Hi Bill,
Thanks for all of your great feedback. :-)
The folks at Lilly who developed the ontology did review a number of
existing ontologies, but they didn't meet our needs. I don't have
the full
list of ontologies that they explored, but they definitely took a
look at
OBI. We are very interested in working with the community to further
develop the ontology, and are in the process of scheduling a call
with some
of the OBI folks.
Cheers,
Susie
Bill Bug
<[EMAIL PROTECTED]
edu> To
Susie Stephens
12/06/2007 11:16 <[EMAIL PROTECTED]>
PM cc
Matthias Samwald
<[EMAIL PROTECTED]>,
"public-semweb-lifesci@w3.org
hcls"
<public-semweb-
[EMAIL PROTECTED]>, Kei
Cheung <[EMAIL PROTECTED]>,
"Karen (NIH/NIDA) [E] Skinner"
<[EMAIL PROTECTED]>, Alan
Ruttenberg
<[EMAIL PROTECTED]>
Subject
Re: Experiment Ontology
Hi Susie,
We certainly do need an "Experiment Ontology" - or Ontology of
Biomedical
Investigation (OBI).
I believe Matthias, Michael, and Kei have all made exactly the
points I
think are most important to consider:
1) Matthias's comments
Are you following "best practices" in creating the ontology. I
believe
Matthias gives many instructive examples on how to adjust what is
here to
bring it much more in sync with the emerging "best practices" that are
coming out of the community development surrounding a variety of OBO
Foundry ontologies. Matthias also makes the point that its
important to
seek to re-use (or directly contribute to) the emerging community
ontologies to cover the required domains. In the case of this
particular
Experiment Ontology, the ontologies to consider are Ontology of
Biomedical
Investigation (OBI), the OBO Relations Ontology, the Gene Ontology
(specifically the Molecular Function and Cellular Component
branches, the
latter of which is designed to capture components down to the level of
macromolecular complexes), the Sequence Ontology, Protein Ontology
(nascent
- but proceeding rapidly), the Cell Ontology - at a minimum. As
many on
this list know - and I'm certain the talented folks at Lilly who
invested
time in assembling this ontology also learned - many of these are
not fully
ready for prime-time, and/or may not FULLY cover the breadth and
depth of
the domains a specific application requires. However, if one
doesn't seek
to work with these community efforts, you cannot expect to achieve the
ultimately goal, which is to make your data maximally "semantically
sticky", so as to ensure the least amount of custom logic and human
effort
will be required to get the most value from your data. Otherwise, you
stand the chance of creating what may be a useful ontology that
meets your
specific requirements (as has been true of "investigation"-oriented
ontologies that have come before such as the MAGE Ontology,
ExperiBase,
EXPO, myGRID KAVE, etc.), but don't help the community at-large to
appropriately re-use your data. In each case, these ontologies or KR
frameworks have been extremely useful in the local application
context for
which they were constructed, but they cannot be effectively employed
as the
basis for semantically-driven integration across data sets that may
not be
able to accept the constraints (or lack thereof) of this
application-oriented ontology.
Would you know off-hand, Susie, whether the folks who worked on this
ontology at Lilly have both reviewed the relevant community efforts
cited
above and/or have sought to interact with those groups to get some
input on
how best to meet the overall requirements that underlie this
particular
Experiment Ontology with the minimal required effort and in a manner
that
could help to ensure Lilly's sunk investment could be of benefit to
us all.
2) Michael's comments
It's very helpful to know what the target is when it comes to
exporting/exchanging the actual data. As Michael points out, a
great deal
of work has gone into the production of FuGE (and MaGE before it) to
come
up with the appropriate division of labor between the semantically-
opaque,
syntactical requirements as represented in a data model such as MaGE
or
FuGE and the explicit semantics as captured in the ontology. For
those
using FuGE, as Michael states, in the realm of syntax, the intention
for
FuGE is to provide a shared structure for universal elements such as
biomaterials, experiment populations/pools/groups, protocol details,
reagents details, etc.. Built on that shared, generic foundation, any
specific discipline - e.g., microarray expression, GC-MS, FISH, MRI,
etc. -
can sub-class FuGE components and add what additional detail
required in
their discipline. In parallel with this effort on data structure,
the OBI
ontology cooperative seeks to provide that same foundation for the
shared
semantic domains, and a clear set of recommended practices for how to
re-use entities from other OBO Foundry ontologies such as ChEBI,
Sequence
Ontology, Protein Ontology, OBO Cell, Organism Taxonomy (OWL
versions of
NCBI Tax), etc. to specify the critical biomedical entities and their
complex relations. As I say above, these are works in progress.
For those
of us who must have something working now, the recommended practice
is to
actively participate in these projects with an eye toward following
their
practice - and replacing any "proxy" you create in the interim with
the
community ontology, when it is ready for use. This is what we have
done in
the BIRN ontology BIRNLex. We actually have an OWL module called
"BIRNLex-OBI-Proxy.owl" which we fully intend to replace with OBI
entities,
when they are ready for use. We also have "BIRNLex-
Investigation.owl" that
builds on this "proxy" to cover entities BIRN researchers must
capture. We
expect to eventually see the contents of "BIRNLex-Investigation" in
OBI in
some form. We intend to "contribute" those elements from this OWL
file
directly to OBI, when OBI is ready for them, and we have the time work
through this migration process.
3) Kei's comments
Examples - examples - examples. This is critical. Working through
the
example Kei cites from the NIH Neuroscience Microarray Consortium is a
wonderful way to determine whether:
- there are existing community ontologies that can meet the KR and
processing requirements
- where the gaps are in those community ontologies
- whether the ontology you are creating effectively fills those gaps
(if it
does, that makes it very clear how the community effort can make
effective
use of your ontology)
In regards to Gene Lists, Kei is certainly correct. If these are
captured
through algorithmic means, it's critical to capture the details on
that
algorithm - typically both the version of the algorithm as well as the
version of the data repository you ran it against.
Also - where gene entities are concerned - there is ongoing work
between
the GO groups, the Sequence Ontology, and the Protein Ontology that is
particularly targeted toward capturing the specific relations
between types
of genomic sequence elements and types of biologically active
protein-based
molecules (e.g., macromolecular complexes composed of a collection of
proteins in a variety of post-translationally modified states -
e.g., GPC
receptors, ion channels, transporters, pathway enzymes, etc. - i.e.,
Rx
drug targets). These are the details we'll all require in order to do
round-trip pharmacogenetics - i.e.,effects of genetic constructs on
target susceptibility to drugs - AND - the ways in which drugs
ultimately
alter macromolecular complexes by leading to changes in gene
expression.
Just my $0.02 filtering on these helpful comments from Matthias,
Michael,
and Kei.
Cheers,
Bill
On Dec 3, 2007, at 1:00 PM, Kei Cheung wrote:
This is great!
I have a microarray experiment description (that has to do with
Alzheimer Disease) extracted from NINDS microarray consortium:
http://arrayconsortium.tgen.org/np2/viewProject.do?action=viewProject&projectId=433773
I just wonder how this example would fit this experiment
ontology (as
well as others such as OBI) As shown in this example, we record
information such as organ type, organ region, cell type (layer II
pyramidal neuron), etc. NINDS microarry consortium uses different
array platforms (e.g., agilent, Affymetrix, and cDNA) for
different
organisms so one may need to divide chips into groups
corresponding
to different platform types. Each group can then be further
divided
into subgroups corresponding to different organisms.
We also would like to capture gene lists (not the raw gene
lists but
the ones (much shorter) that indicate what genes are over/under
expressed under certain experimental conditions). Such gene lists
would usually be extracted from the literature. Also the analysis
package (including version) that was used to generate a gene list
should be identified. One possible use of these gene lists is to
compare them to identify genes are differentially expressed
under the
same/similar experimental condition across different microarray
experiments. This would help identify true signals from noises.
Hope it helps.
Cheers,
-Kei
Matthias Samwald wrote:
Hi Susie,
Susie wrote:
It would be great if you could take a look at it and
provide comments. The
ontology is available at:
http://esw.w3.org/topic/HCLSIG_BioRDF_Subgroup/Tasks/Experiment_Ontology
* Some of the entities/properties are missing a
rdfs:label or
have an empty label (a string with lenght 0).
* Some of the entities could be taken from existing
ontologies
like OBI, RO or some of the OBO Foundry ontologies. This
would
save work and makes integration with other data sources and
ontologies much easier. By the way, there seem to be
several
groups working on ontologies for mircoarray experiments,
or are
at least planning to do that. It would be great if these
groups
could work together.
* The class 'Chip type' should be removed and be replaced
by
subclasses of 'chip', e.g., 'chip (human)', 'chip
(mouse)' etc.
* Some of the object properties appear like they are
intended
to be datatype properties (e.g., 'has proteome id').
* Many of the datatype properties could be replaced with
object
properties, possibly referring to third party ontologies
-- of
course this would require a richer ontology and more work
spent
on creating mappings. 'has molecular function' could
refer to
entities from the gene ontology, 'has associated organ'
could
refer to an ontology about anatomy and so on.
* Object properties and their ranges are quite redundant.
Property 'has reagent' has range 'Reagent', property 'has
treatment' has range'Treatment' and so on. Maybe the
ontology
could be designed in such a way that there are only some
generic properties such as 'has part'. This would make the
ontology much easier to maintain, query and understand in
the
long term.
* It is unclear how 'Gene list' is intended to be used.
* 'Hardware' and 'Software' should not be subclasses of
'Protocol'.
Many of the datatype properties in this ontology look very
interesting and might provide requirements for other
ontologies. It would be great if some of them could be
described/commented in more detail so that we know more
about
the requirements that motivated the creation of these
properties.
I hope that was somewhat helpful.
cheers,
Matthias Samwald
William Bug, M.S., M.Phil.
email:
[EMAIL PROTECTED]
Ontological Engineer (Programmer Analyst III) work: (610) 457-0443
Biomedical Informatics Research Network (BIRN)
and
National Center for Microscopy & Imaging Research (NCMIR)
Dept. of Neuroscience, School of Medicine
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093
Please note my email has recently changed
