The Office of the National Coordinator for Health Information Technology (ONC) Sync for Genes project has and will continue to advance the standardized sharing of genomics information between laboratories, healthcare providers, patients, and researchers. The first two phases of Sync for Genes focused on bringing genomics to the point-of-care, while making the genomic data available for research simultaneously. Sync for Genes Phase 3 aimed to identify the challenges experienced by testing labs when they adopt health IT standards in order to share genomic results. Phase 4 will focus on improving the electronic exchange of genomic data between organizations with at least one data receiver, such as a patient or caregiver. Learn more about these efforts at https://www.healthit.gov/buzz-blog/interoperability/labs-on-fhir-sharing-genetic-test-results

Participants will create apps to explore the relationship between COVID-19 patients’ T- and B-cell receptor repertoire data with disease phenotypes.

During FDA Scientific Computing Days Dr. Emily Boja, Senior Scientist within the FDA Office of Health Informatics and precisionFDA leader, gave a talk on the power of multi-omoics in which she discussed the NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge and the upcoming COVID-19 Precision Immunology App-a-thon.

A precisionFDA poster, featuring the Brain Cancer Predictive Modeling and Biomarker Discovery Challenge and BCO App-a-thon, was presented at FDA Scientific Computing Days

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The FDA’s Genomic Working Group (GWG) hosted a series of precisionFDA training session. Videos of these session are available on YouTube.

The precisionFDA challenge framework has been updated, now allowing participants to submit to challenges without making their files public before the challenge close.

The Global Alliance for Genomics and Health (GA4GH) benchmarking tool, which enables users to compare variant calls against truth sets and assess variant calling performance, is available on precisionFDA as a comparator app.

Join the COVID-19 EHR DREAM Challenge to help identify risk factors that lead to a positive test. Clinical data sets mapped to the OMOP Common Data Model are provided for analytics development.

FDA announced support for use in regulatory submissions the current version of the IEEE bioinformatics computations and analyses standard for bioinformatic workflows (BioCompute) and an update to include this standard in the FDA Data Standards Catalog.

Modern mass spectrometry-based proteomics are capable of profiling tens of thousands of peptides in a single experiment. Each of these peptides is fragmented along the peptide bonds to form a mass spectrum, often interpreted against a theoretical in silico-generated mass spectrum as part of the analysis workflow. Since peptide mass spectra represent physical properties of these molecules, standard interpretation of these mass spectra has the potential to improve the success rate of all discovery experiments in proteomics. To this regard, NIST has developed mass spectral libraries of biologically relevant peptides to provide peptide reference data for proteomic laboratories, enabling the discovery and validation of disease-related "biomarkers." Using mass spectral libraries to identify these compounds is more sensitive and robust than interpretation by theoretical methods. These databases are freely available for testing and development of new applications (http://peptide.nist.gov), in a form easily searchable using software tools such as Mascot, and updated and maintained as a reference data resource. To date, several of the libraries developed, including human, yeast and E. coli, represent significant coverage of these proteomes and are suitable for routine uses. An additional NIST resource for proteomics is the monoclonal antibody reference material, RM8671, a recombinant humanized IgG1κ expressed in murine suspension culture. The reference material is intended for use in evaluating the performance of methods for determining physicochemical and biophysical attributes of monoclonal antibodies (https://www.nist.gov/programs-projects/nist-monoclonal-antibody-reference-material-8671). The RM8671 is accompanied by a high-resolution spectral library for enhanced glycosylation characterization using 2D LC-MS/MS approach (https://chemdata.nist.gov/dokuwiki/doku.php?id=%20peptidew:lib:newigghsalib).

Help inform the design of a prize competition to inspire tools entrepreneurs can use to navigate the complex innovation ecosystem by 11:59 pm Monday June 15 - https://federallabs.org/t2-toolkit/lab-to-market-l2m-innovation-ecosystem. The COVID-19 pandemic has presented many challenges for entrepreneurs. Finding the resources to help turn a good idea into solutions for global impact shouldn't be one of them. We are looking for input to make sure the competition results in useful tools that support an inclusive innovation environment so that all businesses can find the resources they need, which in turn will help the US economy grow and respond to challenges like how to recover from the pandemic. The Federal government has abundant resources (https://federallabs.org/node/218178) available to support innovators – from funding to IP to entrepreneurial training to equipment – but like any complex environment, the innovation ecosystem is difficult to navigate without a roadmap and guides. Gaining access to these resources will boost innovation, create jobs, and build the nation’s economy. Be part of the innovative roadmap to success by submitting your ideas on tapping and connecting the vast resources of the Federal government with your local communities’ efforts to assist entrepreneurs nationwide. Learn more about the competition and how to submit your suggestions at https://federallabs.org/t2-toolkit/lab-to-market-l2m-innovation-ecosystem

NIST has released four new tools for searching the COVID-19 Open Research Dataset (CORD-19), a collection of scientific literature about coronaviruses. The four tools are: (1) the NIST Scientific Indexing Resource for finding related articles from identified keywords and phrases, (2) the COVID-19 Data Repository for searching the dataset by author, institution, and keyword, (3) The COVID-19 Registry of other COVID-19-related resources, and (4) the cord19-cdcs-nist curated archive of the dataset.

A number of bioinformatics tools have been added as precisionFDA apps! The list includes: AMRFinder, FastQC, Cutadapt, MultiQC, Qualimap, STAR, RSEM, ResFinder, StringTie, SKESA, DIAMOND, and snpEff. All of these apps are publicly available, and can be found by going to precision.fda.gov/apps/explore, or by clicking the link above. They may be forked and modified. If you have an app request that you'd like to see developed on precisionFDA, don't hesitate to contact us!

The Pandemic Data Room is a comprehensive global COVID-19 data repository, with unique data sets, created by a consortium of partners and led by QED Group, to improve understanding of the impact of physical distancing policies on social behavior, disease rates, hospital utilization, and local/national economies. Data available from the Pandemic Data Room will be used to generate insights that lead to a better understanding of the impact of physical distancing and the outbreak. Play a role in the response by creating a COVID-19-focused data visualization or analysis tool and sharing with the global health and data visualization communities. Everyone can participate in this Challenge, with separate tracks for students and professionals. Submissions from each track are judged separately and prizes awarded for each track.

A consortium, led by the White House Office of Science and Technology Policy, has released the COVID-19 Open Research Dataset (CORD-19), a machine-readable dataset of over 29,000 scholarly articles for COVID-19 and the coronavirus family. This consortium now calls on data scientists to develop tools to help scientists extract and synthesize knowledge from this dataset.

Baylor College of Medicine is hosting a hackathon on April 19-21 to join the fields of structural variation calling and graph genomes. Registration is open until March 15th.

Ensuring that federally funded health research keeps pace with the explosion of health data depends on better information technology (IT), access to high-quality electronic health data, and supportive policies. Because it prominently funds and conducts health research, the U.S. federal government needs health IT to rapidly evolve and has the ability to drive that evolution. The Office of the National Coordinator for Health Information Technology developed the National Health IT Priorities for Research: A Policy and Development Agenda (the Agenda) that identifies health IT priorities for research in consultation with relevant federal agencies. This article describes support for the Agenda from the Food and Drug Administration, the National Institutes of Health, and the Veterans Health Administration. Advancing the Agenda will benefit these agencies and support their missions as well as the entire ecosystem leveraging the health IT infrastructure or using data from health IT systems for research.

On February 20, 2020, the U.S. Food and Drug Administration (FDA) introduced a new web-based resource that describes some of the gene-drug interactions for which FDA believes there is sufficient scientific evidence to support the described associations between certain genetic variants, or genetic variant-inferred phenotypes, and altered drug metabolism, and in certain cases, differential therapeutic effects, including differences in risks of adverse events. This resource represents a collaborative effort between the FDA’s Center for Devices and Radiological Health (CDRH) and Center for Drug Evaluation and Research (CDER) and is intended to provide the FDA’s current view of certain established gene-drug interactions that appear in FDA-approved drug labeling and additional gene-drug interactions that are consistent with the current FDA labeling and are supported by sufficient scientific evidence based on published literature. The FDA will continue to review the science and strength of the scientific evidence and update this resource periodically. The FDA has opened a docket FDA-2020-N-0839 for stakeholders to provide feedback.

If you are interested in learning more about how FDA is modernizing its data strategy, including data quality, data stewardship, data exchange, and data analytics, attend the “Modernizing FDA’s Data Strategy” public meeting on March 27th!

Join the challenge to advance techniques for surveillance and detection of adverse events associated with FDA products. Submissions are being accepted until February 28th.

FDA’s technology modernization will be informed by direct engagement with stakeholders. As an example of stakeholder engagement, FDA has used PrecisionFDA to provide a community platform for the evaluation of next-generation sequence data and regulatory exploration.

The precisionFDA CDRH Biothreat Challenge addressed the need for a comprehensive citizen-science benchmarking study of infectious disease NGS diagnostic classification algorithms.

Stay up to date on the latest precisionFDA news and challenge information

PrecisionFDA and Georgetown-ICBI are launching the Brain Cancer Predictive Modeling and Biomarker Discovery Challenge! The goal of this challenge is to advance techniques for the prognosis and treatment of brain tumors by asking participants to develop machine learning and/or artificial intelligence models to identify biomarkers and predict patient outcomes using gene expression, DNA copy number, and clinical data.

Advancing the Regulatory Science of Omics via Crowdsourcing on the precisionFDA Platform

Breakout Session: Engaging the Global Community to Support and Inform Regulatory Science

Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.