ε1Ω; 20−ϑΥΛ−00. ∆Ρ ΜΙΜ; 176640; −.
∆Ρ ΜΙΜ; 123400; −.
∆Ρ ΜΙΜ; 137440; −.
∆Ρ ΜΙΜ; 245300; −.
∆Ρ ΜΙΜ; 600072; −.
∆Ρ ΜΙΜ; 604920; −.
∆Ρ ΙντερΠρο; ΙΠΡ000817; Πριον.
∆Ρ Πφαµ; ΠΦ00377; πριον; 1.
∆Ρ ΠΡΙΝΤΣ; ΠΡ00341; ΠΡΙΟΝ.
∆Ρ ΣΜΑΡΤ; ΣΜ00157; ΠΡΠ; 1.
∆Ρ ΠΡΟΣΙΤΕ; ΠΣ00291; ΠΡΙΟΝ_1; 1.
∆Ρ ΠΡΟΣΙΤΕ; ΠΣ00706; ΠΡΙΟΝ_2; 1.
ΚΩ Πριον; Βραιν; Γλψχοπροτειν; ΓΠΙ−ανχηορ; Ρεπεατ; Σιγναλ;
ΚΩ 3∆−στρυχτυρε; Πολψµορπηισµ; ∆ισεασε µυτατιον.

12. What is Knowledge?
• Knowledge – all information
and an understanding to
carry out tasks and to infer
new information
• Information -- data
equipped with meaning
• Data -- un-interpreted
signals that reach our
senses
Michael Ashburner
Professor
University of Cambridge
UK
I
S
M
B
Name
Job
Institution
Country
C
o
n
f
man
academic, senior
ancient university, 5 rated
European
important figure in biology
B
I
O
L
O
G
Y

13. A Knowledge Based Discipline
• Rather than laws captured in mathematics….
• We have lots of facts: the discipline’s knowledge
• Rather than “calculating” what a protein does, we
investigate and write it down
• Equivalent to writing down the trajectories of all
thrown objects and not doing ballistics!
• To do biology one needs “the knowledge”

14. Heterogeneity
• 28 ways to format the representations of a biological
sequence
• Though one way to represent the bases or amino
acids…
• Different words same concept
• Different concepts same words
• Different and implicit data schema

15. Categories and Category Labels
GO:0000368
U2-type nuclear mRNA 5' splice site recognition
spliceosomal E complex formation
spliceosomal E complex biosynthesis
spliceosomal CC complex formation
U2-type nuclear mRNA 5'-splice site recognition

16. An Identity Crisis
• Database entries have identifiers unique within their
database
• The type of entity described in an entry doesn’t have
an identifier
• Different entries about the same type talk about it
differently
• How do we know when an entry in one DB talks
about the same thing as another entry in another
DB?
• That’s the skill of a bioinformatician

17. Why: Society of Biologists
• To do particle physics necessarily has central
organisation
• One central place to generate data
• A communitarian attitude
• It is still possible to do biology in the “garden shed”
• Historicaly less need to organise
• Hence…

18. Navigating the Web of
Knowledge in Bioinformatics

19. Biology is Special
• Large quantities of data: No it doesn’t
• Complex data: Yes it does
• Volatile data: Types of data and what is recorded
changes rapidly
• Nothing that special about biology
• …except that it has all the problem and often to a
large degree

20. Lots of catalogues
Genome
Proteome
Transcriptome
Interactome
Metabolome
PHENOME

21. Biology now has lots of facts

22. Creating Woods, not Trees
Genes
Proteins
Pathways
Interactions
Literature
Complex
Machines
Virtual
Organism
…. from biological facts, we make a system that is some model of a real organism

23. Networks of Chemicals
Image: http://genome-www.stanford.edu/rap_sir/images/Web_FigF_RAP1_glycolysis.gif

24. Systems within Systems
Image: http://www.ehponline.org/members/2007/10373/fig1.jpg

25. A Biologist’s Skills
• By the time a biologist has finished a Ph.D. he/she is
about ready for action
• They have a comprehensive knowledge of the facts
of a (narrow) domain
• He/she also knows how to do experimentation in that
domain
• There are so many facts, it is difficult to move outside
one’s sub-discipline
• Yet in a systems view such movement is mandatory

26. The Role of Knowledge
• A lot of facts
• Perhaps organised into a system
• No equivalent of “laws of mechanics” – we
can’t do this biology with mathematics
• Or at least not without knowing what the
numbers mean...
• This is why we’ve been using ontologies!

27. What is an Ontology?
• A description of that which exists (in our data)
• What it means to be a member of a category
• What categories of things exist and how do I
recognise that a particular object is a member of a
given category

28. Uses of Ontology in Bioinformatics

29. Why develop an ontology?
• To make domain assumptions explicit
– Easier to change domain assumptions
– Easier to understand and update legacy data
• To separate domain knowledge from operational knowledge
– Re-use domain and operational knowledge
separately
• A community reference for applications
• To share a consistent understanding of what information means.

30. History of Bio-ontologies
1992 1996 1998
TAMBIS
2002
MGED
2006
1st
Bio-ontologies
meeting
Gene Ontology
starts
2005

31. Controlled Vocabulary
• An Ontology isn’t a controlled vocabulary, but can be
used to deliver one
• By agreeing upon the categories in a domain and
agreeing upon their labels we are controlling
vocabulary
• Addresses one major problem in biology
• Also forces examination of definitions
• Makes domain assumptions explicit

32. Transferring Characteristics
Uncharacterised protein
Tra1 La2 La3
High similarity transfer characteristics

33. Post-Genomic Biology
• Fly, mouse, yeast, worm all have their own
terminologies
• I want to compare genomes
• How?
• The genomic sequence is easily dealt with
computationally and comparisons are easy
• This is not true of the annotations or knowledge of
those sequences
• Need a common understanding

34. Annotation of Data
• Big effort to create controlled vocabularies using
ontologies
• A huge annotation efffort – describe the entities in DB
with terms from ontologies
• The Gene Ontology (http://www.geneontology.org))
• The Open Biomedical Ontologies Consortiym

35. Genotype Phenotype
Sequence
Proteins
Gene products Transcript
Pathways
Cell type
BRENDA tissue /
enzyme source
Development
Anatomy
Pheonotype
Plasmodium
life cycle
-Sequence types
and features
-Genetic Context
- Molecule role
- Molecular Function
- Biological process
- Cellular component
-Protein covalent bond
-Protein domain
-UniProt taxonomy
-Pathway ontology
-Event (INOH pathway
ontology)
-Systems Biology
-Protein-protein
interaction
-Arabidopsis development
-Cereal plant development
-Plant growth and developmental stage
-C. elegans development
-Drosophila development FBdv fly
development.obo OBO yes yes
-Human developmental anatomy, abstract
version
-Human developmental anatomy, timed version
-Mosquito gross anatomy
-Mouse adult gross anatomy
-Mouse gross anatomy and development
-C. elegans gross anatomy
-Arabidopsis gross anatomy
-Cereal plant gross anatomy
-Drosophila gross anatomy
-Dictyostelium discoideum anatomy
-Fungal gross anatomy FAO
-Plant structure
-Maize gross anatomy
-Medaka fish anatomy and development
-Zebrafish anatomy and development
-NCI Thesaurus
-Mouse pathology
-Human disease
-Cereal plant trait
-PATO PATO attribute and value.obo
-Mammalian phenotype
-Habronattus courtship
-Loggerhead nesting
-Animal natural history and life history
eVOC (Expressed
Sequence Annotation
for Humans)

36. The Sequence
Ontology
(http://obo.sf.net)

38. GO in Analysis
• Microarray analysis one of the original visions for GO
• Clustering of modulated genes cluster about
functional attributes of their proteins
• GO also used in, for example, semantic similarity;
text analysis; etc.

39. Fact Management
• When “stamp collecting” we’re collecting facts
• Biology is a fact management activity
• Knowing what these fact mean is very import
• Science is perofrmed on data and the smeantics of
data enable us to do science
• Semantic e-Science

40. Summary
• The nature of modern biology gives it interesting
knowledge (fact) management issues
• It is a knowledge based discipline
• Not unique, but often extreme
• Ontologies seen as one component in management
(but not a panacea)

41. acknowledgements
• All these people provided slides and input:
• Duncan Hull
• Simon Jupp
• Phil Lord
• Carole goble

42. Genotype to Pathway
Created by Paul Fisher

43. Pathway to Phenotype
Created by Paul Fisher

44. Ontology Space
(Axiomatic)Richness
Usage
Representation

45. Metadata toilet
• Everyone wants to use good metadata but few people want to
spend time curating and cleaning metadata
– Like a clean toilet

46. Biologists Wake up to Standards