Archive for February, 2010

NIF Standard Ontologies NIFSTD 1.7 Released

Posted on February 26th, 2010 in News & Events | 1 Comment »

We are pleased to announce the new release of NIFSTD 1.7. Please visit http://purl.org/nif/ontology/nif.owl to access the owl file.

Major changes of this release include:

  • Retirement of OBO-UBO layer: Eliminated the need of BIRNLex-OBO-UBO module (a common bridge between all NIF modules and BFO) to be imported
  • Content Enhancement and Improvements:
    • Inclusion of Protein Ontology (PRO) under NIF-Molecule module.
    • Molecules hierarchy has been modified to reflect close alignment between NIF’s Chemical and CHEBI’s upper level hierarchies
    • More neuron labels are altered in NIF-Cell module to conform with standardized naming convention by NIF cell working group
    • Additional partonomy relations for NIF-Anatomy
    • New classes and annotations from NeuroLex wiki contributions in different modules
  • Neuron by Brain Region classification: Another bridge file (between NIF-Cell, NIF-Subcellular, and NIF-Anatomy) has been constructed based on NeuroLex contributions by NIF-Cell working group.

Feel free to take a look at the detailed NIFSTD 1.7 release notes and tell us what you think!

NIF Webinar – February 9, 2010 / Topic: Defined versus Asserted Classes

Posted on February 4th, 2010 in News & Events, Webinar Announcement | 1 Comment »

The Neuroscience Information Framework (NIF) hosts Webinar series on topics focused on collaborating with NIF, getting involved in building the NIF vocabulary, using NIF portal resources, as well as other appropriate NIF topics.

Our next NIF Webinar is scheduled for February 9th, 2010. Please join Dr. Maryann Martone, Principal Investigator of the NIF Project, and Fahim Imam, Ontology Engineer from UC San Diego,  for an informative session on the NIF Standard Ontology. Details follow.

Date and Time: Tuesday, February 9, 2010 • 11:00-12:00 PST
Topic: Defined versus Asserted Classes:  Working with the NIF OWL Ontologies
Presenters: Dr. Maryann Martone and Fahim Imam
URL: http://connect.neuinfo.org/webinar
Dial-In (toll-free): 866-740-1260
Access Code: 8220739

The NIF project has established a set of modular ontologies covering many of the major domains of relevance to neuroscience, the NIF Standard Ontology (NIFSTD).  This Webinar will focus on NIF’s approach to the asserted vs inferred hierarchy in the NIFSTD ontology and how NIF builds up more complicated relationships among NIF modules while keeping modularity intact.  When it comes to asserted hierarchy of classes, NIFSTD ontologies took the single inheritance principle which is also an important OBO foundry recommendation.  This principle allows us to have the classes that are univocal and unambiguous within the core NIFSTD modules. We believe that this principle is often misunderstood to mean that you can only have a single hierarchy in your ontology.  However, through the use of logical definitions with necessary and sufficient conditions, multiple parents can be inferred using automated reasoning.  This saves a great deal of manual labor and provides a logical reason as to how that class may exist in different hierarchies.  In this Webinar, we will provide examples including the NIF’s inferred hierarchy of neurons by neurotransmitter and by soma location.

We look forward to seeing you there.

Defining Adulthood

Posted on February 3rd, 2010 in Curation, General information | 1 Comment »

THE PROBLEM

Adulthood, like many terms we use for describing data, is a very poorly defined and a somewhat arbitrary concept. When does an organism become an adult? The answer in general would be “it depends on how you define adult.” In the highly charged world of scientific discourse, people may argue correctly that there is no single definition of adult that would satisfy everyone or that there is a magical time point at which it occurs. The question for the Neuroscience Information Framework or any other group attempting to integrate data from many sources is not whether one group of definitions is correct, but rather whether such a concept is useful for comparing and understanding data.

To illustrate this point, MGI or the mouse genome informatics project, which is the place to go for all things mouse (from mouse strains to ontologies and genes), does not define the term adult, because of the disagreement among scientists as to what constitutes the break between juvenile and adult mice (personal communication). Of course MGI does have the “adult brain ontology”, among other resources labeled with the term adult. So they use the term as it is useful and describes a set of organismal characteristics, but are unwilling to define the term due to the ambiguities in the definitions.

Other large datasets, such as the Allen Brain Atlas do not deal with these sorts of definitions; rather they take data only from postnatal day 55 animals, which they consider safely within the adult range.

In an ideal world, we would provide a standard set of organism attributes for every subject used that is provided in a computable form, e.g., age, weight, sexual maturity. Anyone would therefore request data only from those subsets of animals that were comparable, e.g., between ages 30 days and 90 days and between 100g – 200g. Within a given resource, e.g., database, one can easily set up such a system. However, for a system like NIF that searches across broad swaths of information contained in individual databases, XML files, HTML pages and text, it is currently impossible to provide such a universal computational service on the fly even for something that should be conceptually simple, e.g., representation of age (days, months, years, prenatal, embryonic etc). Nevermind the fact that such information is not consistently available for a source.

A consideration of the literature shows that many times the only label for age is “adult” with no specifics provided.

For databases that take and analyze data from published work, like neuromorpho.org, the word adult is the only age that accurately describes a particular data set. Automated systems recognize this term, but if the definition is not constant across sources, the “adult” is not a useful bucket for aggregating information. One source may have adult as starting at P21 while another at P30. Furthermore automated systems would not be able to translate “P55” as adult, or “week 5” into adulthood unless there was a definition that could be applied.

DEFINITION OF ADULT

The question is whether we can come up with a definition of adulthood that can be consistently applied. Most of the biological definitions of adulthood deal with the readiness of an organism to reproduce, sexual maturity, or the notion that an animal is full-grown. Both definitions have inherent problems. For example, many species including male rats do not stop growing until death, making “full-size” only applicable when animals have reached their death. Similarly, sexual maturity may be defined as the onset of estrus, but can also be defined as the termination of ‘pubescence’ a period of time that is difficult to access in a rat or mouse.

Adding a little complexity to the problem is the relatively simple question of what is the day of birth. Scientists from various entrenched camps define postnatal day zero as the day of birth and others define it as postnatal day one. Neither group is incorrect, but anyone attempting to bring together data from various datasets (or publications) is required to spend a large amount of time attempting to understand whether the particular piece of data comes from an animal that is P5 or P4.

Due to the inherent problems in defining such a thing, the ontology community (a community concerned with establishing standards in discourse in scientific communication) and many researchers that build databases meant to compare data from various sources treat adulthood with caution. Nonetheless, as evidenced by its wide use, the concept of “adult” is useful and often stands alone as an important characteristic for defining data even though it is not well defined for any species.

THE ARBITRARY BUT DEFENSIBLE SOLUTION

The above-mentioned problems with defining adulthood are echoed and magnified in humans, because of a need to access emotional maturity and readiness to take on the tasks of independent existence in a complex society.   The solution to determining what an adult human is has been strangely simple and boils down to a number.  Any parent of a teenager knows that there is no magical event that happens on the 18th birthday of a child, but for legal systems a hard cut-off is needed, so that treatment of criminal activities and rights bestowed on individuals are clearly defined.  Therefore in almost all advanced societies the legal adult is 18 years of age, whether or not they are emotionally ready to be one or whether or not the pubertal period has passed.

We suggest that a similar arbitrary but defensible cut-off date should be established and implemented for all research animals so that when age of animal is reported as “adult” we can, with some degree of certainty, compare data of one study to the thousands of other similar studies.

According to the work of Finlay and Darlington (Science, 268:1578-84) with the chronometry of species, the final important steps in brain development of mice occur 29.7 days after conception, or postnatal day 12 (birth is P0 in this case), menstruation typically begins between postnatal day 25 and 40 and body growth is completed at about age postnatal day 50.  So we can use the arbitrary date of postnatal day 50 as the definition of adult mouse, as this is a reasonable standard for an adult.  We will define the day of birth as postnatal day 0.  Mice between the age of P0 and P24 will be termed juvenile and mice between P25 and P49 should be termed early adult.

IN CONCLUSION

In the NIFSTD (Neuroscience Information Framework standard ontology) we will define arbitrary but defensible standards for mice and other common research species as this sort of standard is an important part of establishing a common framework in discussion, and not necessarily dealing with the absolute scientific truth.

The reason that we need a standard for age and many other such common terms is that we need to establish a point of reference, which will allow for accurate communication about results.  This is presumably the reason that the standard international system of units (SI) was put in place and we believe in the standardization of certain common variables in experiments for the sake of effective data analysis.