Open Access

Integration of omics sciences to advance biology and medicine

Clinical Proteomics201411:45

https://doi.org/10.1186/1559-0275-11-45

Received: 3 September 2014

Accepted: 2 December 2014

Published: 15 December 2014

Abstract

In the past two decades, our ability to study cellular and molecular systems has been transformed through the development of omics sciences. While unlimited potential lies within massive omics datasets, the success of omics sciences to further our understanding of human disease and/or translating these findings to clinical utility remains elusive due to a number of factors. A significant limiting factor is the integration of different omics datasets (i.e., integromics) for extraction of biological and clinical insights. To this end, the National Cancer Institute (NCI) and the National Heart, Lung and Blood Institute (NHLBI) organized a joint workshop in June 2012 with the focus on integration issues related to multi-omics technologies that needed to be resolved in order to realize the full utility of integrating omics datasets by providing a glimpse into the disease as an integrated “system”. The overarching goals were to (1) identify challenges and roadblocks in omics integration, and (2) facilitate the full maturation of ‘integromics’ in biology and medicine. Participants reached a consensus on the most significant barriers for integrating omics sciences and provided recommendations on viable approaches to overcome each of these barriers within the areas of technology, bioinformatics and clinical medicine.

Keywords

Omics integration Omics science Clinical application Risk prediction Proteomics Metabolomics Genomics

Introduction

The past two decades have been witness to an explosion of data stemming from the development and gradual maturation of ‘omics’ technologies and bioinformatics. Today, whole-genome sequencing has become a routine research tool, and state-of-the-art proteomic technologies have caught up to genomics in the past few years in terms of coverage as evidenced by their ability to identify a large percentage of all observed human gene products, including functionally significant alternative splice variants [14]. Nevertheless, the omics mindset has not yet permeated the broad biological and clinical community. Of the ~20,000 genes in the human genome, only 10% have 5 or more publications [5], while one gene, p53 that regulates the cell cycle and functions as a tumor suppressor, is the subject of over 56,000 articles in scientific literature. Clearly, our technological abilities to generate large amounts of data from molecular systems have advanced enormously, but the ability to translate this information for use in the clinic remains elusive due to a number of factors. One key reason postulated is that while individual omics domains yield distinct and important information, no single omics science is sufficient to facilitate a comprehensive understanding of the complex human biology and physiology. Additionally, there are logical scientific steps missing in leaping from a lack of information on 90% of the proteins to clinical use. The integration of omics sciences bioinformatically remains a challenge and thus a limiting factor in fully extracting biological meaning from the mounds of data being generated. For instance, the NCI’s The Cancer Genome Atlas (TCGA) integrated multiple data types to identify three mutually exclusive pathways that affect the development of glioblastoma multiforme (e.g., RTK, TP53, RB) [6], suggesting that the presence of one aberration removes the selective pressure for a second aberration. This example demonstrates the immediate value of data integration since these pathways were not observed from data in isolation (either from mutations, copy number changes, or other measurements). Omics integration is the next logical and necessary step in propelling systems biology and medicine forward and potentially allowing for its use in the clinic. NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) is one such multi-institutional initiative that employs proteogenomic integration to better enhance our understanding of cancer biology using genomically characterized tumors [7], and there are similar international efforts such as uniting the chromosome-centric human proteome project with the Encyclopedia of DNA Elements (ENCODE) [8].

Executive summary

In light of previous workshops addressing the challenges and opportunities of clinical proteomics in biology and medicine [9, 10] and the advancement of proteogenomic science, the NCI and NHLBI organized a workshop focusing the topic of integrating omics datasets obtained from multi-omics technologies to provide broader insights into disease pathophysiology. The workshop was held on the National Institute of Health (NIH) campus in Bethesda, MD on June 19 and 20, 2012 with participants from a diverse variety of scientific expertise. Herein, this report summarizes the major challenges and proposes solutions for omics integration in an effort to raise support and awareness of omics integration within the scientific community. It is hoped that this report will initiate new collaborative efforts that harness the vast amount of knowledge embedded in disparate data sets and promote training of more multidisciplinary scientists better positioned in the science of omics integration (integromics).

Workshop overview

To identify key limiting factors and challenges in integromics and provide actionable solutions to overcome such roadblocks in the context of biology and diseases, the workshop was structured to ground discussions upon three case studies - personal omics profiling [11], multi-omics pathway analysis of cardiovascular-specific circadian clock [12], and glycoproteomics [13]. In addition, experts from the Framingham Heart Study presented a “lessons learned” talk on identifying risk factors for heart disease and its associated studies using omics-based technologies on a much larger patient population [14, 15]. Next, workshop participants broke off into multi-disciplinary groups for further discussion in order to develop integrative solutions to address three major areas of challenges (clinical, informatics, and technology) identified. For example, questions were raised by the participants during rounds of discussions, including: (1) Can omics improve the odds ratio for diabetes or heart disease prediction in cardiovascular research? (2) Can omics science provide the context for cancers that begins as genetic aberrations? Collectively, six major recommendations for facilitating omics integration were put forth and summarized below.

Case studies

Personal omics profiling (case study 1)

The case study described by Dr. Michael Snyder from Stanford University illustrated how integration of different omics data can facilitate a shift from disease treatment to prevention based on his own experience. Discussed was how longitudinal personalized omics profiling (POP) from analysis of the genome, epigenome, transcriptome, proteome and metabolome (“Snyderome”) can collectively provide useful information that otherwise could not be gleaned from any single individual omics domain (data sets) alone. The “Snyderome” included routine measurements interspersed with dense sampling during states of infection. Integrative analyses of the data revealed an increased insulin biosynthetic pathway that spiked during states of viral infections [11]. The data further indicated Dr. Michael Snyder to be at an increased risk of type 2 diabetes, despite having no known family history of the disease, which subsequently proved true. This highlights the fact that following multiple omics components longitudinally may provide valuable information about disease risk, drug sensitivity, and other components of personalized medicine.

This POP study simultaneously illustrated the potential of omics integration. Clearly, methods exist to shift less studied areas of medicine from hearsay and conjecture to data-established-truth. Yet, POP studies are hardly scalable across a population due to an analysis cost of $10,000 per sample. Furthermore, progress in POP research requires people to allow the collection of their omics profiles. This is a delicate subject as the collection of so much data will increase the likelihood of false positives and induce undue or premature emotional strain. The so-called, “democratization of data”, namely the shift from expert protectionism to people governing their own data, has led to the possibility of better decision-making which might significantly impact the choices they make day-to-day. Although this can be done in medicine, the challenge remains to protect human subjects without hindering research, while restraining clinical adoption until clear data-driven-truths have been clinically validated.

Pathways and targets to modulate clocks (case study 2)

Dr. John Hogenesch from University of Pennsylvania discussed the utility of omics integration to identify clock-modifying genes and pathways. The circadian clock regulates many aspects of biology, including core body temperature, organ function, heart rate, and blood pressure, among others. Clocks are present in most of the body’s cells and interestingly most cancers appear to have lost their circadian clocks.

Omics approaches that include whole-genome siRNA circadian genomic screens, gene expression data, and protein-protein interaction data are used to identify clock-modifying genes and define their mechanistic and functional attributes [16]. The insulin signaling pathway is one of the most significant clock-modifying pathways identified by such an approach. Dr. John Hogenesch discussed the use of Bayesian integration strategies to help assess whether the evidence provided by a given result indicates that the gene is a core clock component. Additional discussion on major challenges for integrating omics results include the use of different synonyms by the scientific community (e.g., multiple names for a given gene and/or its variants, and access to high-quality standardized data sets for "trustworthy" analyses).

Glycoproteomics (case study 3)

Drs. Gerald Hart and Jennifer Van Eyk from Johns Hopkins University discussed the fields of glycobiology, highlighting the critical nature of integrative approaches since one omics domain cannot adequately explain the underlying biology. Dr. Gerald Hart estimated that 90% of proteins are glycosylated, and glycosylation is involved in nearly all cellular activities and metabolic processes. He also noted that post-translational modifications (PTMs), such as glycosylation, greatly expand the genetic code’s chemical diversity, and hence, function cannot be inferred through genomics approaches alone. “Glycomics” is defined as the study to characterize or quantify the glycome of a cell, tissue, or organ. Glycome complexity is a reflection of cellular complexity and the collective tools of genomics, proteomics, lipidomics and metabolomics are required for functional characterization. Challenges to the integration of glycomics include a lack of integration of glycan data into mainstream databases, a lack of standardization across existing glycomic databases, and a lack of clarity regarding different levels of glycan “structure” in published literature. A further challenge is the paucity of measurement tools for site-specific identification and quantitation of glycoproteomics.

The Framingham heart study (lessons learned)

The Framingham Heart Study was initiated in Framingham, Massachusetts in 1948 to understand the underlying causes of cardiovascular disease (CVD). The study aimed to investigate the expression of coronary disease in a normal population, determine factors that predispose individuals to develop CVD, and evaluate new screening tests (e.g., electrocardiography, blood metabolites). Currently, the Framingham Study incorporates a systems biology approach to biomarker research [i.e., CVD Systems Approach to Biomarker Research (SABRe) initiative], aiming to identify biomarker signatures of CVD and its major risk factors using omics technologies. Dr. Andrew Johnson from NHLBI summarized omics data collected to date, in which studies have profiled three generations of families across thousands of phenotypes with many of them being longitudinal. Specific data collected include 8,500 genome-wide association studies, 7,000 cell line analyses, 300 whole exome sequences, 1,000 whole-genome sequences, 5,000 DNA microarrays, 2,000 metabolomics analyses, and ongoing data collection with induced pluripotent stem cells, DNA methylation, computed tomography scans, and magnetic resonance imaging. Challenges identified in the Framingham Heart Study include data acquisition (e.g., throughput, cost, and sample tracking/batch effects), storage (e.g., results, storage demands, raw data in one place for cross-comparison, etc.), and limitations with data processors, competing needs on servers, costly renewal of outdated resources, and security issues.

Roadblocks in integrating omics knowledge in biology and medicine

Discussions regarding roadblocks and challenges in omics science that took place following the presented case studies are outlined below with a focus on three main areas - clinical utility, informatics, and technology.

Clinical utility challenges

Two fundamental challenges that were identified for the integration of omics into medicine included (1) disseminating, managing, and interpreting omics data in a clinical context, and (2) ensuring that omics results have added value to existing paradigms of patient care. Providing a solution to these problems should allow for enhanced preventative, diagnostic, and prognostic procedures [17]. The democratization of multi-omics data is a key aspect of the integration of omics data in medicine. While the physical barriers to access, management, and transfer of data have been removed through the digitalization of data files, clinical utility of research data is limited by privacy and other barriers, justly placed to prohibit the abuse of protected health information. However, the ease of disseminating, managing, and interpreting massive amounts of omics data would allow for quicker application of integrative omics knowledge to clinical practice.

Transforming and incorporating data derived from different omics approaches into a defined clinical context is essential, but remains complex and problematic [18, 19]. Genomic scans, for example, have started to identify more and rarer variants in addition to common SNP variants [20], and when different commercial platforms are used to molecularly analyze a common sample, variability is often found in their risk prediction capacities [21]. This variability most likely lies in data interpretation models that incorporate different assumptions during data processing and widespread problems of overfitting high dimensional data with an extremely large number of molecular measurements relative to limited sample size [19]. This begs the question of how well a genetic variant correlates to a specific disease condition and whether predicted disease risks have any clinical validity. In the age of declining genotyping costs and retail genome sequencing kit, consumers can now obtain data on their own personal DNA, and patient expectations of clinicians providing useful genetic information are soaring. Therefore, a disconnect is growing between the realistic, operable utilities of omics sciences and the expectations of patients with little clarity on how to bridge the gap. Finally, legitimate concerns about how to keep data and results private and secure are becoming more prominent.

The second major clinical challenge lies in determining, through appropriate studies, whether the new omics findings add incremental value to current clinical practices or clinical decision making. While multiple omics technologies can potentially discover a host of biological candidates from samples, their clinical utility requires rigorous validation. Hence, discovery-based omics research should seek to maximize the signal-to-noise ratio of a biomarker candidate(s) in order to produce fewer false leads [19]. Furthermore, it is important to distinguish the causes of pathogenesis versus markers that indicate disease phenotypes, since causes are often treatable and have robust associations (e.g., LDL and atherosclerosis [22]), whereas markers of disease are the often most powerful predictors. Although the markers of diseases can guide diagnosis and treatment, their effects are not a direct target for treatment (e.g., you can treat LDL, but you do not treat Troponin). Cholesterol was studied for over 100 years prior to becoming a clinically useful biomarker. However, it is uncertain that any new biomarker candidates from omics studies alone or in combination to cholesterol perform better than cholesterol alone. Such complex barriers need to be adequately addressed to be of help in actionable clinical decision-making.

Informatics challenges

Three major challenges identified in informatics that limit the integration of omics data in the clinic were (1) the development of more mature models of cellular processes that incorporate non-commensurate omics data types [23, 24], (2) data storage limitations and organization of fragmented data sets, and (3) a shortage of multidisciplinary scientists with training in biology, computer science, informatics and statistics.

Omics integration includes the incorporation of multiple omics data types into a comprehensive model that accurately describes biological processes. The simplest model assumes the “central dogma” and maps transcripts and proteins to gene sequences. Slightly more sophisticated models entail quantitative information and use correlations across molecular entities. As each “ome” reflects a distinct biological domain (e.g., transcripts, proteins, metabolites), the resulting datasets represent the measurements of various underlying variables on different scales. For example, transcriptional and translational profiles for mRNA transcripts and corresponding proteins are often but not always the same [2527]. To capture both the temporal and spatial dynamics of biomolecules embedded within complex biological relationships, the most complex models must appropriately integrate all pertinent, distinct measurements of the various Omes. However, the modeling of non-commensurate data types comprised of nonlinear relationships and multivariate signals is extremely complex, and current computational algorithms and statistical procedures are limited in this capacity. Additionally, the non-synonymous naming systems for the myriad of biological molecules in the various Omes further complicate algorithm development and inhibit omics integration. As discussed previously, modeling would be greatly aided by the standardization of gene names (e.g., circadian clock genes). Once a model is established, faster and more efficient methods are required to validate computational results in cellular and animal model systems, representing a huge challenge in the field of integrative omics science [28].

This specific challenge is particularly difficult to address, involving many aspects of the scientific and clinical disciplines dependent on the diseases, including but not limited to:
  1. a)

    relative risk of disease or adverse outcome is often arbitrarily assigned,

     
  2. b)

    association does not necessarily equal prediction,

     
  3. c)

    insufficient sample numbers in some studies,

     
  4. d)

    difficult to extrapolate from n = 1 to a population and to model the environment, and

     
  5. e)

    modeling needs to be performed by computers and not by physicians, with results translated to a scale that physicians can easily understand (e.g., 10-year coronary heart disease risk).

     

The second bioinformatics challenge for omics integration involves the storage of large, heterogeneous datasets generated from multiple high-throughput omics platforms. With the continued development of more sophisticated instrumentation for data acquisition, the amount of data generated is exponentially rising, along with the demand for data storage. As the usage of stored data occurs at distinct levels (e.g., raw data vs. mass spectrometry search results files in proteomics, or raw nucleotide sequence reads vs. variant calls in vcf format in genomics) specific to a particular expertise in the multi-disciplinary end user pool (e.g., computer scientists vs. genome biologists), data storage infrastructure should be stratified and specifically tailored to meet the needs of end users. If storing all data is cost-prohibitive, the difficulty lies in determining which data are the most valuable to keep. Furthermore, datasets are heterogeneous with respect to both intra-omics (e.g., proteomic datasets from different file formats) and inter-omics (e.g., genomic vs. proteomic datasets) acquisition protocols. This results in a storage infrastructure that is fragmented and disjointed, thereby hindering cross-comparison and retrograde use by the scientific community. Security and privacy of stored clinical data is an additional issue for avoiding ethical concerns.

The participants collectively put forth recommendations to overcome informatics barriers by:
  1. a)

    establishing data standards for all types of omics data files (e.g., cite genomics and proteomics papers),

     
  2. b)

    changing access to data [29] to protect research subjects without hindering valuable research opportunities,

     
  3. c)

    completing the incomplete reference databases (~1/3 of SNPs in dbSNP), such as using proteomics data to confirm/verify gene annotation [30], and adding PTMs that are not routinely integrated in mainstream databases,

     
  4. d)

    calculating some key parameters for data processing and storage, such as how many times will a raw file be processed? How long will it need to be stored? How frequently do data analysis methods change?

     
  5. e)

    providing sufficient incentive to data generators for data deposition into publicly accessible repositories although great stride has been made in the past few years such as dbGAP and ProteomeXchange [31], and

     
  6. f)

    overcoming data storage and computing power limitations.

     

The third major bioinformatics challenge is primarily driven by technology. Rapidly evolving analytical methods unleash new measurements which in turn give rise to new types of data and data analysis. Hence, there is a constant requirement for scientists including bioinformaticians to keep up with the developing technologies and methodologies. Most experts in the field have experience in a single omics technology, such as calling mutations in next-generation sequencing data or extracting peptides from mass spectra, and those who specialize in the next higher level of data integration are rare. A combination of reasons contribute to this dearth including: rapidly changing technologies that keep bioinformaticians from continually specializing in the analysis of one molecular moiety, insufficient biomedical informatics training opportunities, and the transient nature of the interface between technology development and disease-specific research. Major adjustments to the vision and expanding the training of medical bioinformatics research community are highly recommended and required to surpass these obstacles, even though informatics training opportunities related to NIH’s BD2K initiative and others have been added more recently to address this challenge.

Technological challenges

Two major technological challenges that were recognized to limit omics integration into medicine were (1) a lack of reproducibility of data acquired through non-uniformly standardized sample preparation, including a lack of understanding of the impact of pre-analytical variables on samples [32], and inconsistent instrument performance [19], and (2) a lack of high-throughput and multiplexing methods that make parallel measurements of multiple types of analytes for handling large clinical studies. Addressing such obstacles, the scientific community has come a long way to demonstrate the analytical robustness of genomic, proteomic, and metabolomic workflows, including data analysis pipelines as witnessed by a flurry of standardization/harmonization activities during the last two decades in several omics areas including Genomic Standards Consortium, CPTAC, HUPO and ABRF [3340]. Furthermore, there have been significant technological advances in measuring genomic variants, proteins and peptides, and small molecule metabolites that include next-generation genomic sequencing, immuno-multiple reaction monitoring mass spectrometry, flow cytometry, and protein microarrays [4144]. There is no doubt that technologies will continue to be improved/developed to increase sensitivity, specificity and throughput, making it feasible to measure every molecule at the single cell level. To apply multiplexing and high throughput methods in clinical studies, researchers need to ensure that the appropriate technologies/platforms and bioinformatic analyses are analytically robust and standardized, and can be validated in an independent lab and/or in a separate set of clinical samples.

Recommendations for successful omics integration

Following rounds of discussions, six major recommendations for facilitating omics integration to address the identified roadblocks described above were put forth by workshop participants and summarized below.
  1. 1)

    Committed funding for the education of multi-disciplinary teams is needed. Clinicians, clinical scientists, basic scientists, and bioinformaticians need to be educated in these disciplines, and form collaborative, multi-disciplinary teams to carry out omics integration from discovery to the patient. Omics sciences are inherently integrative of multiple specialties. Therefore, all phases of discovery efforts, including sample procurement, experimental design and bio-interpretation, and all phases of clinical translation including clinical trials and implementation into clinical procedures must be performed by a multi-disciplinary team of investigators. From this, appropriate epidemiological and statistical measures should be applied for determining whether a newly discovered marker or panel of markers adds value to pre-existing clinical regimes of risk prediction, diagnosis and prognosis. Furthermore, end users need to be educated on the realistic utilities of omics results at each stage of omics development. This can be accomplished via public seminars or via genetic counselors acting as a liaison between clinicians and patients. This will lessen unrealistic expectations of the public for physicians to infer patient risk from the results of omics studies. In the long term, committed funding to create a new discipline of omics sciences is needed, providing rigorous training in the omics sciences in order to create a group of specialized experts to propel the field forward. Fellowships are needed for young scientists in the field of omics sciences to train future experts. Specifically, there is a need for the development of informatics training centers that produce experts who derive meaning from large omics datasets, including data curators and wranglers.

     
  2. 2)

    Committed and sustained funding for technology development is needed. In particular, further developments are needed in mass spectrometry instruments and technologies (e.g., top-down MS) in order to sequence deeper proteomes and/or metabolomes, and to allow for high throughput multiplexed analysis.

     
  3. 3)

    Sample preparatory procedures and acquisition must be standardized to allow for reliable cross-comparison, sharing and integration of large omics datasets and for whole-omics profiling from the same sample.

     
  4. 4)

    The development of an unifying resource is needed to permanently store data in a coordinated and structured manner. This resource would provide security, privacy and consensus on how data are stored and accessed by the community. This is critical for the integration of omics sciences and one where the National Institutes of Health (NIH) can play a significant role.

     
  5. 5)

    Mature models for integrating non-commensurate data types are needed. Algorithms must be developed for data compression, integration, querying and display to handle the distinct data types of omics sciences. Quality control algorithms should be developed for data format and exchange, and natural language data mining.

     
  6. 6)

    A consensus needs to be developed in order to create validity and value for integrating omics findings into clinical guidelines. Useful, reliable and valid metrics for establishing association and prediction in diagnostic and prognostic studies need to be utilized. Moreover, calculations for diagnostic and prognostic purposes need to be locked down and automated within a laboratory in order to remove any inconsistencies stemming by physician bias or interpretation. Translating scores to a scale that physicians can understand and converting to a single scale that can be modified over time is very important in this process [19, 45].

     

Conclusion

Omics science has transformed biology and has the potential of transforming medicine. This workshop was a first step on opening a dialogue amongst scientists and clinicians in relevant omics disciplines to (1) update recent progress and further emphasize the importance of omics science and its potential in transforming biology and future clinical practice, (2) discuss barriers in omics integration existent in a variety of forms, and (3) put forth recommendations to overcome such barriers to enable the science to move forward.

Declarations

Acknowledgement

We thank all the workshop participants for their contribution to the discussions and recommendations with regards to the topic of omics integration.

Workshop participant list

Bishow B. Adhikari, Ph.D.

Program Director

Heart Failure and Arrhythmias Branch

Division of Cardiovascular Diseases

National Heart, Lung, and Blood Institute

National Institutes of Health

6701 Rockledge Drive, RKL2 BG RM 8186

Bethesda, MD 20892 USA

Phone: +1 (301) 435-0504

Fax: +1 (301) 480-7404

Email: bishow.adhikari@nih.gov

Ivor J. Benjamin, M.D.

Chair and Professor

Departments of Medicine and Biochemistry

School of Medicine

Health Sciences Center

University of Utah

30 North 1900 East, Room 4A100

Salt Lake City, UT 84132 USA

Phone: +1 (801) 587-9785

Fax: +1 (801) 585-1082

Email: ivor.benjamin@hsc.utah.edu

Aruni Bhatnagar, Ph.D.

Professor

Institute of Molecular Cardiology

Department of Medicine/Cardiology

University of Louisville

580 South Preston St.

Delia Baxter Building, Room 421 F

Louisville, KY 40202 USA

Phone: +1 (502) 852-5966

Fax: +1 (502) 852-3663

Email: aruni@louisville.edu

Emily Boja, Ph.D.

Program Manager

Office of Cancer Clinical Proteomics Research

National Cancer Institute

National Institutes of Health

9000 Rockville Pike

Building 31, Suite 10A52

Bethesda, MD 20892 USA

Phone: +1 (301) 451-8883

Fax: +1 (301) 496-7808

Email: bojae@mail.nih.gov

Kimberly Bunje

Senior Administrative Analyst

NHLBI Proteomics Coordinating and Administration Center

Departments of Physiology and Medicine/Cardiology

University of California at Los Angeles

CHS 14-142

10833 LeConte Ave.

Los Angeles, CA 90095 USA

Phone: +1 (310) 825-5175

Fax: +1 (310) 267-5623

Email: kbunje@medmet.ucla.edu

Sonia L. Calcagno

Science Program Coordinator

Office of Cancer Nanotechnology Research

Office of Cancer Clinical Proteomics Research

National Cancer Institute

National Institutes of Health

31 Center Drive, Suite 10A52, MSC 2580

Bethesda, MD 20892

Phone: +1 (301) 594-5612

Fax: +1 (301) 496-7807

Email: calcagnosl@mail.nih.gov

Josef Coresh, M.D., Ph.D., M.H.S.

Director and Professor

Cardiovascular Epidemiology & Comstock Center Departments of Epidemiology/Biostatistics

Bloomberg School of Public Health

Johns Hopkins University

2024 E. Monument Street, Suite 2-600

Baltimore, MD 21287 USA

Phone: +1 (410) 955-0495

Fax: +1 (410) 955-0476

Email: jcoresh@jhsph.edu

James M. Deleo

Section Chief

Department of Clinical Research Informatics

Scientific Computing Section

National Institutes of Health

9000 Rockville Pike, Building 10

Bethesda, MD 20892 USA

Phone: +1 (301) 496-3848

Fax: +1 (301) 496-3848

Email: jdeleo@nih.gov

Leslie K. Derr, Ph.D.

Program Director

Office of Strategic Coordination

Office of the Director

National Institutes of Health

9000 Rockville Pike, Building 1, Room 201B

Bethesda, MD 20892 USA

Phone: +1 (301) 594-8174

Fax: +1 (301) 480-6641

Email: leslie.derr@nih.gov

Valentina Di Francesco

Senior Program Officer

Bioinformatics, Structural Genomics and Systems Biology Division of Microbiology and Infectious Diseases

National Institute of Allergry and Infectious Diseases

National Institutes of Health

6610 Rockledge Dr., MSC 6603; Room 4802

Bethesda, MD 20892 USA

Phone: +1 (301) 496-1884

Fax: +1 (301) 480-4528

Email: vdifrancesco@niaid.nih.gov

Kay Fleming, Ph.D.

Writer Editor

Center for Biomedical Informatics and Information Technology

National Cancer Institute

National Institutes of Health

2115 E. Jefferson Street, Room 6047

Rockville, MD 20852

Phone: +1 (301) 594-3602

Email: flemingl@mail.nih.gov

Nancy Fournier, Ph.D., M.B.A.

Director

Génome Québec

630, boul. René-Lévesque Ouest

Bureau 2660

Montréal, Québec QC H3B 1S6 Canada

Phone: +1 (514) 398-0668 x224

Fax: +1 (514) 398-0883

Email: nfournier@genomequebec.com

Weiniu Gan, Ph.D.

Program Director

Division of Airway Biology and Disease

National Heart, Lung, and Blood Institute

National Institutes of Health

6701 Rockledge Drive, Room 10164

Bethesda, MD 20892 USA

Phone: +1 (301) 435-0202

Fax: +1 (301) 480-1336

Email: ganw2@mail.nih.gov

Scott Geromanos

Waters Corporation

5 Technology Drive

Milford, MA 01757 USA

Phone: +1 (508) 482-2904

Fax: +1 (508) 482-4524

Email: scott_geromanos@waters.com

Morgan Giddings, Ph.D.

Research Professor

Department of Biochemistry and Biophysics

College of Arts and Sciences

Boise State University

1910 University Dr., Boise, ID 83725 USA

Phone: +1 (919) 240-7007

Fax: +1 (919) 240-7356

Email: morgan@giddingslab.org

Charles A. Goldthwaite Jr, Ph.D.

Science Writer

Goldthwaite & Associates

254 Leo Avenue

Shreveport, LA 71105 USA

Phone: +1 (318) 865-5058

Fax: +1 (318) 865-5058

Email: charlesgoldthwaite@gmail.com

Gerald Hart, Ph.D.

Director and Professor

Department of Biological Chemistry

School of Medicine

Johns Hopkins University

725 N. Wolfe St. 515 WBSB

Baltimore, MD 21205 USA

Phone: +1 (410) 614-5993

Fax: +1 (410) 614-8804

Email: gwhart@jhmi.edu

Henning Hermjakob

Team Leader

Proteomics Services

EMBL - European Bioinformatics Institute

Wellcome Trust Genome Campus

Hinxton, Cambridge CB10 1SD UK

Phone: +44 (1223) 49 4671

Fax: +44 (1223) 49 4468

Email: hhe@ebi.ac.uk

Joseph A. Hill, M.D., Ph.D.

Chair and Professor

Departments of Medicine / Molecular Biology

The University of Texas Southwestern Medical Center

UT Southwestern Medical Center

5323 Harry Hines Boulevard

Dallas, TX 75390 USA

Phone: +1 (214) 645-8300

Email: joseph.hill@utsouthwestern.edu

Tara Hiltke, Ph.D.

Program Manager

Office of Cancer Clinical Proteomics Research

National Cancer Institute

National Institutes of Health

9000 Rockville Pike

Building 31, Suite 10A52

Bethesda, MD 20892 USA

Phone: +1 (301) 451-8883

Fax: +1 (301) 496-7808

Email: hiltket@mail.nih.gov

John B. Hogenesch, Ph.D.

Associate Professor

Department of Pharmacology

Perelman School of Medicine

University of Pennsylvania

Translational Research Center 10-124

3400 Civic Center Blvd., Bldg. 421

Philadelphia, PA 19104-5158 USA

Phone: +1 (484) 842-4232

Email: hogenesc@mail.med.upenn.edu

Andrew D. Johnson, Ph.D.

Tenure Track Investigator

Framingham Heart Study

National Heart, Lung, and Blood Institute

73 Mt. Wayte Avenue

Framingham, MA 01702 USA

Phone: +1 (508) 663-4082

Fax: +1 (508) 626-1262

Email: andrew.johnson@nih.gov

Youngsoo Kim, Ph.D.

Professor

Departments of Biomedical Sciences and Biomedical Engineering

Seoul National University College of Medicine/Hospital

103 Daehangno Chongno-gu

Seoul 110-799 South Korea

Phone: +82 (2) 740-8073

Fax: +82 (2) 741-0253

Email: biolab@snu.ac.kr

Christopher Kinsinger, Ph.D.

Program Manager

Office of Cancer Clinical Proteomics Research

National Cancer Institute

National Institutes of Health

9000 Rockville Pike

Building 31, Suite 10A52

Bethesda, MD 20892 USA

Phone: +1 (301) 451-8883

Fax: +1 (301) 496-7808

Email: kinsingc@mail.nih.gov

John R. Knowlton, Ph.D.

Program Director

Structural Biology and Molecular Applications Branch

Division of Cancer Biology

National Cancer Institute

National Institutes of Health

6130 Executive Blvd

Rockville, MD 20852 USA

Phone: +1 (301) 435-5226

Fax: +1 (301) 480-2854

Email: knowltoj@mail.nih.gov

Cheolju Lee, Ph.D.

Principal Researcher

Life Sciences Division

Korea Institute of Science and Technology

39-1 Hawolgok-dong

Seongbuk-gu, Seoul 136-791 Republic of Korea

Phone: +82 (2) 958-6788

Fax: +82 (2) 958-6919

Email: clee270@kist.re.kr

Daniel Levy, M.D.

Director and Professor

Framingham Heart Study

Center of Population Studies

National Heart, Lung, and Blood Institute

Boston University School of Medicine

73 Mt. Wayte Avenue

Framingham, MA 01702 USA

Phone: +1 (508) 935-3458

Fax: +1 (508) 626-1262

Email: levyd@nih.gov

Aldons J. Lusis, Ph.D.

Director and Professor

Departments of Medicine/Cardiology, Human Genetics, Microbiology, Immunology & Molecular Genetics

University of California at Los Angeles

675 CE Young Dr. MRL Bldg. RM 3730

Los Angeles, CA 90095 USA

Phone: +1 (310) 825-1359

Fax: +1 (310) 825-1595

Email: jlusis@mednet.ucla.edu

Pamela Marino, Ph.D.

Program Director

Pharmacology, Physiology and Biological Chemistry

National Institute of General Medical Sciences

National Institutes of Health

9000 Rockville Pike, Building 45

Bethesda, MD 20892 USA

Phone: +1 (301) 594-3827

Fax: +1 (301) 402-0224

Email: marinop@nigms.nih.gov

Mehdi Mesri, Ph.D.

Program Manager

Office of Cancer Clinical Proteomics Research

National Cancer Institute

National Institutes of Health

9000 Rockville Pike

Building 31, Suite 10A52

Bethesda, MD 20892 USA

Phone: +1 (301) 451-8883

Fax: +1 (301) 496-7808

Email: mesrim@mail.nih.gov

Ken Miller, Ph.D.

Vice President, Marketing

Life Sciences Mass Spectrometry

Thermo Fisher Scientific

355 River Oaks Parkway

San Jose, CA 95134

Phone: +1 (408) 965-6336

Fax: +1 (408) 965-6132

Email: ken.miller@thermo.com

Larry G. Moss, M.D.

Associate Professor

Division of Endocrinology, Metabolism & Nutrition

Sarah W. Stedman Nutrition & Metabolism Center

School of Medicine, Duke University

2100 Erwin Road, Durham

Durham, NC 27710 USA

Phone: +1 (919) 479-2310

Email: larry.moss@duke.edu

Peter J. Munson, Ph.D.

Chief, Center for Information Technology

Mathematical and Statistical Computing Laboratory

National Institutes of Health

Bldg 12A, Rm. 2039

Bethesda, MD 20892-5626 USA

Phone: +1 (301) 496-2972

Fax: +1 (301) 402-2172

Email: munson@helix.nih.gov

Larry A. Nagahara, Ph.D.

Director, Office of Physical Sciences Oncology

National Cancer Institute

National Institutes of Health

9000 Rockville Pike, Building 31, Suite 10A03

Bethesda, MD 20892 USA

Phone: +1 (301) 451-3388

Fax: +1 (301) 496-7807

Email: larry.nagahara@nih.gov

Susan E. Old, Ph.D.

Special Assistant (Detail) to Deputy Director

Office of Extramural Research

National Center for Advancing Translational Sciences

National Institutes of Health

6705 Rockledge Drive, Room 5162

Bethesda, MD 20892

Phone: +1 (301) 435-1961

Fax: +1 (301) 402-1798

Email: susan.old@nih.gov

Samuel Payne, Ph.D.

Scientist

Pacific Northwest National Laboratory

P.O.Box 999

MSIN: k8-98

Richland, WA 99352 USA

Phone: +1 (509) 371-6513

Fax: +1 (509) 371-6564

Email: samuel.payne@pnnl.gov

Peipei Ping, Ph.D.

Director and Professor

Departments of Physiology and Medicine/Cardiology

University of California at Los Angeles

MRL Building, Suite 1-609

675 Charles E. Young Dr.

Los Angeles, CA 90095-1760 USA

Phone: +1 (310) 206-0058

Fax: +1 (310) 267-5623

Email: pping@mednet.ucla.edu

Dennis A. Przywara, Ph.D.

Program Director

Heart Failure and Arrhythmias Branch

Division of Cardiovascular Diseases

National Heart, Lung, and Blood Institute

National Institutes of Health

6701 Rockledge Drive, RKL2 BG RM 8182

Bethesda, MD 20817 USA

Phone: +1 (301) 435-0506

Fax: +1 (301) 480-7404

Email: przywarad@nhlbi.nih.gov

Mona A. Puggal, M.P.H.

Epidemiology Branch

National Institute of Enviromental Sciences

National Heart, Lung, and Blood Institute

National Institutes of Health

6701 Rockledge Drive, RKL2 BG RM 10199

Bethesda, MD 20817 USA

Phone: +1 (301) 435-0704

Fax: +1 (301) 480-1455

Email: puggalma@mail.nih.gov

Robert Rivers, Ph.D.

Program Manager

Office of Cancer Clinical Proteomics Research

National Cancer Institute

National Institutes of Health

9000 Rockville Pike

Building 31, Suite 10A52

Bethesda, MD 20892 USA

Phone: +1 (301) 451-8883

Fax: +1 (301) 496-7808

Email: riversrc@mail.nih.gov

Henry Rodriguez, Ph.D., M.B.A.

Director

Office of Cancer Clinical Proteomics Research

National Cancer Institute

National Institutes of Health

9000 Rockville Pike

Building 31, Suite 10A52

Bethesda, MD 20892 USA

Phone: +1 (301) 451-8883

Fax: +1 (301) 496-7807

Email: rodriguezh@mail.nih.gov

Lisa Schwartz, Ph.D.

Program Director

Heart Failure and Arrhythmias Branch

Division of Cardiovascular Diseases

National Heart, Lung, and Blood Institute

National Institutes of Health

6701 Rockledge Drive, RKL2 BG RM 8166

Bethesda, MD 20817 USA

Phone: +1 (301) 402-4826

Fax: +1 (301) 480-1336

Email: schwartzlongal@mail.nih.gov

Belinda L. Seto, Ph.D.

Deputy Director

Office of the Director

National Institute of Biomedical Imaging and Bioengineering

National Institutes of Health

9000 Rockville Pike, Building 31, Suite 1C18

Bethesda, MD 20817 USA

Phone: +1 (301) 496-8859

Fax: +1 (301) 480-4515

Email: setob@mail.nih.gov

Svati H. Shah, M.D., M.H.S.

Associate Professor

Department of Medicine/Cardiology

Duke Center for Human Genetics

Duke University Medical Center

DUMC Box 3445

Durham, NC 27710 USA

Phone: +1 (919) 684-2859

Fax: +1 (919) 684-0928

Email: svati.shah@duke.edu

Douglas M. Sheeley, SC.D.

Senior Scientific Officer

Center for Bioinformatics and Computational Biology

National Institute of General Medical Sciences

National Institutes of Health

9000 Rockville Pike, Building 45

Bethesda, MD 20892 USA

Phone: +1 (301) 435-0755

Fax: +1 (301) 402-0224

Email: douglas.sheeley@nih.gov

Phyliss Sholinsky, M.S.P.H.

Senior Advisor

Prevention and Population Sciences Program

Division of Cardiovascular Diseases

National Heart, Lung, and Blood Institute

National Institutes of Health

6701 Rockledge Drive, RKL2 BG RM 10120

Bethesda, MD 20892-7936 USA

Phone: +1 (301) 435-0703

Fax: +1 (301) 480-1864

Email: sholinsp@nhlbi.nih.gov

Gary Siuzdak, Ph.D.

Director and Professor

Scripps Center for Metabolomics and Mass Spectrometry

Departments of Chemistry/Molecular Biology

The Scripps Research Institute

Mailcode: SR-15

10550 North Torrey Pines Road

La Jolla, CA 92037 USA

Phone: +1 (858) 784-9415

Fax: +1 (858) 784-9496

Email: siuzdak@scripps.edu

Steven Skates, Ph.D.

Associate Professor

Department of Medicine

Harvard Medical School

Massachusetts General Hospital

50 Staniford Street, Suite 560

Boston, MA 02114 USA

Phone: +1 (617) 726-4309

Fax: +1 (617) 724-9878

Email: sskates@partners.org

Michael Snyder, Ph.D.

Chair and Professor

Department of Genetics

Stanford University

300 Pasteur Dr., M-344

Stanford, CA 94305-5120 USA

Phone: +1 (650) 736-8099

Fax: +1 (650)796-6378

Email: mpsnyder@stanford.edu

Heidi J. Sofia, Ph.D., M.P.H.

Program Director

Computational Biology

National Human Genome Research Institute

National Institutes of Health

5635 Fishers Lane, Suite 4076

Bethesda, MD 20892 USA

Phone: +1 (301) 496-7531

Fax: +1 (301) 480-2770

Email: heidi.sofia@nih.gov

Pothur Srinivas, Ph.D., M.P.H.

Lead Program Director

Division of Cardiovascular Sciences

National Heart, Lung, and Blood Institute

National Institutes of Health

6701 Rockledge Drive, Room 10184, MSC 7936

Bethesda, MD 20892 USA

Phone: +1 (301) 402-3712

Fax: +1 (310) 480-2858

Email: srinivap@nhlbi.nih.gov

Sudhir Srivastava, Ph.D., M.P.H.

Chief, Cancer Biomarkers Research Group

Division of Cancer Prevention

National Cancer Institute

National Institutes of Health

6130 Executive Boulevard, Suite 3142

Rockville, MD 20852 USA

Phone: +1 (301) 496-3983

Fax: +1 (301) 402-8990

Email: sudhir.srivastava@nih.gov

Michael B. Strader, Ph.D.

Researcher

Laboratory of Biochemistry and Vascular Biology

Center for Biologics Evaluation and Research

Food and Drug Administration

9000 Rockville Pike, Building 29, Room B26

Bethesda, MD 20892 USA

Phone: +1 (301) 827-0288

Fax: +1 (301) 451-5780

Email: michael.strader@fda.hhs.gov

Danilo A. Tagle, Ph.D.

Program Director, Neurogenetics

National Institute of Neurological Disorders and Stroke National Institutes of Health

6001 Executive Blvd, Room 2114

Bethesda, MD 20892 USA

Phone: +1 (301) 496-5745

Fax: +1 (301) 402-1501

Email: danilo.tagle@nih.gov

Magdalena Thurin, Ph.D.

Program Director

Cancer Diagnosis Program

National Cancer Institute

National Institutes of Health

6130 Executive Blvd, Room 6044

Rockville, MD 20852 USA

Phone: +1 (301) 496-1591

Fax: +1 (301) 402-7819

Email: magdalena.thurin@nih.gov

Jennifer E. Van Eyk, Ph.D.

Director and Professor

JHU Bayview Proteomics Center

Departments of Medicine/Biological Chemistry and Biomedical Engineering

School of Medicine

Johns Hopkins University

5200 Eastern Avenue

Mason F. Lord Building, Center Tower, Room 602

Baltimore, MD 21224 USA

Phone: +1 (410) 550-8511

Fax: +1 (410) 550-8512

Email: jvaneyk1@jhmi.edu

John N. Weinstein, M.D., Ph.D.

Chair and Professor

Division of Quantitative Sciences

Department of Bioinformatics and Computational Biology

The University of Texas M.D. Anderson Cancer Center

Unit 1410 P.O. Box 301402

Houston, TX 77230 USA

Phone: +1 (713) 563-9296

Fax: +1 (713) 563-4242

Email: jweinste@mdanderson.org

John Yates III, Ph.D.

Director and Professor

Department of Chemical Physiology

The Scripps Research Institute

10550 North Torrey Pines Rd.

Department of Chemical Physiology, SR11

La Jolla, CA 92037 USA

Phone: +1 (858) 784-8862

Fax: +1 (858) 784-8883

Email: jyates@scripps.edu

Jun Zhang, Ph.D.

Director

NHLBI Proteomics Coordinating and Administration Center

Departments of Physiology and Medicine/Cardiology

University of California at Los Angeles

MRL Building, Suite 1-619, 675 Charles E. Young Dr.

Los Angeles, CA 90095-1760 USA

Phone: +1 (310) 794-1348

Fax: +1 (310) 267-5623

Email: jzhang@mednet.ucla.edu

Authors’ Affiliations

(1)
Office of Cancer Clinical Proteomics Research, National Cancer Institute, National Institutes of Health
(2)
Division of Cardiovascular Sciences, National Heart, Lung and Blood Institute
(3)
Omics Integration Workshop Participants

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