PROKINO: DESIGN AND DEVELOPMENT OF ONTOLOGY ON PROTEIN KINASES by GURINDER PAL SINGH GOSAL (Under the Direction of KRZYSZTOF J. KOCHUT and NATARAJAN KANNAN) ABSTRACT The prominent role protein kinases play in cell regulation and disease has given rise to an abundance of information about the structure, function, interactions and evolution of these proteins. This information, however, is currently spread across several heterogeneous resources, an obstacle to the kind of integrative approaches needed in utilizing existing knowledge for research related to diseases. We have designed and developed an ontology for protein kinases, ProKinO, that serves as a useful and efficient representation of the integrated knowledge about these complex proteins which are intimately involved in the genesis and behavior of cancer cells. ProKinO captures concepts and relationships important to protein kinases and the instances are populated from disparate resources including KinBase, COSMIC, Protein Data Bank, UniProt and Pfam. ProKinO has potential applications in text mining in the protein kinase literature; cancer genome annotation in cancer genome sequencing studies and research related to protein kinases and associated domains. INDEX WORDS: Kinase, Ontology, Semantic Web, Text Mining, Annotation.
Transcript
PROKINO: DESIGN AND DEVELOPMENT OF ONTOLOGY ON PROTEIN
KINASES
by
GURINDER PAL SINGH GOSAL
(Under the Direction of KRZYSZTOF J. KOCHUT and NATARAJAN KANNAN)
ABSTRACT
The prominent role protein kinases play in cell regulation and disease has
given rise to an abundance of information about the structure, function, interactions and
evolution of these proteins. This information, however, is currently spread across several
heterogeneous resources, an obstacle to the kind of integrative approaches needed in
utilizing existing knowledge for research related to diseases. We have designed and
developed an ontology for protein kinases, ProKinO, that serves as a useful and efficient
representation of the integrated knowledge about these complex proteins which are
intimately involved in the genesis and behavior of cancer cells. ProKinO captures
concepts and relationships important to protein kinases and the instances are populated
from disparate resources including KinBase, COSMIC, Protein Data Bank, UniProt and
Pfam. ProKinO has potential applications in text mining in the protein kinase literature;
cancer genome annotation in cancer genome sequencing studies and research related to
protein kinases and associated domains.
INDEX WORDS: Kinase, Ontology, Semantic Web, Text Mining, Annotation.
PROKINO: DESIGN AND DEVELOPMENT OF ONTOLOGY ON PROTEIN
KINASES
by
GURINDER PAL SINGH GOSAL
BA, Punjabi University, India, 1991
MCA, Punjabi University, India, 1996
A Thesis Submitted to the Graduate Faculty of The University of Georgia in Partial
mutation residue, and cancer types. The information about the functional features, such as
the modified residue, signal peptide, topological domain, cellular location, tissue
specificity as well as about the crystal structures and the identification and classification
of functional domains in protein kinase sequences is also captured and represented in
ProKinO.
2.7 Challenges in Integrating Protein Kinase Knowledge
The biological researchers and scientists use specific theories and models for the
data related to their work in the domain of interest and often end up applying different
descriptions of the same data [48]. There has been an exponential growth in the research
and clinical data resulting in many isolated repositories of knowledge designed,
developed and maintained by separate groups working in the same or related area of
interest. Whenever a need makes obligatory for a user to access the information from
these disparate sources, many challenges are posed for utilizing knowledge stored in
them. The researcher has to make customized arrangements for fetching data from
required sources and integrating for the current need fulfillment and later on any new
query will require going through the adaption again. The protein Kinase knowledge is
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31
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We can very well imagine the challenges considering the voluminous data and
numerous queries that may have to be handled. We will see in later sections how a well
populated ProKino, containing knowledge from various sources, can be helpful in dealing
with these kinds of challenges.
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CHAPTER 3
DESIGN OF PROKINO
In this chapter we discuss the design of ProKinO, starting with looking at the
heterogeneous knowledge sources used in ProKinO development such as Kinbase,
COSMIC, Protein data Bank, Pfam and UniProt and then moving to the ProKinO design
discussing ProKinO classes and properties. Finally, we describe the overall architecture
of ProKinO, describing all its components at the end of this chapter.
3.1 Heterogeneous Knowledge Sources
The ProKinO ontology is an attempt to provide a common vocabulary of concepts
and relationships between those concepts about protein kinase domain. Our goal has been
to make ProKino a unified compendium of knowledge, captured from disparate
knowledge sources related to the domain of protein kinase. Here we discuss briefly the
protein kinases and the sources of protein kinase related knowledge, as well as the
information they contain.
3.1.1 KinBase
One of the well recognized sources in the domain of the protein kinases is an
interactive kinase database KinBase from Kinase.com. Kinase.com is produced and
managed by Gerard Manning's lab at the Salk Institute in California. Sucha Sudarsanam,
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of the biotech company Sugen developed Kinase.com in 1999 to support the publication
of Sugen's analysis of the protein kinases. The site was designed and enlarged with
several bioinformatics tools to use the data by Jonathan Bingham of Sugen. The site was
further developed in 2002-2003 to support KinBase, an interactive kinase database. The
system was shaped and administered by Glen Charydczak and was designed by Gerard
Manning [49].
There has been a non-redundant set of 518 human protein kinase genes identified
based on a comprehensive approach using human genome analysis in KinBase. This
collection in KinBase is made up of published human genome sequences as well as of
other sequence databases and also including directed cloning and sequencing of
individual genes. The set of protein kinase genes in KinBase includes most human
members of the eukaryotic protein kinase super family, and many atypical kinases and
almost all human protein phosphorylation [50]. A popular poster of human kinome by
KinBase is shown in Figure 8.
Figure 8: Human kinome poster (source: [51]).
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The collection of eukaryotic protein kinases is categorized into ten groups. The
classification of protein kinases into groups by KinBase is depicted in Figure 9.
Figure 9: The Protein kinases classification in groups by KinBase (Atypical and Other groups not shown) [50].
The ten groups are classified in KinBase as:
• AGC group (including cyclic-nucleotide and calcium-phospholipid-dependent
kinases, ribosomal S6-phosphorylating kinases, G protein- coupled kinases and close
relatives of these kinases)
• Atypical group
• CAMKs (calmodulin-regulated kinases)
• CK1 group (casein kinase 1 and close relatives)
• CMGC group (including cyclin-dependent kinases, mitogen-activated protein kinases,
CDK-like kinases and glycogen synthase kinase)
• RGC group (receptor gyanylate cyclase kinases)
• STE group (MAP Kinase cascade kinases),
• Tyrosine kinase group (TKs) and
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• TKL group (Tyrosine kinase like family) - which are a cluster of serine-threonine
kinases resembling TKs.
• Other group: Another extensive, miscellaneous group called 'Other' is also considered
for those proteins that do not fit in any of the predefined sets.
These protein kinase groups are further subdivided into families and sub families in KinBase. This distribution into groups, families and subfamilies is shown in Table 1.
Table 1: Kinase distribution by major groups in human systems (Source & Adaption:[50])
Groups Families Sub Families Human Kinases
AGC 14 21 63
CAMK 17 33 74
CK1 3 5 12
CMGC 8 24 61
Other 37 39 83
STE 3 13 47
Tyrosine Kinase 30 30 90
Tyrosine Kinase-Like 7 13 43
RGC 1 1 5
Atypical-PDHK 1 1 5
Atypical-Alpha 1 2 6
Atypical-RIO 1 3 3
Atypical-A6 1 1 2
Atypical-Other 7 7 9
Atypical-ABC1 1 1 5
Atypical-BRD 1 1 4
Atypical-PIKK 1 6 6
Total 134 201 518
The sequences of all these protein kinase genes in KinBase database are also
available from kinase.com [52]. The data is extracted from this well recognized and the
fundamental source of information on protein kinases.
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3.1.2 COSMIC (Catalogue of Somatic Mutations in Cancer)
Catalogue of Somatic Mutations in Cancer abbreviated as COSMIC is the largest
resource for information about the somatic acquired mutations associated with human
cancer used by the biomedical community and available freely in the public domain [53].
Mutations are changes in a DNA sequence caused by transcription inaccuracies in genetic
material, induced cellular processes by the organism itself, damage due to mutagenic
chemicals or viruses or radiations, or errors during meiosis. The mutations are mapped to
a single version of each gene sequence in COSMIC and somatic mutation frequencies are
also made available.
After being launched in 2004, there has been a gigantic increase in the size of the
COSMIC database. Presently (v43, August 2009), COSMIC is said to be holding the
details of 1.5-million experiments performed through 13423 genes in almost 370000
tumours, describing over 90,000 individual mutations. The main two sources of data
inclusion in COSMIC are the publications in the scientific literature and the output from
the large analyses from the Cancer Genome Project (CGP) at the Wellcome Trust Sanger
Institute, UK [54]. This has been stated that most of the world’s literature on the genes
known to have tumor-promoting point mutations is curated in COSMIC [54]. The
curation of fusion events (when two genes fuse together) between multiple genes, which
are also common occurrences in cancer, is also continually updated.
One of the important studies in cancer research is finding the mutated genes that
are implicated in cancer. The COSMIC database is integrated into ProKinO for capturing
the mutation knowledge about genes related to protein kinase so that we can relate
otherwise heterogeneous information to be available in a single integrated representation.
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3.1.3 Pfam (Protein Families Database)
Pfam, a wide-ranging database for conserved protein families, was originated in
1998 and since then has been growing with deposition of new protein sequences. Pfam
which is a collection of multiple sequence alignments and profile hidden Markov models
(HMMs) is publically available via servers in the UK [55], Sweden [56] and USA [57].
Pfam’s latest release contains a collection of nearly 12000 families each
represented by multiple sequence alignments and hidden Markov models. There are
domains which are functional regions that form the proteins. Entries in Pfam can be
families which are sets of related proteins; domains which are structural units found in
many protein frameworks; repeats, which are units forming a steady structure when many
copies are there but unsteady in isolation, and motifs which can be a short unit found
outside globular domains. A clan is a higher level grouping of related families, produced
by Pfam, that has come up from a single evolutionary source. The resemblance in tertiary
structures, or, in common sequence motifs when structures are not available give their
evolutionary substantiation.
Pfam families can be categorized in two levels of quality: Pfam-A and Pfam-B.
Entries coming up from the core sequence database, known as Pfamseq, which is built
from the most recent release of UniProtKB [58] at a specific time, are termed as Pfam-A
entries. To cover more known proteins alongwith Pfam-A families, Pfam generates Pfam-
B families using the Automatic Domain Decomposition Algorithm (ADDA) database
[59]. Pfam-B families have no associated annotation or literature reference and are of
much lower quality than Pfam-A families, as their alignments have not been manually
checked by a Pfam curator. If we perform a search on protein kinases against the Pfam
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library of HMMs, we can find its domain architecture (for a protein shown in Figure 10)
i.e., find which domains it carries.
Figure 10: Pfam display of protein domain architecture (source: [57]).
The data is extracted from this source about identification and classification of functional
domains in protein kinase sequences to be captured in ProKinO.
3.1.4 Protein Data Bank (PDB)
The Protein Data Bank (PDB) contained 7 structures to start with when it was
founded in 1971, at the Brookhaven National Laboratory. After that, PDB has grown by
large proportions to become a large repository for three-dimensional structures of
macromolecular complexes of proteins, nucleic acids, and other biological molecules.
The knowledge about the shape of the molecules of life is useful in understanding their
working. This knowledge can go a long way in deducing a structure's role in disease and
developing the drugs for these diseases.
There was another step in this connection when The Worldwide Protein Data
Bank (wwPDB) [60] was formed in 2003 to keep a single PDB archive of
macromolecular structural data which is freely and openly available to the global
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community. There are many participating organizations of the wwPDB. These
organizations are basically the centers that collect, process and distribute the PDB data.
wwPDB was joined by The Biological Magnetic Resonance Data Bank (BMRB) [61] in
the year 2006. Many utilities are extended by wwPDB with providing databases and
websites that can be helpful for obtaining different views and analysis of the structural
data contained within the PDB archive.
PDB has many tools and resources available for searching based on annotations
relating to sequence, structure and function and then using the results further for analysis
or visualization. The curation and annotation of PDB data is done as per the agreed
principles by the PDB that supports a website to perform simple and complex queries on
the data, analyze, and visualize the results. The crystal structure of a protein kinase gene
product EGFR (whose PDBId is given by the property hasStructure in ProKinO) is
shown in Figure 11.
Figure 11: Crystal structure of protein kinase gene product EGFR (ProKinO PDBId:
1IVO, source: [62]).
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Information is extracted from the Protein Data Bank to be included in ProKinO
about the crystal structures related to protein kinases and PDB database references for
that structure.
3.1.5 The Universal Protein Resource (UniPort)
There has been tremendous growth of the proteomics data and also soaring
production of genome sequencing data in the recent past. This has resulted in a huge
wealth of protein sequences and associated data for a large number of organisms. There
was an observed need for a kind of storehouse that can serve as a global collection of
protein sequences with all-inclusive coverage and a methodical approach to protein
annotation, incorporation, integration and standardization of data from the various
sources and UniProt was an initiative in this direction [63].
The Universal Protein Resource (UniProt) is supported by the UniProt
Consortium, a collaboration between the European Bioinformatics Institute (EBI), the
Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR).
UniProt is mainly supported by the National Institutes of Health (NIH). The mission of
UniProt is to offer the scientific community a wide-ranging, high quality and freely
accessible resource of protein sequence and functional information.
The UniProt databases are the UniProt Knowledgebase (UniProtKB), the UniProt
Archive (UniParc), The UniProt Metagenomic and Environmental Sequences (UniMES),
and the UniProt Reference Clusters (UniRef). UniProt Knowledgebase (UniProtKB) is
the database for broad curated protein information, including function, classification, and
cross-reference. UniProtKB is further made up of UniProtKB/Swiss-Prot and
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UniProtKB/TrEMBL. The first one is manually annotated and is reviewed and second
one is automatically annotated and not reviewed. Sequences and their identifiers are
stored in the UniProt Archive (UniParc). The UniProt Reference Clusters (UniRef)
databases help to obtain complete coverage of sequence space at several resolutions while
hiding redundant sequences by presenting clustered sets made of sequences from the
UniProtKB and selected UniProt Archive records [64]. The UniProt Metagenomic and
Environmental Sequences (UniMES) database is for metagenomic and environmental
data. These databases are shown in Figure 12.
Figure 12: The Universal Protein Resource (UniProt) databases [65].
Significantly, the UniProt consortium in 2008 completed the first draft of the
complete human proteome in UniProtKB/Swiss-Prot, thereby providing manually
annotated representation of all presently known human protein-coding genes in UniProt
release 14.0 with 20325 entries [63].
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Information extracted from UniProt and represented in ProKinO includes
functional features, such as the modified residue, signal peptide, topological domain,
cellular location, tissue specificity, organism, and database cross references for
Wikipedia, PubMed, MEDLINE and UniProt.
3.2 ProKinO Design
Ontology development is a complex process involving a series of systematic and
well organized steps in the process. This is not merely a technological process but
requires a deep understanding of the area about which the knowledge is being represented
in the ontology. The ProKinO realization includes following certain principles for its
design and development. One important aspect of ontology design is to keep in mind the
interests of the intended user community and that the design must incorporate the needs
of user in its final requirements.
In the case of ProKinO, the golden rule of reusing the existing ontologies in the
public domain for specialization or generalization was considered. However, we
recognized the need for a knowledge repository serving the specific domain of protein
kinase due to their important role in diseases like cancer. The domain expertise was
provided by the Evolutionary Systems Biology Group (ESBG) in the Biochemistry and
Molecular Biology department at The University of Georgia that is researching
extensively in the area of protein kinases, with the help of the protein kinase community.
On the other hand, the technical facilitation was extended by the Large Scale Distribution
Information Systems Lab (LSDIS) in Computer Science department at The University of
Georgia. The specification and conceptualization of ProKinO was finalized after a long
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deliberation period during which ProKinO has undergone numerous revisions. In the
subsequent sections, we discuss the current specification of ProKinO design. However,
the ontology may be revised in the future, as the requirements of user community may
evolve.
3.2.1 ProKinO Classes
Ontologies are made of components namely classes (slots), properties
(relationships) and individuals (members or instances) to fully capture the knowledge
about a particular domain. The steps in ontology construction include developing the
class hierarchy and defining a number of properties and relationships characterizing the
classes. The main concepts in ProKinO have been defined as major classes and are
shown in Figure 13.
The classes defined in ProKinO are used to represent the sequence, structure,
function, sub-domain, and mutation data. We used a combination of top-down and
bottom-up approaches to first define some main concepts and then specialize or
generalize them, depending on further needs. All the major classes and sub classes
specified in the design of ProKinO are shown in Table 2.
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Figure 13: Conceptualization of ProKinO (illustrating only a part of overall ontology)
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Table 2: Classes in ProKinO.
Class Name Sub Class Name (Level-1)
Sub Class Name (Level-2)
Sub Class Name (Level-3)
DbXref - - - The DbXref class is used to represent the various database cross references for the instances stored in the Gene class.
This class contains cross references for the UniProt, PDB, PubMed, MEDLINE, Wikipedia and KinBase databases.
Disease Cancer - - The Disease class represents the different diseases with which protein kinase genes are associated with. The Disease
class have these diseases represented under further sub classes of diseases (Cancer, initially).
FunctionalDomain - - - The FunctionalDomain class represents the functional domains identified and classified in protein kinase sequences.
FunctionalFeature ModifiedResidue
SignalPeptide
TopologicalDomain
TransmembraneRegion
-
-
The FunctionalFeature class represents data about the various functional features related to the protein kinase genes
for which sub classes of ModifiedResidue, SignalPeptide, TopologicalDomain, TransmembraneRegion have been
designed to represent the different groups of functional features of gene.
Gene - - - The Gene class in ProKinO represents all the protein kinase genes which are classified into the protein kinase groups,
families, and sub families.
Mutation - - - The Mutation class is used to represent all the somatic cancer mutations that are linked with the protein kinase genes.
Organism - - The Organism class represents all the organisms (Human, initially) for the protein kinase genes.
ProteinKinase (groupname) group (familyname) family (subfamilyname) subfamily
The ProteinKinase class represents classification of the protein kinase genes by having the groups of genes represented
by the ProteinKinaseGroup as a sub class under it. The family of protein kinase genes is represented by the
ProteinKinaseFamily sub class designed under the ProteinKinaseGroup class. Similarly the sub family of protein
kinase genes is represented by the ProteinKinaseSubFamily sub class designed under the ProteinKinaseFamily class.
Sequence - - - The Sequence class of ProKinO represents the sequences for the protein kinase genes.
Structure - - - The Structure class of ProKinO represents crystal structures of the proteins kinase genes.
SubDomain SubDomain(I to XI) - - The SubDomain class represents the motif data related to the protein kinase genes with sub classes of SubDomainI to
SubDomainXI under it capturing the various motifs related to the genes.
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3.2.2 ProKinO Properties
Once we have defined the major classes in the ontology, the next step is to
describe additional specifics of the concepts by defining their relationships in the form of
object and data properties in ProKinO. The object properties capture the relationships
between the members of various classes to make the collected information integrated as
per the agreed conceptualization. How a gene in the Gene class is related with a disease
in the Disease class is formalized with the object property associatedWith. On the other
hand, the correlatesTo property connects a Disease member, i.e. a particular disease
(cancer for our purposes), to the gene in the Gene class. These are descriptions of only a
few object properties discussed above. The full list of object properties conceptualized in
ProKinO is given in Table 3.
As we need information to describe the internal organization of concepts captured
in ontology, the data properties in ProKinO are defined apart from object properties.
There are many properties of classes considered, such as what type of values, permissible
values, and cardinality while defining relationships. The property hasFASTAFormat is
defined as a data property in ProKinO to represent the FASTA-formatted sequences of all
protein kinase genes, and it is of data type string. Similarly, there exist another property,
hasOtherName, which is used to store the other names by which a gene may be known in
literature (synonyms). For example, a protein kinase gene ADCK3 has four other names
CABC1, COQ8, MGC4849 and LOC56997. There is a large list of data properties
defined in ProKinO as shown in Table 4.
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Table 3: Object Properties in ProKinO.
Object Property Domain (Class) Range (Class)
associatedWith Gene Disease The object property associatedWith represents the relationship between the instances of the Gene class with the
instances of the Disease class. This relationship represents how the protein kinase genes are associated with the
different diseases. For example, the gene “ABL1” is associated with cancer disease “giloma”.
contains Sequence Mutation The object property contains represents the relationship between the instances of the Sequence class with the instances
of the Mutation class. This relationship represents which sequences of the protein kinase genes contain which
mutations represented in Mutation class. For example, the gene sequence “Seq-ABL1” (sequence of the gene “ABL1”)
contains mutation “Q252H”.
correlatesTo Disease Gene The object property correlatesTo represents the relationship between the instances of the Disease class with the
instances of the Gene class. This relationship represents how a disease correlates to the protein kinase genes. For
example, the cancer disease “giloma” correlates to the genes “ABL1”, “FYN”, “TBK1” and many others.
foundIn Mutation Gene The object property foundIn represents the relationship between the instances of the Mutation class with the instances
of the Gene class. This relationship represents what mutations are found in which protein kinase genes. For example,
mutation “L387M” (identified by mutation id 12624) is found in the gene “ABL1”.
hasFunctionalDomain Gene FunctionalDomain The object property hasFunctionalDomain represents the relationship between the instances of the Gene class with the
instances of the FunctionalDomain class. This relationship represents which protein kinase gene has which functional
domain. For example, the gene “ABL1” has the functional domains of “Pkinase” and “Pkinase_Tyr”, “F-actin_bind”
and “SH3_1”.
hasFunctionalFeature Gene FunctionalFeature The object property hasFunctionalFeature represents the relationship between the instances of the Gene class with the
instances of the FunctionalFeature class. The FunctionalFeature class further has sub classes such as
ModifiedResidue, SignalPeptide, TopologicalDomain, and TransmembraneRegion. This relationship represents which
protein kinase gene has which functional features represented in these FunctionalFeature sub classes. For example, the
gene “ABL1” has a functional feature of “phosphoserine”.
hasFunctionalRelationship FunctionalDomain Gene The object property hasFunctionalRelationship represents the relationship between the instances of the
FunctionalDomain class with the instances of the Gene class. This relationship represents which functional domain has
a functional relationship with which protein kinase genes. For example, the functional domain “F-actin_bind” has a
functional relationship with the genes “ABL1” and ABL2”.
hasGene ProteinKinase Gene The object property hasGene represents the relationship between the instances of the ProteinKinase class with the
instances of the Gene class. This relationship represents which protein kinase genes represented in groups, families or
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subfamilies of protein kinases are related with the genes represented in the Gene class.
hasMEDLINEId Gene DbXref The object property hasMEDLINEId represents the relationship between the instances of the Gene class with the
instances of the DBXref class related to the MEDLINE cross references. This relationship represents the protein kinase
genes’s MEDLINE database cross references. For example, the gene “ABL1” has the MEDLINE ids “93101588”,
“95199229” and many others.
hasMutation Gene Mutation The object property hasMutation represents the relationship between the instances of the Gene class with the instances
of the Mutation class. This relationship represents which protein kinase genes are having which mutations. For
example, the gene “ABL1” has the mutations “Q252H”, “R47G” and many others.
hasMutationDbXref Mutation DbXref The object property hasMutationDbXref represents the relationship between the instances of the Mutation class with
the instances of the DBXref class. This relationship represents a mutation’s Mutation (NM) database cross reference.
For example, the mutation “Q456Q” (mutation id 1110) has Mutation database cross reference “NM_004333”.
hasPDBId Gene DbXref The object property hasPDBId represents the relationship between the instances of the Gene class with the instances of
the DBXref class related to the PDB cross references. This relationship represents the protein kinase genes’s PDB
database cross references. For example, the gene “ABL1” has a PDB id “1OPL”.
hasPfamId Gene DbXref The object property hasPfamId represents the relationship between the instances of the Gene class with the instances of
the DBXref class related to the Pfam cross references. This relationship represents the protein kinase genes’s Pfam
database cross references.
hasProteinKinaseDbXref ProteinKinase DbXref The object property hasProteinKinaseDbXref represents the relationship between the instances of the ProteinKinase
class with the instances of the DBXref class related to the protein database cross references. This relationship
represents protein kinase genes’s KinBase database cross references.
hasPubMedId Gene DbXref The object property hasPubMedId represents the relationship between the instances of the Gene class with the
instances of the DBXref class related to PubMed cross references. This relationship represents protein kinase genes’s
PubMed database cross references. For example, the gene “ACK” has the PubMed ids “16641997”, ”17344846”,
”15951569” and many other.
hasSequence ProteinKinase Sequence The object property hasSequence represents the relationship between the instances of the ProteinKinase class with the
instances of the Sequence class. This relationship represents which protein kinase genes under the groups, families or
subfamilies of the protein kinases are having which sequences stored under the Sequence class.
hasStructure Gene Structure The object property hasStructure represents the relationship between the instances of the Gene class with the instances
of the Structure class. This relationship represents which protein kinase gene has which crystal structure. For example,
the gene “ABL1” has a structure “1OPL”;
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hasSubDomain Gene SubDomain The object property hasSubDomain represents the relationship between the instances of the Gene class with the
instances of the SubDomain class. This relationship represents which protein kinase gene has which sub domains that
are represented under various sub classes (SubDomain I to IX) of the SubDomain class. For example, the gene “ABL1”
has sub domains for all the sub domains from SubDomain I to IX such as for G-helix, the sub domain “ABL1-G-helix”
has a start location of 446, end location of 460 and subsequence “VLLWEIATYGMSPYP”.
hasUniProtId Gene DbXref The object property hasUniProtId represents the relationship between the instances of the Gene class with the
instances of the DBXref class related to the UniProt cross references. This relationship represents the protein kinase
genes’s UniProt database cross references. For example, the gene “ACK” has the UniProt ids “Q07912”, ”Q6ZMQ0”,
”Q8N6U7” and many other.
hasWikipediaId Gene DbXref The object property hasWikipediaId represents the relationship between the instances of the Gene class with the
instances of the DBXref class related to the Wikipedia cross references. This relationship represents the protein kinase
genes’s Wikipedia database cross references. For example, the gene “MUSK” has a Wikipedia id “Musk_protein”.
implicatedIn Mutation Disease The object property implicatedIn represents the relationship between the instances of the Mutation class with the
instances of the Disease class. This relationship represents which mutations are found implicated in which diseases
(Cancer initially). For example, the mutation “L387M” (identified by the mutation id 12624) is implicated in the
disease “haematopoietic_neoplasm”.
locatedIn Mutation Structure The object property locatedIn represents the relationship between the instances of the Mutation class with the instances
of the Structure class. This relationship represents which mutations are located in which structures of the protein
kinase genes. For example, the mutation “L387M” (identified by the mutation id 12624) is located in the
structure”1OPL “.
occursIn Mutation Sequence The object property occursIn represents the relationship between the instances of the Mutation class with the instances
of the Sequence class. This relationship represents which mutations are found in which sequences of the protein kinase
genes. For example, the mutation “L387M” (identified by mutation id 12624) is found in the sequence “Seq_ABL1”
hasMutationAA Mutation String The data property hasMutationAA represents the mutation Amino Acid of a particular mutation in the Mutation class
and it takes the string value. For example, the mutation “S605F” (mutation id 1135) has mutation AA as “p.S605F”.
hasMutationDescription Mutation String The data property hasMutationDescription represents the mutation description of a particular mutation in the Mutation
class and it takes the string value. For example, the mutation “S605F” (mutation id 1135) has mutation description as
“substitution-Missense”.
hasMutationId Mutation String The data property hasMutationId represents the mutation id (COSMIC) of a particular mutation in the Mutation class
and it takes the string value. For example, the mutation “S605F” has a mutation id as “1135”.
52
hasOrganism Gene String The data property hasOrganism represents the organism of the protein kinase genes in the Gene class and it takes the
string value. For example, the gene “ABL1” has an organism “Homo Sapiens”.
hasOtherName Gene String The data property hasOtherName represents the other names (Synonyms) of the protein kinase genes in the Gene class
and it takes the string value. For example, the gene “ABL1” has other names of “p150”, “c-ABL”, “ABL”, “JKT7” and
many others.
hasPosition FunctionalFeature String The data property hasPosition represents the position of the different functional features represented in the
FunctionalFeature class and the subclasses under this class and it takes the string value. For example, the functional
feature “ABL1-phosphoserine” (Modified Residue of the gene “ABL1”) has position “50”.
hasPrimaryName Gene String The data property hasPrimaryName represents the primary or common name (most representative name in literature)
of the protein kinase genes in the Gene class and it takes the string value. For example, the gene “ABL1” has a primary
Name “ABL1”.
hasPrimarySite Mutation String The data property hasPrimarySite represents the primary cancer site of a particular mutation in the Mutation class and
it takes the string value. For example, the mutation “S605F” (mutation id “1135”) has a primary site of ”skin”.
hasPubMedPMID Mutation String The data property hasPUBMEDPMID represents the PUBMED PMID of a particular mutation in the Mutation class
and it takes the string value. For example, the mutation “S605F” (mutation id “1135”) has a PUBMED PMID’s
”16773193” and “15331929”.
hasSubDomainSequence SubDomain String
The data property hasSubDomainSequence represents the subsequence of a particular sub-domain in SubDomain class
for the protein kinase genes and it takes the string value. For example, ACK-B3-strand sub domain has a subsequence
"SGKTVSVAVKCLKPD".
hasTissueSpecificity Gene String The data property hasTissueSpecificity represents the tissue specificity of the protein kinase genes in the Gene class
and it takes the string value. For example, the gene “ABL1” has tissue specificity “Widely expressed”.
3
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53
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54
Data is acquired from these sources automatically and is analyzed and stored in
ProKinO as classes, properties, and instances. To assure that the ontology is up-to-date
and correct, ProKinO will be continually updated by an automatic population process,
described above. Once developed and verified, ProKinO will be publicly available and
accessible from our Web Portal. ProKinO will also be made available from NCBO
BioPortal after it gets included in the BioPortal. In addition to freely downloadable OWL
files containing ProKinO and its prior versions, the portals will provide access to a live
version of the ontology so that portal visitors can browse and explore the ontology.
Furthermore, we plan to create a Web Service providing a programmatic access to
ProKinO.
55
CHAPTER 4
PROKINO LIFE CYCLE
Ontology development is a complex task involving a series of systematic and well
organized steps in the process. Ontology Engineering is a relatively new field but there
has already been a lot of work and research in this field during the last decade.
Ontological Engineering refers to the set of activities that concern the ontology
development process, the ontology life cycle, the methods and methodologies for
building ontologies, and the tool suites and languages that support them [66].
Ontologies in the past have been created by different people by following
different methods and methodologies. The type of ontology being developed, the domain
of discourse to be captured, the expertise of the developers of the ontology, all these
factors drive the selection of one method over another while building ontologies.
Further, what type of applications of ontology have been envisioned, the time and
resources available for the task and other related factors also influence the choice of the
approach. Ontology Life Cycle identifies the stages through which the ontology should
go through during its life time. There are certain sets of activities that are performed in
each stage and different models have been proposed by researchers for formalizing how
the different stages can be related in terms of their order. In developing ProKinO, certain
sets of activities which were performed to accomplish the task are discussed in the
subsequent sections.
56
4.1 Data Acquisition
The data sources of interest to biomedical community are often large, dissimilar
in structure and content, scattered, separately controlled, and rapidly changing [67]. One
of the vital steps in building ontologies is to acquire the knowledge from these kinds of
sources that is to be integrated and represented in the ontologies. This poses several
challenges in information extraction and knowledge acquisition from these disparate
sources. In the development of ProKinO we faced similar challenges in acquiring the data
from the identified sources of the domain knowledge. These data sources, such as
KinBase, COSMIC, PDB, Pfam, and UniProt, provide a range of information related to
the protein kinases but they are stored in different formats and controlled and maintained
by different groups.
We used some open source tools for the overall development process of
ProKinO. We have used Jena API as software support to build and populate ProKinO.
This is an open source tool which was developed by a team at HP lead by Brian Mcbride
and is widely used for ontology development. There is another useful API, called OWL
API, is available for building ontologies, but we used Jana API as it is more mature and
stable. The Jena Ontology API is language-neutral and the Java class names do not
mention the underlying language. For example, the OntClass Java class can represent an
OWL class, RDFS class, or DAML class and to represent the differences between the
various representations, each of the ontology languages has a profile, which lists the
permitted constructs and the names of the classes and properties [68] . Jena is very easy
to use and can be used for parsing, creating and searching RDF models. The key RDF
package for the application developer is com.hp.hpl.jena.rdf.model. The API has been
57
defined in terms of interfaces so that application codes can work with different
implementations without change. Jena includes interfaces for model, statement, resource,
property, object, literal etc. An example for Jena API used in Java code to create an
empty ontology model is shown in Figure 15.
public class CreateProkinoBaseModel {
/** * This method is used to create an Ontology model * @param owlFileName The name of the file in which OWL file has to be stored for the * model that is created. * @return The ontology model. */
public static OntModel createBaseModel( String owlFileName) {
OntModel basemodel; // create an empty model basemodel = ModelFactory.createOntologyModel( OntModelSpec.OWL_MEM_RULE_INF); PrintStream out;
try {
out = new PrintStream(new FileOutputStream(owlFileName)); basemodel.write(out);
}
catch (FileNotFoundException e) {
// TODO Auto‐generated catch block e.printStackTrace();
} return basemodel; } // method createBaseModel end
} // CreateProkinoBaseModel class end
Figure 15: Jena example to create an ontology model.
Protégé is a free, open-source software system that has become a very popular
tool for constructing knowledge-based applications with ontologies which provides a
suite of tools to the user community. There are many knowledge-modeling compositions
and procedures implemented by Protege which facilitate in creation, visualization, and
58
manipulation of ontologies in various representation formats. Protégé can be extended by
way of a plug-in architecture and a Java-based Application Programming Interface (API)
for building knowledge-based tools and applications [69].
The Protégé platform supports two main ways of modeling ontologies:
The Protégé-Frames editor is a way of modeling ontologies that provides support to
users in constructing and storing frame-based domain ontologies, customizing data entry
forms, and entering instance data by presenting a full-fledged user interface and
knowledge server. Protégé-Frames use the Open Knowledge Base Connectivity protocol
(OKBC) to implement a knowledge model. The Protégé-OWL editor is an expansion of
Protégé that supports the Web Ontology Language (OWL) which is the standard ontology
language recommended by the World Wide Web Consortium to prop up the Semantic
Web vision. The Protégé-OWL editor provides users support to load and save OWL and
RDF ontologies, edit and visualize classes, properties, define logical class characteristics
as OWL expressions and execute reasoners such as description logic classifiers [69].
For acquiring data from different sources we have written customized software in
Java which performs many required functions in a single unified manner. This software
first of all automatically fetches the relevant data from KinBase about Protein kinase
genes and the associated attributes and after processing the data populates ProKino. Then,
this populated information in our ontology further becomes the basis of acquiring and
parsing the data from other sources of knowledge. The software then automatically
retrieves the needed information by running certain BLAST searches against the protein
kinases data provided by ProKinO to produce pertinent data about functional domains,
motifs, structures, and functional features. These whole sets of data are processed and
p
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59
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60
For instance, from KinBase, we acquire data regarding kinase genes and their
species, their corresponding groups, families and subfamilies, chromosomal location, and
the synonyms and acronyms used for protein kinase genes in the literature. Likewise,
from PDB, we acquire information which includes the PDB ID, three dimensional
coordinates and structure abstract. From COSMIC, we retrieve information regarding the
mutation location, mutation type, tissue type, and cancer type and literature reference.
From the Pfam database, we acquire information about identification and classification of
functional domains in protein kinase sequences, and from UniProt knowledge is acquired
about functional features of protein kinase genes.
4.2 Data Integration and Ontology Population
After acquiring the data relevant to ProKinO from disparate heterogeneous
sources and processing further that data, we integrate this knowledge in ProKinO and
automatically populate the ontology. Our software populates ProKinO with the acquired
knowledge automatically. To integrate the various classes, i.e. the diverse forms of data
in ProKinO, we have developed object properties and relationships that relate sequence,
structure, function and mutation data in meaningful ways. For example, the 'occursIn'
relationship between Mutation and Sequence classes relates a mutation entry in the
Mutation class with a gene entry in the Sequence class. The other relationships among
various classes further integrate these, otherwise disparate, sources of data with each
other.
The automatic process of data population from the KinBase database integrates
and populates the information about the protein kinase genes and their species, their
corresponding groups, families and subfamilies, the other names by which they are
61
known across the domain and about the chromosomal position. Also, the knowledge
about the sequences of protein kinase genes with the information about their FASTA
Formats is populated. The acquired knowledge becomes the basis for automatically
populating the ProKinO ontology by inserting the instances of the genes for Protein
Kinase Group, Protein Kinase Family, Protein Kinase Sub-Family classes in Gene class,
instances for Sequence class, and the data properties for the corresponding classes.
The automatic process of mining COSMIC database in the ProKinO ontology
population extracts the information about the mutations related to cancer by capturing
MutationID, MutationAA, Chromosome, Primary site, Pubmed_PMID, Description and
other relevant information from COSMIC. Then, ProKinO is populated with instances of
classes Mutation and Disease along with their corresponding data properties.
Information integrated and populated from UniProt is about functional features
such as the modified residue, signal peptide, topological domain, about cellular location,
tissue specificity and organism and about Wikipedia, PubMed, MEDLINE and UniProt
database cross references. The data property of hasFunctionalFeature is used to capture
functional features for protein kinase genes depicting the modified residue, signal peptide
and the topological domain. The information populated from Protein Data Bank is about
the crystal structures and PDB database references. ProKinO is populated with instances
of class Structure, as well as their corresponding object and data properties.
Pfam and related Hidden Markov Models (HMM) provide information about
identification and classification of functional domains in the protein kinase sequences.
The ProKinO ontology population from the Pfam resource is done by integrating the
information about the functional domains related to protein kinases and then populating
P
pr
in
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th
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n Figure 17.
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62
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63
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64
The sequence and structure of this gene associated with the in question mutation
can be retrieved as Seq_AKT1 and 3CTQU respectively with properties hasSequence and
hasStructure. The functional feature of cellular location of this gene is provided by
hasCellularLocation as Cytoplasm. This example of knowledge discovery is depicted in
Figure 18.
We should keep in mind that this output of the whole linked information provided
as a unit is basically coming from disparate and separately formatted sources, for which
otherwise we would have to use customized processes to obtain the same results.
Clearly, we see that although the information required may be stored in many
heterogeneous sources in the original databases yet we can get the specific answers to the
queries by navigating through the classes and properties in ProKinO. We believe that
ProKinO will emphasize the usefulness of this type of integration of knowledge in the
form of ontology.
4.3 Curation and Ontology Modification
The ontologies are bound to evolve with the time because with any changes in the
sources of knowledge in the domain, suitable changes to the schema of representation of
knowledge will have to be incorporated, to make the ontology up-to-date and consistent.
Curation: To assure that the ontology is up-to-date and correct, ProKinO will be
continually updated by the automatic population processes described above.
Furthermore, to assure the maximum correctness of the ProKinO data, a domain expert
will be designated to monitor and verify the ontology updates. The ontology curator will
also be charged with introducing any needed schema corrections and its extensions.
Scientists working in the area of protein kinases will be able to submit relevant data for
65
inclusion in ProKinO, using a convenient GUI-type interface. The curator will be
charged with reviewing such submissions and adding such entries upon approval.
4.4 ProKinO Revisions
As an ontology undergoes necessary modifications, the newly updated ontologies
are saved as versions. Ontology versioning is the concept of keeping multiple versions of
ontologies to mange changes and evolution in ontologies. The compatibility of versions
must be checked for instance-data preservation (no data lost between versions unless
explicitly warranted), ontology preservation (query is satisfied in both versions),
consequence preservation (all the facts could be inferred equally from the new version as
inferred from older one) and consistency preservation (no logical inconsistencies) [70].
As ProKinO is an integration of knowledge from disparate sources and the
integration is done without modifying the original sources, it has to be in consonance
with all these dynamic sources that are subject to frequent modifications. For the
knowledge integrated in the ontology to be current and consistent with the existing data
available in the parent sources and to make any changes in the conceptualization and
specification, ProKinO will be subjected to regular revisions, as agreed upon by the
community. We will be keeping the different versions of ProKinO along with the
information about the differences among these versions. The ontology lifecycle will be
tracked by a versioning system and any prior versions of ProKinO will be easily
accessible.
66
4.5 Ontology Dissemination and Evaluation
When the ontologies are disseminated in the form of applications based on the
knowledge contained in them, they can be considered as good quality and valuable
ontologies only if they are able to serve the intended purposes. To look for ontologies that
can be viewed as complete is actually an unrealistic goal. As ontology building is an
expensive and time consuming process. Once the ontology is developed by following the
selected development approaches, it must be evaluated for its quality by following certain
evaluation criteria.
An ontology is as good as the knowledge it contains, so for evaluating specialized
ontologies, their evaluation should be based on the specific needs of the users of that
domain. The simple measures of precision and recall are not that easy to be applied to
ontologies as is the case of other knowledge extraction methods. In case of specialized
ontologies these metrics may assume different notions in different kinds of applications.
Specialized ontologies, such as ProKinO, can be evaluated on the basis of satisfying the
specified needs of intended users. So, we tested and performed an evaluation of ProKinO
based on the requirements of the protein kinase community.
The domain experts in Evolutionary Systems Biology Group (ESBG) lab at The
University of Georgia evaluated the ontology by designing their specific queries
dependent on the knowledge existing in it and then checking the results manually. As this
was a basic evaluation conducted by a subset of user community we expect the ontology
to be further evaluated on the basis of usage by wider protein kinase community while
ProKinO being available in public domain. Every ontology has its ultimate evaluation of
quality and success based on whether it is used and accepted widely by the community or
67
not. Therefore, we are providing a mechanism, so that ProKinO is easily available to all
through our web service. To further strengthen the process of evolution and refinement,
our community will be provided the facility to give its feedback for further improvement.
68
CHAPTER 5
POTENTIAL APPLICATIONS OF PROKINO
We envision a set of semantic, ontology-based bioinformatics applications
utilizing knowledge represented in ProKinO. We plan to create an ontology browsing
visualization tool, available via a standard Web browser. Also, we are going to use
ProKinO in two major applications. First, to mine the wealth of scientific literature data
that is accumulating on protein kinases and second, to annotate the vast amounts of
sequence data generated from cancer genome sequencing studies. A variety of other
applications are possible, as well.
5.1 ProKinO Browsing
For ontologies to be fully adapted by a user community the knowledge contained
in them must be accessible by simple means of browsing through the concepts and
relationships of ontologies. There are many editors (e.g. Protégé) which are providing
the very basic browsing about going through the ontology constituents. But it has been
seen that most website based ontology editors use separate HTML pages not just for each
entity, but for each view of those entities and this distances the user and the ontology
itself [71]. There are many general purpose ontology browsing tools such as the one
offered by OwlDoc plugin (shown in Figure 19 for ProKinO) available in public domain.
However, there is a distinct need to provide specialized browsers to deal with specific
ontologies. A specialized ontology browser must take into account the specific
re
co
a
b
ac
k
fr
elationships
oncepts from
form most s
Figure
We ar
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re creating an
via a standa
ProKinO kn
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mportant one
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shot of elem
n ontology b
ard Web bro
nowledge bu
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abilities.
69
y and disting
es. Furtherm
ontology aud
mentary brow
browsing and
owser. This
ut also certai
for knowle
guish among
ore, the con
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wsing of ProK
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KinO (using
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and often vi
d be visualiz
g OwlDoc).
ProKinO tha
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sought by pr
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at will
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user
70
5.2 Text Mining
The main motivation behind the text mining systems is that most of the world’s
published scientific data is in unstructured or semi-structured form. This becomes more
significant when the researcher or scientist have to deal with huge wealth of literature in
biomedical domain. The overwhelming information bewilders the potential user and he
is not able to keep up with the relevant publications in his own discipline leave aside the
related disciplines. So a constant effort is always neede to search for solutions to absorb
the high flow of new scientific literature. Text-mining tools are becoming indispensable
now for extracting information from the biomedical literature. The natural language
processing field has distinguished between information retrieval and information
extraction with information retrieval said to be recovering a pertinent subset of
documents while information extraction seen as a process of obtaining pertinent
information from documents [72].
Text mining can be defined as a knowledge extracting method to extract useful
and previously unknown information from a document set of texts through the
identification of facts inherent and inexplicit in the data. Biomedical Text mining is based
on using the automated text mining tools for extracting the vast amount of knowledge
existing in the biomedical literature. A biomedical text mining tool includes a component
of text mining which extracts biomedical concepts and entities in the literature and then
the relationships between biomedical entities are detected. Relation Extraction methods
range from co-occurrence, as the simplest way to detect relations between biomedical
entities is to collect texts or sentences in which they co-occur, to patterns, detecting
71
individual hypothetical instances of relations, which can be aggregated over a corpus, and
then to fuller parsing, producing more elaborate syntactic information [73].
The text mining systems can have a wide scope ranging from very simple tasks of
recognizing named entities and their categorization to more complex tasks of
summarizing, question answering, processing non-textual texts. All these approaches can
be further used for making literature based discoveries that can become the foundation of
new hypothesis to research upon. More recently, the focus has shifted for text mining
systems to address the user specific needs and the specialized applications are developed
for solving the user problems.
We intend to develop a text mining system which would allow scientists to
formulate advanced search queries, unlike the typical “bag of terms” queries adopted by
most search engines today. In essence, scientists would rely on our system to
automatically integrate the knowledge from several information sources while
formulating an advanced query, which otherwise would be very challenging. Such an
advanced query, represented as a graph, would include concepts (and their synonyms)
and data instances, all semantically interconnected by relevant relationships retrieved
from ProKinO. The query would be used to search for publications in which the concepts
and data items as recognized in a document section would match such a query graph, or
its significant sub-graphs.
5.3 Cancer Genome Annotation
One of our main focuses at present is on creation of an automated cancer genome
annotation system based on ProKinO. Genomic annotation is defined as the process of
72
marking the genes and other biological features in a DNA sequence. Dr. Owen White was
the first person to develop a software system of genome annotation in 1995. He was the
member of the team at The Institute for Genomic Research [74] that sequenced and
analyzed the first genome of a free-living organism to be decoded.
Cancers arise due to the buildup of mutations in critical genes that change normal
programmes of cell proliferation, differentiation and death [75]. The cancer research has
its focus on finding the mutated genes that are implicated in cancer development. A
‘census’ of cancer genes that are also known as oncogenesis, indicates that mutations in
more than 1% of genes contribute to human cancer and the protein kinase is the domain
most commonly found among known cancer genes [76]. The coding sequences of the
protein kinases make a much larger sample of cancer genome to look into the general
patterns of somatic mutation in human cancers [77].
To consistently and accurately annotate protein kinase mutations in upcoming
cancer genome sequencing studies we introduced a class in ProKinO to provide a short
description regarding the structural location and evolutionary conservation for every
mutation. Once the annotation class is sufficiently populated, we will develop an
application that will essentially transfer information from the annotation class to a newly
identified mutation in cancer genome sequencing studies. We realize that novel mutations
that do not exist in ProKinO cannot be annotated this way. However, by frequently
updating the ontology, we will be able to address this issue. In this way we will be able to
provide a consistent annotation for protein kinase mutations discovered in cancer
genomes and allow cancer researchers to prioritize mutations for experimental studies.
73
CHAPTER 6
CONCLUSION AND FUTURE WORK
Protein kinases are a large family of proteins that are implicated in many diseases
such as human cancer and have been broadly studied both from the basic and clinical
point of view. In the public domain, there are a few ontologies available that are serving
the domains of protein families, but none of these is directly related to the protein kinases
and there is a need in satisfying the requirements of the protein kinase community. To
fill this existing need and keeping in view the huge importance of protein kinases in
protein family, we have developed a Protein Kinases Ontology (ProKinO) that is a
comprehensive and a specialized ontology for protein kinases.
The data and information about protein kinases domain is spread across several
heterogeneous resources and most of these resources are storing data in different formats
following different schema. There are many challenges faced by the protein kinase
community due to the difficulty in integrating data from these disparate sources and
heterogeneous data formats and that becomes a hindrance in utilizing existing knowledge
for research related to diseases like cancer. ProKinO is an endeavor to deal with this
problem and provide a framework for detailed understanding of the relationships between
sequence, structure, function and disease in the protein kinase family.
ProKinO has been developed to capture, integrate and represent sequence,
structure, function, motif and disease information on protein kinases and provide a
sharable and consistent vocabulary to formally specify concepts and their relationships in
74
the domain of protein kinases. Our ProKinO population system does this by
automatically extracting information from diverse sources, such as Protein Data Bank
(PDB), Protein Families database (Pfam), KinBase, Catalogue of Somatic Mutations in
Cancer (COSMIC), and The Universal Protein Resource (UniProt) and then integrating
data to automatically populate knowledge in the ontology. The ProKinO then serves as a
knowledge base about the protein kinase domain allowing the protein kinase community
to navigate this specialized knowledge in one place and also building applications of their
interest.
ProKinO has become the basis for ongoing work on developing a text mining
system that mines the wealth of protein kinase literature accumulating constantly due to a
huge growth in the information about the structure, function, interaction and evolution of
protein kinases. The text mining system would allow scientists to formulate advanced
search queries, unlike the typical “bag of terms” queries adopted by most search engines
today. In essence, scientists would rely on our system to automatically integrate the
knowledge from several information sources while formulating an advanced query,
which otherwise would be very challenging.
To provide an elementary access to the ProKinO knowledge along with certain
capabilities specifically sought by protein kinase community, we are building an ontology
browsing and visualization tool for ProKinO. This browsing visualization tool will be
available via a standard Web browser. Through this utility researchers can pose queries
for knowledge extraction along with general user friendly navigational capabilities.
The integrated knowledge in ProKinO will be used to consistently and accurately
annotate protein kinase mutations in upcoming cancer genome sequencing studies. This
75
way the protein kinase mutations discovered in cancer genomes can be provided a
consistent annotation and cancer researchers can prioritize mutations for experimental
studies.
The OBO foundry establishes the main requirements for joining OBO and one has
to agree to adopt and refine a set of principles that prove effective for ontology
development in serving the biomedical research community. As OBO principles have
been followed in the development of ProKinO and it serves an important domain that is
not already covered in any of the available ontologies, we hope it to be a part of OBO in
the near future. The efforts are underway to get ProKinO included in the Open
Biomedical Ontologies Foundry.
In the future, one goal can be to integrate the pathway and sequence variation data
for visualization, analysis and modeling purposes using the knowledge present in
ProKinO. Presently, ProKinO has been developed for protein kinases which are a specific
family of proteins and it focuses only on human organism, but in the future this work can
be extended to include knowledge for other related important biomedical families of
proteins such as phosphates and hydrolyses, and also incorporating other organisms.
76
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