 Hello, we are going to talk about our special article in human mutation. Free the data, one laboratory's approach to knowledge-based genomic variant classification and preparation for EMR integration of genomic data. Current technology allows rapid identification of disease genes and subsequent translation of research discoveries into clinical genetic tests. Many sequence variants identified in individuals with clinical features of genetic disorders are detected by clinical laboratories and are often not published or made available. Many clinical laboratories are willing to share this data and eventually will through efforts such as NCBI's ClinVar project and the Human Variable Project. Anticipating the need for large-scale transfer of sequence data, we at Emory Genetics Laboratory developed a data management system called M-Base to record, store, and manage data on Sanger-confirmed sequence variants in a highly structured data format. This structured format allows the base pair change, predicted amino acid change, positioned relative to a genomic or mRNA reference, as well as our laboratory director review classification of the variant as pathogenic, likely pathogenic, unknown clinical significance, likely benign, or benign to be recorded for a variant and for the variant data to be associated with specific patients. To demonstrate the ease with which M-Base can be used to disseminate de-identified information on observed sequence variants, we developed M-Base class, a web-based tool that allows any user access to variants seen at EGL and the variant's current classification. From this website, a review of the variant's classification can be requested or a discussion about the variant can be initiated. No registration is needed. We will now demonstrate the use of M-Base and M-Base class. M-Base sits between Emory Genetics Laboratory's EMR, Laboratory Information Management System, and Reporting Systems, as depicted in Figure 2 of the paper. For every gene, we maintain critical transcript and mapping information and the variant seen here at EGL for that gene. For every variant, we maintain an extensive annotation system that includes HDMD, DBSNP, Exome Variant Server, and predicted effects from Polyfan 2 and SIFT version 4.04 and 5.02. M-V class is the public interface to EGL's variant's data. As depicted in Figure 3, M-V class allows the user to monitor and review Emory Genetic Laboratory's current classification of a variant, count a review for reclassification, or to request an updated report. M-V class may be accessed off of Emory Genetic Laboratory's primary website at genetics.emory.edu.gl. M-V class may be searched by entering an HGNC-recognized gene symbol, such as ACA-DM. From here, you are able to review a summary of your returned results, 66 records of which 20 are pathogenic, 37 are variants of unknown significance, and 9 are recorded as benign. You are also able to further refine your search by changing nomenclature, such as K304, something like Exon, Exon 7, or even classification, pathogenic. You may open the dialogue with Emory Genetic Laboratory by clicking on this button here where you will be requested to enter your name, email, phone, and question or comment where we will get back to you. You may also expedite the review of variants of unknown significance by clicking the appropriate checkbox. M-V class also allows you to access our data by direct URL construction, as this example is shown here, or by download for integration into your applications. So in conclusion, with the exception of a few gene or disease-specific databases, there is currently no systematic way for clinical laboratories to share knowledge about sequence variants. The time is rapidly approaching when clinical laboratories will share informational sequence variants to web-based databases such as ClinVar and the Human Varian Project. In the meantime, we have designed a system that both prepares our laboratory's data for future data transfers and allows a real-time view of our observed variants and their classifications. We believe that a good management of sequence data as well as the willingness to share this data are critical to providing the best genetic healthcare possible.