NCI systems epidemiology 03012019

Need for systems approaches to dissect environment and genetics in disease

Science

P = G + EType 2 Diabetes

Cancer

Alzheimer’s

Gene expression
Phenotype Genome
Variants
Environment
Infectious agents

Diet + Nutrients

Pollutants

Drugs
Chirag Patel

@chiragjp

3/1/19

NCI Systems Epidemiology workshop

Lakhani et al, Nature Genetics 2019http://apps.chiragjpgroup.org/catch/
56K twins and 700K siblings in a massive health insurance cohort
point to complex and elusive variation in 560 phenotypes
https://rdcu.be/boZeV
Chirag Patel

@chiragjp

3/1/19

NCI Systems Epidemiology workshop

56K twins and 700K siblings in a massive health insurance cohort
point to complex and elusive variation in 560 phenotypes
560phenotypes
Nature Genetics 2019http://apps.chiragjpgroup.org/catch/
h2=0.3 (🧬)
c2=0.1 (🏠)
?
https://rdcu.be/boZeV
Chirag Patel

@chiragjp

3/1/19

NCI Systems Epidemiology workshop

🐘
Chirag Patel

@chiragjp

3/1/19

NCI Systems Epidemiology workshop

Elephant in the room (full of system epidemiologists):

Is all cancer risk random?
Tomasetti and Vogelstein Science 2015
Chirag Patel

@chiragjp

3/1/19

NCI Systems Epidemiology workshop

• What techniques, cohorts, and data
measurement modalities to capture non-genetic
variation in populations?

• Does it matter?
• Is it possible to develop approaches to partition
genetic vs non-genetic vs random chance in
disease variation?

• What is “error” vs. true non-genetic signal?
Chirag Patel

@chiragjp

3/1/19

NCI Systems Epidemiology workshop

Abstract Pharos (https://pharos.nih.gov/) is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied “dark” targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. In this webinar, Dr. Tudor Oprea will introduce how to use Pharos to find targets of interest for drug discovery. The top 3 key questions that Pharos can answer: 1. What are the novel drug targets that may play a role in a specific disease? 2. What are the diseases that are related directly or indirectly to a drug target? 3. Find researchers that are related directly or indirectly to a drug target. Presenter: Tudor Oprea, MD, PhD, Professor of Medicine, Chief of Translational Informatics Division & Internal Medicine, University of New Mexico dkNET Webinar Information: https://dknet.org/about/webinar

Open zika presentation

Identification of PFOA linked metabolic diseases by crossing databases

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The increasing amount of biological data makes possible their interpretation more accurate and richer than never before. Various way of representations and interpretations of the links between those data have been applied or developed consequently to these new elements which can be taken into account in diagnostics and soon in personalized medicine. The aim of this student project was to cross data coming from various databases to be able to link Perfluorooctaoic Acid (PFOA) to one or more human phenotypes and metabolic diseases. Our approach makes possible an easy and confident interpretation on the data kept and also allow us to rank diseases linked according to their risk of correlation to a specific set of proteins.

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Abstract Mechanisms of inter-organ signaling have been established as hallmarks of nearly every pathophysiologic condition, where many exist as related and complex diseases. While significant work has been focused on understanding how individual cell types contribute and respond to specific perturbations related to common, complex disease, an equally-important but relatively less-explored question involves how relationships between organs are altered in the context of an integrated living organism. Current technical advances, such as proteomic analysis of plasma or conditioned media, have allowed for a more unbiased visualization and discovery of additional inter-tissue signaling molecules. However, one important feature which is lacking from these approaches is the ability to gain insight as to the function, mechanisms of action and target tissue(s) of relevant molecules. To begin to address these constraints, we initially developed a correlation-based bioinformatics framework which uses multi-tissue gene expression and/or proteomic data, as well as publicly available resources to statistically rank and functionally annotate endocrine proteins involved in tissue cross-talk. Using this approach, we identified many known and experimentally validated several novel inter-tissue circuits. This was this first study to directly link an endocrine-focused bioinformatics pipeline from population data directly to experimentally-validated mechanisms of inter-tissue communication. While these validations provide strong support for exploiting natural variation to discover new modes of communication, these serve as simple proof-of-principle studies and, thus, have promising potential for expansion. Some of these will be discussed during the presentation. Presenter: Marcus Seldin, Ph.D. Assistant Professor, Biological Chemistry, University of California Irvine Upcoming webinars schedule: https://dknet.org/about/webinar

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In science, data visualization serves two primary purposes. The first is to explore data sets interactively and the second is to communicate discoveries. However, the requirements for visualizations employed in these activities are very different. Therefore, the software tools used for these purposes are typically disconnected, creating significant challenges for reproducibility and effective communication of discoveries in data-driven biomedical science. In this presentation, I will address how a new approach to creating data visualization tools can connect data analysts and other stakeholders inside and outside the scientific community. I will introduce and demonstrate the "Vistories" approach that was motivated by these question. Presented at the 5th Cancer Research UK Big Data Analytics Conference on Data Visualization.

Human Disease Ontology Project presented at ISB's Biocurator meeting April 2014

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The Human Disease Ontology (DO), organized as a directed acyclic graph, represents a knowledge base of inherited, environmental, infectious diseases (http://www.disease-ontology.org). DO's textual definition model incorporates a semi-structured format describing the disease etiology built to capture the complex nature of human disease etiology within a is_a hierarchy. DO includes disease concepts for cancer, metabolic disease, infectious disease, mental disorders, genetic disease and syndromes. DO contains disease definitions, external references to resources including ICD, NCI-metathesaurus, SNOMED, MeSH and OMIM and extended relationships that conform to OBO guidelines. DO provides a central ‘switchboard’ for connecting resources, datasets, and computational tools that include disease terms or relationships.

eumr_issue_2-2_2015-2.compressedSharon Hsieh

Michael Valentine CVMichael Valentine, Ph.D.

Introduction to Gene Mining Part A: BLASTn-off!

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What's hot

Big data and the exposome, Oregon State 040616

Data analytics to support exposome research course slides

Informatics and data analytics to support for exposome-based discovery

Methods to enhance the validity of precision guidelines emerging from big data

Correlation globes of the exposome 2016

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Identification of PFOA linked metabolic diseases by crossing databases

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Data Visualization in Biomedical Sciences: More than Meets the Eye

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Human Disease Ontology Project presented at ISB's Biocurator meeting April 2014

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eumr_issue_2-2_2015-2.compressedSharon Hsieh

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What's hot (20)

Big data and the exposome, Oregon State 040616

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Methods to enhance the validity of precision guidelines emerging from big data

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Identification of PFOA linked metabolic diseases by crossing databases

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Data Visualization in Biomedical Sciences: More than Meets the Eye

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