Translational bioinformatics: linking knowledge
across biological and clinical realms
Indra Neil Sarkar,1,2,3Atul J Butte,4Yves A Lussier,5,6,7Peter Tarczy-Hornoch,8,9,10,11
Nearly a decade since the completion of the first draft of
the human genome, the biomedical community is
positioned to usher in a new era of scientific inquiry that
links fundamental biological insights with clinical
knowledge. Accordingly, holistic approaches are needed
to develop and assess hypotheses that incorporate
genotypic, phenotypic, and environmental knowledge.
This perspective presents translational bioinformatics as
a discipline that builds on the successes of bioinformatics
and health informatics for the study of complex diseases.
The early successes of translational bioinformatics are
indicative of the potential to achieve the promise of the
Human Genome Project for gaining deeper insights to the
genetic underpinnings of disease and progress toward
the development of a new generation of therapies.
The study of complex diseases requires the effective
integration and analysis of disparate features that
originate from genotypic, phenotypic, and environ-
mentalsources. In contrast
data type, a macroscopic approach offers a holistic
view for exploring systems of relationships.1Mean-
ingful insights from a systems theory approach
require the coalescence of many, often intractable,
heterogeneous data types.2Traditionally,biomedical
informatics innovations have focused (‘microscopi-
cally’) on innovations constrained to particular
domains3(eg, clinical innovations in health infor-
matics; biological innovations in bioinformatics).
This has led to a perceived gulf between bioinfor-
matics and health informatics, thus decreasing the
potential impact of a ‘macroscopic’ approach.
Recent years have seen recognition of the growing
need to bridge these domains through the develop-
ment of trans-disciplinary training programs and
curricula4as well as venues specifically designed to
share innovations that span the laboratory and
clinical spaces (eg, the AMIA Summit on Trans-
lational Bioinformatics). Translational bioinfor-
matics (TBI) has thus emerged as a systems theory
approach to bridge the biological and clinical divide
through a combination of innovations and resources
across the entire spectrum of biomedical infor-
Along with complementary areas of
emphasis, such as those focused on developing
systems and approaches within clinical research
contexts,6insights from TBI may enable a new
paradigm for the study and treatment of disease.
The rapid escalation of activity in TBI can be
attributed to parallel advancements in the biological
and clinical realms. In biology, we have seen
unprecedented advances in technology, such as
those associated with generation of molecular
sequences.7In healthcare, we are observing a new
era of clinical data acquisition and decision support
that is driven by Federal legislation fostering adop-
tion of electronic health records and enablement of
seamless exchange of health information.8 9The
challenges have been paralleled in the biological and
clinical realms, where there are common challenges
in heterogeneous data integration, missing data, and
semantic mapping. Nonetheless, opportunities to
develop linkages between genetic and clinical
information are also increasing as a result of
participatory initiatives, such as those promoted by
some direct-to-consumer genetic test vendors.10
Furthermore, there is great opportunity to leverage
common challenges (eg, some of the tools developed
by clinical research informatics researchers6).
The promise of the $2.7 billion Human Genome
Project was to enable scientists to understand the
genetic basis of human disease.11However, nearly
a decade since the completion of the first draft of
the human genome,12there is still much to be
elucidated. Through technological and computa-
tional advances, the $1000 genome is becoming
a very real possibility.13The availability of a large
number of complete human genomes with clinical,
phenotype, and environmental information may
enable a new paradigm for the development of new
sets of hypotheses pertaining to complex diseases,
such as those that involve multiple genes and
environmental parameters.14A major goal of TBI is
thus to develop informatics approaches for linking
across traditionally disparate data and knowledge
sources enabling both the generation and testing
of new hypotheses.15As large volumes of linked
biological and clinical data become available, the
complexity of disease may be dissected using novel
TBI approaches designed in silico, but validated in
traditional in vitro or even in vivo interventions.
BUILDING ON PREVIOUS SUCCESSES
TBI is built on the successes of research that have
evolved in the 30 years since the first use16of the
term ‘bioinformatics.’ Four notable areas germane
to the present discourse are clinical genomics,
genomic medicine, pharmacogenomics, and genetic
epidemiology (figure 1). The acceptance of clinical
genomics (which has the purpose of identifying
the clinical community can be measured by
the growing number of clinically relevant genetic
For numbered affiliations see
end of article.
Dr Indra Neil Sarkar, Center for
Clinical and Translational
Science, University of Vermont,
89 Beaumont Avenue, Given
Courtyard N309, Burlington, VT
Received 14 March 2011
Accepted 19 April 2011
Published Online First
10 May 2011
This paper is freely available
online under the BMJ Journals
unlocked scheme, see http://
354 J Am Med Inform Assoc 2011;18:354e357. doi:10.1136/amiajnl-2011-000245
medicine,’ (which aims to identify genotypeephenotype corre-
lations relevant to individuals, or haplotype variation) is posi-
tioned to uncover large-scale genotypeephenotype associations
as a result of genome-wide testing and increased resolution of
representation of clinical data. Pharmacogenomics may also
benefit from ascertaining correlations with data captured for
clinical purposes (eg, such as captured in electronic health
records). For example, it may enable correlation of genomic
measurements with clinical phenotypes observed relative to
pharmacological substances (eg, as listed in the Pharmacoge-
nomics Knowledge Base (PharmGKB)18). It may also potentially
provide patient-specific prescribing advice through decision
support systems. Finally, genetic epidemiology is rising to new
levels with the aggregation of genome-based data alongside
public health and environmental registries (eg, such as cataloged
in HuGENet19). Collectively, these sub-disciplines of bioinfor-
matics have been suggested as core to the integration of
biological and health data.20However, the mere availability of
observations or statistically significant associations is of little
practical value without explanations of potential clinical utility.
This challenge of finding true biomedical explanations has been
reflected before in medicine, for example, when improved
methods for acquiring physiological data were developed.21
The ability to sequence a patient’s genome as routinely as
other routine clinical laboratory tests is no longer a far-fetched
possibility.13Accordingly, the sheer volume of potentially
available data poses significant challenges for their integration in
a form that can be used to either test current hypotheses or
develop new ones. The heterogeneity of data suggests the need
for new multi-dimensional paradigms for knowledge integra-
tion, requiring a deeper understanding of biology than previ-
ously required by informatics practitioners. Should one only
consider single nucleotide polymorphic markers, or also include
intronic (non-coding DNA) regions that have been shown to
participate in gene regulation? Can gene expression measure-
ments capture the effects of the environment? How do we then
integrate relevant biological data, such as from proteomic
studies, and correlate them with fidelity to phenotype data to
track subtle, but essential, environmental phenomena? Parallel
to the difficulty in addressing these queries there will be signif-
icant ethical, legal, and social implication issues to consider.22
At the core of TBI is the development of new hypotheses
originating from the integration of genomic and clinical data.
TBI reflects a new era of trans-disciplinary science, and reflects
the needed unification of multi-scale biological and clinical
information for enabling the formal postulation of a deeper
understanding of disease such as originally proposed by Blois23
and more recently by Kalet.24Understanding the genomic
influences on the complex evolution of disease, the impact of
therapeutic approaches as can be measured by molecular
biomarkers, and the overall consistency of genotypeephenotypee
environmental correlations across populations forms the basis of
focus for the TBI community.
CHALLENGES IN STUDYING COMPLEX DISEASES
Understanding complex diseases toward the development and
assessment of putative therapies requires traversing between the
bench and bedside, often referred to as the ‘T1 translational
ascertain how basic science observations can be applied to clin-
ical contexts, either in the form of prognostic, diagnostic, or
therapeutic approaches to disease. As an endeavor, it represents
a grand challenge in modern medicine and also a potential
paradigm shift for how to integrate a broad set of data points.
The high dimensionality of potential data types when
considering the full array of biological and clinical data that can
be generated dwarfs any previous attempt at heterogeneous data
integration. There is therefore a need to develop the next
generation of clinical decision support systems that can incor-
porate data from massive biological datasets that will need to be
combined with relevant disease phenotype information and
computable knowledge bases to offer clinically useful sugges-
tions. Perhaps more mundane, but of equal significance, is the
need to develop approaches that can accommodate a dizzying
set of file formats and representation standards. These are not,
by themselves, completely new challenges to the biomedical
informatics community. Nonetheless, they reflect a core area of
emphasis where energy is needed to integrate knowledge across
clinical genomics, genomic medicine, pharmacogenomics, and
genetic epidemiology in light of the avalanche of additional
genomic and clinical data and the corresponding knowledge of
Amidst the challenges of knowledge integration and handling
unprecedented volumes of data, TBI is greatly challenged with
developing approaches that can bridge biological knowledge and
place it into a meaningful clinical context. The volume of data
can lead to spurious correlations that may be an artifact of the
data and neither biologically nor clinically insightful. For
26As a goal, the objective is uncomplicateddto
clinical knowledge using translational
approaches, focused on areas from
molecules to populations (eg, clinical
genomics, genomic medicine
pharmacogenomics, and genetic
epidemiology), form the foundation of
approaches that are used by
translational bioinformatics (TBI; large
bidirectional arrow). TBI thus bridges
knowledge acquired from both the
biological (using bioinformatics) and
health (using health informatics)
domains. Accordingly, the success of
TBI will result in the crossing of the T1
translational barrier, and thus link
innovations from bench to bedside.
Bridging biological and
T1 Translational Barrier
J Am Med Inform Assoc 2011;18:354e357. doi:10.1136/amiajnl-2011-000245355
example, if a physician had access to a patient’s entire genome,
how could it be leveraged to provide clinically insightful
knowledge that would not have been possible using solely data
already in a medical chart (eg, family history of a disease)? As
shown for the genomic era’s ‘Patient 0,’ it is plausible to inte-
grate genomic data with relevant clinical data to develop prog-
nostic approaches.27The potential to provide appropriate care
with respect to predicted disease outcome or efficacy of thera-
peutics offers great incentive for developing TBI approaches that
integrate the full complement of biological, clinical, and envi-
ronmental data. For this reason, phenotypic annotation of
samples whose gene expression or single nucleotide polymorphic
information is available in genomic data repositories such as
GEO28and dbGAP29is underway in different laboratories,30 31
involving methodologies that are widely used in health infor-
matics (eg, natural language processing, ontology mapping).
Finally, approaches such as those implemented by the Crimson
system32hold promise for capitalizing on the clinical data that
are captured as an artifact of standard clinical care. The extent to
which this type of relatively noisy data can be used for research
is still the object of active research by the TBI community.
Projects that involve TBI approaches to integrate biological
and clinical data are already underway. The NIH-funded
eMERGE (Electronic Medical Records and Genomics) project is
a multi-site endeavor exploring issues involved with linking
genomic information (from genome-wide association studies)
with clinical data for individuals with specific conditions.33
Other efforts such as the Personal Genome Project,34the Exome
Project,35the Million Veteran Program,36and the 1000 Genomes
Project37reflect the increasing interest of the biomedical research
and clinical communities in studying the complexity of geno-
typeephenotype relationships as well as postulating hypotheses
for disease that incorporate genomic data. In addition to human-
based genome projects, there are also initiatives such as the
Human Microbiome Project (HMP38) and Metagenomics of the
Human Intestinal Tract (MetaHIT39) that strive to provide
a census of commensal microbial flora potentially related to
THE EMERGING TBI TOOLBOX
The relationship between bioinformatics and health informatics,
while conceptually related under the umbrella of biomedical
has not always been very clear. The TBI
community is specifically motivated with the development of
approaches to identify linkages between fundamental biological
and clinical information. As technological advances continue to
produce data that enhance our ability to further understand the
biological underpinnings of complex diseases,41the clinical
community will depend on the development of approaches to
interpret these data such that they can be clinically actionable.
TBI approaches are emerging as a melding of a complemen-
tary suite of techniques that strive to meet this need. Network
approaches42have led to the development of new techniques to
study drugetarget43and geneedisease relationships44as well as
to provide a deeper understanding of the human metabolism.45
Techniques have also been developed to combine genomic and
public datasets for studying allelic variation at the population
level.46Systems biology approaches have been used to identify
genomic signatures that correlate with the potential efficacy of
vaccines.47Finally, high-throughput sequence based approaches
are showing promise for the identification of prognostic genetic
markers for increasing numbers of rare diseases.48e50As the
results of these early successes suggest, the TBI community is
beginning to work closely with biomedical scientists to develop
a new cadre of approaches to study the complex relationships
between genotypic, phenotypic, and environmental data.
Building on these endeavors will bring us closer than ever before
to an entirely new generation of prognostic tests and highly
effective and personalized clinical interventions.
The decade following the completion of the first draft of the
human genome has witnessed unprecedented technological
advancements that have led to the increasing prominence and
importance of bioinformatics and health informatics for biology
and healthcare, respectively. The exponential growth of genomic
data, along with parallel achievements in acquiring and
analyzing clinical data position the biomedical research enter-
prise to deliver on the promise of the Human Genome Project.
TBI is accordingly positioned to enable a systems view of
1Center for Clinical and Translational Science, University of Vermont, Burlington,
2Department of Microbiology and Molecular Genetics, College of Medicine, University
of Vermont, Burlington, Vermont, USA
3Department of Computer Science, College of Engineering and Mathematical
Sciences, University of Vermont, Burlington, Vermont, USA
4Division of Systems Medicine, Department of Pediatrics, Stanford University School
of Medicine, Stanford, California, USA
5Section of Genetic Medicine, Department of Medicine, University of Chicago,
Chicago, Illinois, USA
6UC Comprehensive Cancer Center, Ludwig Centre for Metastasis Research, University
of Chicago, Chicago, Illinois, USA
7Institute of Genomics and Systems Biology, Institute for Translational Medicine,
Computational Institute, University of Chicago, Chicago, Illinois, USA
8Division of Biomedical and Health Informatics, University of Washington, Seattle,
9Institute of Translational Health Sciences, University of Washington, Seattle,
10Institute for Genomic Medicine, University of Washington, Seattle, Washington, USA
11Department of Computer Science, University of Washington, Seattle, Washington,
12Division of Biomedical Informatics, University of California San Diego, La Jolla,
Acknowledgments The authors wish to acknowledge Casey Overby, PhD and
Elizabeth Chen, PhD for valuable discussion.
Funding INS is funded in part by a grant from the National Institutes of Health
(R01LM009725). AJB is funded in part by grants from the Lucile Packard Foundation
for Children’s Health, Hewlett Packard Foundation, and the National Institutes of
Health (R01LM009719). YAL is funded in part by a grant from the National Institutes
of Health (UL1RR024999). LOM is funded in part by grants from the National Institutes
of Health (R01LM009520, U54HL108460, and UL1RR031980), the Komen Foundation,
and the Agency for Healthcare Research and Quality (R01HS019913). PTH is funded in
part by grants from the Washington Life Sciences Discovery Fund (‘Institute for
Genomic Medicine’) and National Institutes of Health (T15LM07442, UL1RR025014,
Competing interests INS, YAL, PTH, and LOM declare they have no competing
interests. AJB COI has been submitted in accordance to the ICMJE COI form.
Provenance and peer review Not commissioned; externally peer reviewed.
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