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AMIA Annu Symp Proc. 2005; 2005: 61–65. | PMCID: PMC1560846 |
Copyright This is an Open Access article: verbatim copying and redistribution of
this article are permitted in all media for any purpose Of Mice and Men: Aligning Mouse and Human Anatomies Olivier Bodenreider, MD, PhD,1 Terry F. Hayamizu, MD, PhD,2 Martin Ringwald, PhD,2 Sherri De Coronado, MS, MBA,3 and Songmao Zhang, PhD1 1 National Library of Medicine, Bethesda, Maryland 2 The Jackson Laboratory, Bar Harbor, Maine 3 National Cancer Institute, Bethesda, Maryland This paper reports on the alignment between mouse and human anatomies, a
critical resource for comparative science as diseases in mice are used
as models of human disease. The two ontologies under investigation
are the NCI Thesaurus (human anatomy) and the Adult Mouse Anatomical Dictionary, each
comprising about 2500 anatomical concepts. This study
compares two approaches to aligning ontologies. One is fully automatic, based
on a combination of lexical and structural similarity; the other
is manual. The resulting mappings were evaluated by an expert. 715 and 781 mappings
were identified by each method respectively, of which 639 are
common to both and all valid. The applications of the mapping
are discussed from the perspective of biology and from that of ontology. Comparing gene-related information across model organisms is crucial to
understanding similarities and differences among species. The availability
of fully-sequenced genomes for an increasing number of model organisms
enables comparisons across species. At the sequence level, tools
such as BLAST help identify orthologous genes based on sequence similarity. Analogously, functional genomics uses the annotations of gene
products (e.g., to the Gene Ontology) to compare genes. One element shared by all model organisms is anatomy. Anatomical structures
can be described at the subcellular (e.g., nucleus) and cellular (e.g., lymphocyte) level
as well as in terms of tissues (e.g., adipose
tissue), organs and organ parts (e.g., heart, aortic valve), body systems
and their components (e.g., central nervous system, brain) and entire
organisms. Anatomy is in fact central to the biomedical domain as
the description of virtually every biological process makes reference
to some anatomical structure. For example, the information stored about
microarray experiments routinely includes tissue and cell type. Many
representations of anatomy have been developed for various model organisms [ 1]. While some of them are mere lists of anatomical names for anatomical
entities (e.g., Terminologica Anatomica), others are full-fledged
ontologies, organizing anatomical entities in hierarchies ( isa, part of) in order to support reasoning (e.g., Foundational Model of Anatomy 1). The general framework of this study is that of ontology alignment. For
a survey of alignment techniques, the interested reader is referred to [ 2]. Lexical features (i.e., the names of anatomical entities) and
structural features (e.g., the relations described among anatomical
entities) can be used to compare representations of anatomies within and
across species. In previous research, we have developed methods for
comparing two representations of human anatomy [ 3]. The objective of this study is to align two ontologies of anatomy, for
human and mouse anatomy. In other words, we aim at identifying
equivalent anatomical entities between the NCI Thesaurus (human anatomy) and
the Adult Mouse Anatomical Dictionary (mouse anatomy). The
originality of this study lies in using two independent approaches (lexical
and manual), followed by a structural validation and a manual evaluation
of the results. This study is a contribution to the caBIG project 2. The two resources under investigation in this study are part of the Open
Biomedical Ontologies 3 (OBO). NCI Thesaurus Published by the National Cancer Institute (NCI), this thesaurus 4 contains the working terminology of many data systems in use at NCI [ 4]. Its scope is broad as it covers vocabulary for clinical care
as well as translational and basic research. Among its 37,386 concepts, 4,410 (11.8%) correspond to anatomical entities ( anatomic structure, system, or substance hierarchy). For example, the concept liver is identified by C12392 and has several synonyms (e.g., hepatic organ system). Additionally, liver is subsumed by organ and related to abdominal cavity ( has_location) and to gastrointestinal system ( is_physical_part_of). The version used in this study is version 04.09a (September 10, 2004). Adult Mouse Anatomical Dictionary The Adult Mouse Anatomical Dictionary 5 has been developed as part of the mouse Gene Expression Database (GXD) project [ 5] to provide standardized nomenclature for anatomical entities
in the postnatal mouse [ 6]. It will be used to annotate and integrate different types of
data pertinent to anatomy, such as gene expression patterns and phenotype
information, which will contribute to an integrated description of
biological phenomena in the mouse. The ontology contains more than 2,400 unique
terms, is structured as a directed acyclic graph (DAG) and
is organized hierarchically in both spatial and functional ways. For
example, the concept liver is identified by MA:0000358 and is a ‘child’ of (is_a) abdomen organ as well as part_of the liver/biliary system. The version used in this study was downloaded on December 22, 2004 (under
the name Mus adult gross anatomy in OBO). Of the 4,410 NCI Thesaurus terms, over 2,000 terms correspond to entities
that are not included in the Adult Mouse Anatomical Dictionary, such
as cell types and subcellular components. Thus, only about 2,400 would
be expected to be candidates for matching. The method used for aligning the NCI Thesaurus (NCI) and Adult Mouse Anatomical
Dictionary (MA) was originally developed by the authors for aligning
two ontologies of human anatomy: the Foundational Model of Anatomy (FMA) and
GALEN [ 3] and can be summarized as follows. Concept names and relations
are extracted from each ontology. In the lexical approach, additional synonyms are collected. All names are normalized and compared
across ontologies. Lexically similar names form the basis for identifying
equivalent concepts. Structural similarity (e.g., shared relations to other equivalent concepts) is required for
concepts to be aligned. Independently of the lexical approach, a manual alignment is performed, resulting in a different set of equivalent concepts. Structural similarity is also computed on this set in order to eliminate from the alignment
the concepts failing to be supported by structural evidence. The evaluation consists of comparing the sets of equivalent concepts resulting from each
approach. Equivalent concepts are reviewed manually by an expert for
accuracy. An overview of the methods is presented in . Lexical approach The lexical alignment identifies shared concepts across systems lexically
through exact match and after normalization. Concepts exhibiting similarity
at the lexical level across systems are called anchors, as they
are going to be used as reference concepts in the structural alignment. Additional
anchors are identified through UMLS synonymy. Two concepts
across systems are considered anchors if their names are synonymous
in the UMLS Metathesaurus (i.e., if they name the same UMLS concept) and
if the corresponding concept is in the anatomy domain (i.e., has
a semantic type related to Anatomy). For example, the NCI term triangular bone and the MA term ulnar carpal bone, although lexically different, were mapped through the synonymy of these
two terms in the UMLS Metathesaurus. Manual approach This approach utilized the search capabilities of the available web-based
ontology browsers for the NCI Thesaurus 6 and Adult Mouse Anatomical Dictionary 7 to identify potential matching concepts between the two ontologies. A
manual comparison of adult mouse anatomy terms against the entire NCI
Thesaurus was performed using lexical similarity and synonymy in both
sources. This process was repeated in order to match each mouse term against
a list of approximately 4,400 human anatomy terms provided by NCI. Additional
matches were identified by a domain expert (TH) familiar
with both mouse and human anatomy. All matches were then validated using
available anatomy reference sources. Validation by structural similarity The structural alignment first consists of acquiring the semantic relations
explicitly represented in each system. In order to facilitate the
comparison of relations across systems, the transitive closure of isa relations is computed in each system, as well as that of part_of relations. With these semantic relations, the structural alignment identifies
structural similarity among anchors across systems. Structural
similarity, used as positive structural evidence, is defined by the presence
of at least one common hierarchical relation among anchors across
systems, e.g., <c1, part_of, c2> in one system and <c1’, part_of, c2’> in another where {c1, c1’} and {c2, c2’} are anchors across systems. For example, the anchor
concepts triangular bone in the NCI Thesaurus and ulnar carpal bone in Mouse Anatomy, presented earlier, received positive structural evidence
because they share hierarchical links to some of the other anchors
across systems. As illustrated in , triangular bone is related to bone of the upper extremity (isa) and to upper extremity (part of) through carpal bone (isa). These relations from the NCI Thesaurus mirror relations among equivalent
concepts in the Mouse Anatomy. The structural validation is performed
automatically in both cases, but separately on the set of equivalent
concepts identified by each approach (lexical and manual). Evaluation The evaluation consists of comparing the sets of equivalent concepts obtained
by lexical and manual alignment, after eliminating the concepts
not supported by structural similarity. An additional manual review of
the pairs identified specifically by either lexical or manual alignment
was performed by an expert (TH). Quantitative results As mentioned earlier, of the 4,410 NCI Thesaurus terms, over 2,000 terms
correspond to entities that are not included in the Adult Mouse Anatomical
Dictionary. Thus, only about 2,400 NCI Thesaurus terms would be
expected to be candidates for matching against the 2,404 terms in the
Adult Mouse Anatomical Dictionary. As illustrated in , the lexical alignment identified 715 equivalent concepts (anchors), while
the manual identified 781. Of these, the structural alignment removed
the anchors for which no structural evidence was found: 62 (8.7%) for
the concepts aligned lexically and 53 (6.8%) for
those aligned manually, leaving 653 pairs of equivalent concepts for the
lexical alignment and 728 for the manual alignment. The intersection of these two sets of concepts contains 639 concepts. In
other words, most equivalent concepts are identified by both approaches
simultaneously. The vast majority of these 639 mappings is supported
by structural evidence. Only 45 of them (7.0%) are rejected
for lack of structural evidence when the lexical alignment is used as
the reference (43 when the manual alignment serves as the reference). 76 concepts
were considered equivalent by the lexical method only, while 142 concepts
were identified by the manual approach only. Qualitative results Mappings identified by both approaches All of the 639 mappings identified simultaneously by both approaches were
determined to be appropriate matches by a domain expert, including
the 43 or 45 for which no structural evidence was found, depending which
alignment is used as the reference. Examples of such mappings include
the pairs { uterine cervix (MA:000392) and Cervix Uteri (NCI: C12311)}, identified by lexical similarity and supported
by structural evidence, and { tendon (MA:0000115) and Tendon (NCI: C13045)}. The latter pair was not supported by structural
evidence because it is related to connective tissue ( isa) in MA but to Musculoskeletal System ( part of) in NCI. Mappings specific to the lexical approach Of the 76 mappings identified by the lexical method only, 61 (80.2%) were
deemed to be appropriate matches which, in theory, should have
been picked up by the manual approach. Use of UMLS synonymy clearly
augmented the sensitivity of the lexical approach. The remaining 15 concept
pairs were not considered to be valid. Examples of valid mappings specific to the lexical approach include the
pairs { urinary bladder urothelium (MA:0001693) and Transitional Epithelium (NCI: C13318)} (where MA provides transitional epithelium as a synonym for urinary bladder urothelium) and { lienal artery (MA:0001991) and Splenic Artery (NCI: C33597)} (where the synonymy between the two terms comes
from the UMLS concept Structure of splenic artery (UMLS:C0037996)). The following mapping was considered invalid: between cerebellum lobule I (MA:0000998) and Lingula of the Lung (NCI: C40373), both terms having lingula as a synonym. In both cases, lingula refers to a tongue-shaped entity, but one is part of the cerebellum, while
the other is part of the lung. Mappings specific to the manual approach Of the 142 mappings identified only by the manual approach, 133 (93.7%) were
validated as matches. In 6 of the 9 remaining mappings, an
operator-generated error was involved; that is, incorrect numerical
identifiers had been used for one of the terms. In each of these cases, at
least one of the terms in the set was found to have another, more
appropriate match; 4 of these were already represented on the lists, while 2 new
matches were identified. Examples of valid mappings specific to the manual approach include the
pairs { alveolus epithelium (MA: 0001771), Alveolar Epithelium (NCI: C12867)} and { cervical vertebra 1 (MA:0001421) and C1 Vertebra (NCI: C32239)}. In these cases, the equivalence is easy to determine
for an expert. However, the lexical approach failed to identify
these mappings because the terms are different and no common synonym is
provided by either source or found unambiguously in the UMLS. Applications of the mapping for biologists The mapping between human and mouse anatomies is one element of a broader
framework under development whose objective is to support reasoning
about diseases across various model organisms. More precisely, the mapping
between human and mouse anatomies together with mappings between
diseases in humans and mice will enable comparative science, i.e., will
help validate the use of mouse models of human disease. In practice, instead
of creating a unique ontology for the anatomies of various species, relations
such as Anatomy Equivalent_To Anatomy will be created in the NCI Thesaurus between human and mouse anatomical
entities. Analogously, the relationship Disease Similar_To Disease will be used to relate human and mouse diseases. Users of the mapping
between mouse and human anatomies will include, for example, researchers
from the NCI Mouse Models of Human Cancers Consortium 8 (MMHCC). The following hypothetical use case illustrates the practical application
of this framework. Researchers studying small cell lung carcinoma (SCLC) in
humans may want to use a mouse model of lung cancer to test a
new therapeutic agent. In order to find out whether there is a mouse
equivalent of SCLC in humans, they start their search from SCLC (human
diseases) and use the link Similar_To to reach diseases in mice. Such a link would lead possibly to Neuroendocrine
Carcinoma of the Mouse Pulmonary System. Analogously, the links
between anatomies would help determine, for example, correspondences
between parts of the mouse lung and the parts of the human lung of interest
in their study. The availability of such correspondences would also
greatly facilitate the retrieval by clinicians familiar with human
anatomy and diseases of scientific information about the neuroendocrine
carcinoma models in mouse. Applications of the mapping for ontologists Many large-scale alignment experiments lack thorough validation, because
it requires a manual review of the mappings by experts, which is a labor
intensive task. This study provides a curated mapping between the
human and mouse anatomy ontologies. However, one limitation of this study
is that evaluation of the alignment was performed by the same expert
who also performed the manual alignment. In practice, the results
show that the expert reversed some of her initial judgements during the
evaluation, which confirms that the bias, if any, is not important. The large proportion of mappings identified by the fully automatic lexical
approach and deemed satisfactory by the expert confirmed that the
alignment techniques we developed for large ontologies remain valid on
smaller ontologies. The large proportion of valid mappings not supported
by structural evidence also indicates that this alignment technique
is rather conservative in evaluating validity. Moreover, assuming our
definition of structural evidence is correct, the presence of valid
mappings not supported by structural evidence essentially reveals a lack
of relationships represented in the ontologies or differing modeling
choices between them. The analysis of fine differences between human and mouse anatomies is beyond
the scope of this paper but is addressed in [ 7]. However, a careful analysis of the mappings (and failures) also
reveals differences between the two ontologies and thus helps identify
issues in the representation of anatomical entities, including missing
and inaccurate synonyms and relations. The results obtained will
serve as a guide in addressing these weaknesses, and in complementing
and harmonizing the anatomical concepts and relations in both ontologies. As
the human and mouse ontologies are refined and harmonized, the
mapping approaches presented here can also help monitor progress. Lexical vs. manual Both approaches showed strengths and weaknesses. The lexical approach performs
consistently, at a low cost, but its model of lexical similarity
is limited. In contrast, experts may identify mappings in the absence
of lexical similarity, but they also sometimes make mistakes, and their
knowledge comes at a high price. This study suggests a strategy in
which the results of an automatic mapping (combining lexical and structural
similarity) could be used to direct the attention of experts on
those cases where their knowledge is required. Such a strategy would
make it possible to achieve high quality mapping with limited human resources
and to bring consistency to the mapping between large ontologies. The limited overlap found between human anatomy in NCI and mouse anatomy
is not due to the lack of power of our methods, but rather to differences
in coverage. For example, cell types, developmental stages and subcellular
entities are covered by NCI, but not by MA. The presence of
mouse-specific anatomical structures in MA also accounts for some of
the differences. The research was supported in part by an appointment to the National Library
of Medicine Research Participation Program administrated by the
Oak Ridge Institute of Science and Education through an interagency agreement
between the U.S. Department of Energy and the National Library
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