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Baltimore Convention Center Baltimore, MD Saturday, May 6, 2017 8:30am-4:30pm BIG DATA: CURRENT STATUS AND FUTURE DIRECTIONS AGENDA Organizers: Michael F. Chiang, MD, Anne L. Coleman, MD, PhD, FARVO and Seth Blackshaw, PhD Big Data is one of the most frequently used terms in the press. This course will inform participants about what Big Data actually is, what are some of the Big Data sets available in vision research, some of the analytics used on Big Data and some of the potential applications of Big Data. Speakers and topics are subject to change. 8:30-9:00am Introduction and Welcome Anne L. Coleman, MD, PhD, FARVO, Jules Stein Eye Institute, UCLA A general overview of Big Data, its definition, uses, benefits and liabilities will be presented. 9:00-9:20am Tapping into Health Care Claims Databases to Learn About Ocular Diseases Joshua D. Stein, MS, MD, Kellogg Eye Center, University of Michigan This talk will describe some of the common large health care claims datasets that researchers can access to study patients with ocular diseases. We will go over some of the strengths of performing research using these databases over other sources as well as limitations of working with these resources. Examples of analyses performed using health care claims data will be described so the audience can get a flavor for the types of studies and research questions that can be answered using these data sources. 9:20-9:40am Mining electronic health record (EHR) data Michael F. Chiang, MD , Casey Eye Institute, Oregon Health & Science University This presentation will discuss practical applications of mining EHR data. These will include clinical research applications of large-scale EHR data, as well as operational applications of EHR data for improving real-world clinical workflow. 9:40-10:00am Clinical Registries and "Big Data" Paul P. Lee, JD, MD, FARVO, Kellogg Eye Center, University of Michigan Clinical registries for eye care have existed since the post US Civil War period's trauma registry. In the last 40 years, several eye registries have been developed, covering not only trauma and ocular medications but also focused clinical issues such as cataract and cataract surgery. The Swedish National Cataract Registry is one excellent example. The newest registries, such as the American Academy of Ophthalmology's IRIS and the American Optometric Association's MORE are even more comprehensive, expanding registry coverage to the broad range of eye care and eye diseases that affect human health. The advent and growth of these comprehensive registries will enable us to build upon the quality and safety work that has been done for cataracts and ocular medications to the broad range of eye conditions as well as to begin to explore detailed relationships among many factors that have heretofore been difficult to understand due to practical limitations such as sample size and the necessity of chart abstraction. As such, clinical registries are both a powerful tool and exemplar of the promise of "big data" to improve vision and lives. Page 1 of 4 10:00-10:30am Panel discussion Michael F. Chiang, MD, Paul P. Lee, JD, MD, FARVO and Joshua D. Stein, MS, MD 10:30-10:45am – Break 10:45-11:05am Visualizing “Big data” Aaron Y. Lee, MD, University of Washington One of the great challenges in Big Data is being able to visualize large volumes of data in order to understand new insights and trends. This presentation will feature several visualizations aiming to help researchers slice through different dimensions of data. Many of these visualizations are interactive allowing for deeper hypothesis generation by being able to visualize data. 11:05-11:25am Applications of machine learning and deep learning Adnan Tufail, FRCOphth, MD, Moorfields Eye Hospital The current explosion of activity in machine learning is remarkable. A non-technical overview of machine learning aimed at clinicians will be given and the potential to capturing the full value of big data discussed. We will discuss the importance of validation of machine learning algorithms. Current and emerging applications of machine learning in ophthalmology, such as the grading of diabetic retinopathy and diagnosis of AMD on retinal images, will be given. 11:25-11:45am Big data and ophthalmic genetics Michael B. Gorin, MD, PhD, FARVO, Jules Stein Eye Institute, UCLA There is considerable interest in multiple areas of medicine to use big data from electronic health records and genetic data. There are a number of opportunities and challenges that confront the use of this approach to address key issues in ophthalmic genetics and foster the application of molecular genetics in ophthalmic care. A number of projects have successfully used selective data from large data sets to test for associations with genetic variants/markers. We will review recent efforts in medicine and ophthalmology to employ institutional and consortia biobanks and EMR data in conjunction with genome wide, next generation sequencing and/or genotyping to elucidate genetic components of disease. This talk will highlight the challenges of defining phenotypes for disease using ICD-9, ICD-10 codes and natural language processing, harmonizing ocular-related measures that are not standardized and determined by multiple methods and devices, dealing with the laterality of diagnoses and treatments, missing and incomplete data especially with records that rely on varying inclusion of information from different medical specialties, and the lack of adequate family historical data. Realization of the potential of big data for ophthalmic genetics will ultimately require a concerted effort to address the consistent acquisition of data for clinical records. 11:45am-12:15pm Panel discussion Michael B. Gorin, MD, PhD, FARVO, Aaron Y. Lee, MD, Adnan Tufail, FRCOphth, MD 12:15-1:15pm – Lunch 1:15-1:35pm Epigenetic changes associated with age-related macular degeneration Jiang Qian, PhD, Wilmer Eye Institute, Johns Hopkins School of Medicine Age-related macular degeneration (AMD) is the leading cause of severe vision loss in people over 60. Genetic association studies have successfully identified genetic variants associated with the disease. However, the genetic variants only explain 40-70% of disease variability, suggesting that factors other than the genetics might contribute to the disease process. Here we attempted to identify the epigenetic factors associated with AMD. We performed epigenetic profiling on retina and RPE Page 2 of 4 layers from postmortem eyes. By comparing the profiles between AMD and normal eyes, we discovered massive and consistent changes in chromatin structures in both retina and RPE. Some AMD susceptibility genes were found to have significant chromatin changes. The epigenetic changes were related to the changes in transcription factor binding in the genome and the alterations in gene expression levels. Our study is the first systematic assessment of epigenetic changes associated with AMD and suggests that epigenetics might play a critical role in AMD. 1:35-1:55pm Transcriptomics Salil Anil Lachke, PhD, University of Delaware The identification of genes linked to eye development and its associated defects presents a formidable challenge. In the recent past however, genome-level transcript profiling (transcriptomics) has become increasingly feasible due to technologies such as microarrays and RNA sequencing. Their application to interrogate specific eye tissues and cell types holds high promise to impact ocular gene discovery. However, global gene expression profiling has brought with it new challenges, such as parsing through the large amounts of data to prioritize select candidates for further analysis. Toward this goal, the development of strategies such as “in silico subtraction” has led to the construction of a systems-based web-resource tool called iSyTE integrated Systems Tool for Eye gene discovery). In this presentation, I will discuss the impact of iSyTE and eye-tissue transcriptomics on identification of genes linked to lens biology and cataract, and outline the ongoing efforts for extending this approach to other ocular tissues such as the retina and the cornea. 1:55-2:15pm A standardized toolbox of highly specific, immunoprecipitation-grade antibodies against human transcription factors and associated proteins Heng Zhu, PhD, Johns Hopkins School of Medicine To better understand what makes a cell function normally and what may go awry in disease, we need better tools and resources, such as renewable protein capture reagents. Monoclonal antibodies (mAbs) are indefinitely renewable and possess consistent epitope recognition properties, making them more desirable for most basic and clinical applications. To extend these efforts, the NIH Common Fund in 2010 initiated the Protein Capture Reagent Program (PCRP), an effort that was aimed at generating highly-specific, extensively characterized and readily available renewable affinity reagents that target a broad range of human transcription factors and associated proteins. Our JHU-CDI team has developed an integrated production and validation pipeline for high-throughput and low-cost generation of mouse monoclonal antibodies (mAbs) that recognize their targets with both high specificity and affinity. By using human proteome microarray validation as a first screening step, we show that we can rapidly enrich for ultraspecific, high-affinity mAbs that perform well in downstream applications -- particularly immunoprecipitation and ChIP-Seq. We demonstrated the flexibility of protein microarray-based validation of antibody specificity, and show that it can be used to identify mAbs that selectively recognize their intended target in native and/or denatured conformation. We also demonstrate that this approach can be used to rapidly and efficiently identify proteins that cross-react with commercially available mAbs, and can thus be used as a standardized platform for analysis of antibody specificity. We have used this pipeline to generate a total of 1,361 mAbs that target a total of 731 unique human transcription factors and associated proteins, and which worked for one or more common research applications, including immunoprecipitation, immunoblotting and ChIP-Seq. We have made these mAbs readily available to the research community through both the Developmental Studies Hybridoma Bank (DSHB) and various commercial suppliers. These reagents serve a standardized toolbox for biochemical analysis of transcriptional regulatory mechanisms in human cells 2:15-2:30pm – Break Page 3 of 4 2:30-2:50pm Parsing retinal development and disease at the single cell level David Cobrinik, MD, PhD, Keck School of Medicine, USC Studies of retinal developmental and disease often involve large scale genomic, transcriptomic, and proteomic analyses. However, interpretations of such studies can be compromised by the admixture of cells of different types and in different developmental or pathogenic stages. For example, RNA-Seq analysis of whole retina gives a vastly different transcriptional profile from that of a single purified cell type, and RNA-Seq of a specific cell type in a developing retina may differ from that of the different cell sub-populations in a different developmental stages. Similarly, a pathogenic process may be obscured if affected cells are mixed together with the bulk unaffected population. Because of these considerations, understanding of normal and pathogenic developmental processes within heterogeneous tissues can be improved through analyses of single cells, followed by grouping of cells with similar properties into individual states, and pseudotemporally ordering the different cellular states into a developmental progression. While single cell ‘omics’ analyses provide unprecedented resolution they also generate far larger data sets than standard omics analyses, as well as challenges for ‘big data’ interpretation and integration. In this presentation I will share our experience in modeling and comparing retinal development in human, mouse, and embryonic stem cell (hESC) -derived retina, as well as the developmental aberrations that occur in response to a disease perturbation. The studies will focus on RNA-Seq of developing cone photoreceptor precursors, which appear to have substantially different features in human, mouse and hESC retinal tissue. They will also describe changes in the cone precursor developmental trajectory elicited by depletion of the RB1 gene, which is thought to reflect early steps of retinoblastoma tumorigenesis. These studies will illustrate how the application of big data approaches to single cell biology can provide novel insights into retinal development and disease. 2:50-3:10pm Single-cell RNA-Seq: a window into retinal cell specification and diversification Seth Blackshaw, PhD, Johns Hopkins School of Medicine Cell type specification in the mammalian retina occurs through a process of temporal patterning, in which individual progenitor cells successively acquire and lose the competence to generate specific postmitotic cell types. The genes that underlie these changes in competence, and extent to which retinal progenitors are heterogeneous in the developmental potential, are still unclear. To address these questions, we have performed large-scale profiling of gene expression in individual cells of the developing retina using RNA-Seq. These data identify canididate regulators of developmental competence in non-neurogenic progenitors, early markers of lineage commitment in neurogenic progenitors, and temporally dynamic patterns of gene expression in postmitotic precursor cells. Potential regulatory relationships among these genes will be discussed. 3:10-3:40pm Panel discussion Seth Blackshaw, PhD, David Cobrinik, MD, PhD, Salil Anil Lachke, PhD, Jiang Qian, PhD, Heng Zhu, PhD 3:40-4:15pm Panel discussion - Brainstorming on next steps and global collaborations All faculty 4:15-4:30pm Closing remarks Michael F. Chiang, MD This presentation will summarize discussion from this session. Page 4 of 4