OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Understanding the human condition is a Big Data problem. This statement is nicely illustrated by the articles that follow from seven of the Centers for Data Excellence that have been funded by the National Institutes of Health (NIH) Big Data to Knowledge (BD2K) initiative. BD2K is a trans-NIH program, funded by all Institutes and Centers at NIH as well as the NIH Common Fund; it is overseen by the NIH Office of Data Science within the NIH Office of the Director. The beginnings of BD2K have been described previously, 1 and the purpose here is to provide an overall context, from the perspective of NIH, for the emerging program, as exemplified by the work of the Data Centers of Excellence (7 of 12 described here), the Data Discovery Index Coordinating Consortium, the various training awards, and the various individual investigator awards that have been made. What is aptly described by the seven Center articles is the research being performed across a rich array of data types and emergent infrastructure—metadata, analysis tools, frameworks, web resources, and more—that focus on problems inherent in extracting knowledge from large amounts of data with varying degrees of structure. Also detailed are plans to train researchers to make the most of the new opportunities presented by these data science advances. Recognizing that what motivates researchers is the desire to understand the human condition, the BD2K initiative was designed so that driving biological problems are central to the effort, but with the solutions to those problems being deliverables in the form of new methods, tools, software, and training. The deliverables that emerge from the Centers should be FAIR 2 —that is, contributing to the ability to F ind, A ccess, I nteroperate, and R euse the products of this research. BD2K aims to have these digital objects exist, not in isolation, but rather as part of an emergent ecosystem that is shared with the biomedical research community at large. To this end, we have introduced the notion of the Commons , a shared virtual space that conforms to the FAIR principles. The Commons allows digital objects to be stored and computed upon by a broad community once found by the emergent data discovery index being developed by the BioCADDIE group. 3 The Commons pilots that are under way are based on public cloud resources, but other compute and storage resources (such as high-performance computing facilities and institutional facilities) are expected to join the Commons as it develops. This assumes that the initial Commons pilots provide a cost-effective, sustainable, and usable environment. The primary aim of pilots now under way is to test this notion. If the pilots are successful, the BD2K Centers described herein and individual investigator grants will seed the Commons with data and software, whereupon we can monitor usage, which is an important for determining the value of this research output. We are at the beginnings of an exciting initiative, and the plans of the BD2K Centers are an excellent first step in advancing biomedicine in the era of Big Data.

Biomedical research has and will continue to generate large amounts of data (termed 'big data') in many formats and at all levels. Consequently, there is an increasing need to better understand and mine the data to further knowledge and foster new discovery. The National Institutes of Health (NIH) has initiated a Big Data to Knowledge (BD2K) initiative to maximize the use of biomedical big data. BD2K seeks to better define how to extract value from the data, both for the individual investigator and the overall research community, create the analytic tools needed to enhance utility of the data, provide the next generation of trained personnel, and develop data science concepts and tools that can be made available to all stakeholders.

UNLABELLED: VarSifter is a graphical software tool for desktop computers that allows investigators of varying computational skills to easily and quickly sort, filter, and sift through sequence variation data. A variety of filters and a custom query framework allow filtering based on any combination of sample and annotation information. By simplifying visualization and analyses of exome-scale sequence variation data, this program will help bring the power and promise of massively-parallel DNA sequencing to a broader group of researchers. AVAILABILITY AND IMPLEMENTATION: VarSifter is written in Java, and is freely available in source and binary versions, along with a User Guide, at http://research.nhgri.nih.gov/software/VarSifter/.

MOTIVATION: The construction of physical maps based on bacterial clones [e.g. bacterial artificial chromosomes (BACs)] is valuable for a number of molecular genetics applications, including the high-resolution mapping of genomic regions of interest and the identification of clones suitable for systematic sequencing. A common approach for large-scale screening of bacterial clone libraries involves the hybridization of high-density arrays of immobilized, lysed colonies with collections of DNA probes. The use of a multiplex hybridization screening strategy, whereby pooled probes are analysed en masse, simplifies the effort by reducing the total number of parallel experiments required. However, this approach generates large amounts of hybridization-based data that must be carefully analysed, assimilated, and disambiguated in a careful but efficient manner. RESULTS: To facilitate the screening of high-density clone arrays by a multiplex hybridization approach, we have written a program called ComboScreen. This program provides an organizational framework and analytical tools required for the high-throughput hybridization screening of clone arrays with pools of probes. We have used this program extensively for constructing mouse sequence-ready BAC contig maps.