The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

Nucleosome Interactions and Stability in an Ordered Nucleosome Array Model System*

Although it is well established that the majority of eukaryotic DNA is sequestered as nucleosomes, the higher-order structure resulting from nucleosome interactions as well as the dynamics of nucleosome stability are not as well understood. To characterize the structural and functional contribution of individual nucleosomal sites, we have developed a chromatin model system containing up to four nucleosomes, where the array composition, saturation, and length can be varied via the ordered ligation of distinct mononucleosomes. Using this system we find that the ligated tetranucleosomal arrays undergo intra-array compaction. However, this compaction is less extensive than for longer arrays and is histone H4 tail-independent, suggesting that well ordered stretches of four or fewer nucleosomes do not fully compact to the 30-nm fiber. Like longer arrays, the tetranucleosomal arrays exhibit cooperative self-association to form species composed of many copies of the array. This propensity for self-association decreases when the fraction of nucleosomes lacking H4 tails is systematically increased. However, even tetranucleosomal arrays with only two octamers possessing H4 tails recapitulate most of the inter-array self-association. Varying array length shows that systems as short as dinucleosomes demonstrate significant self-association, confirming that relatively few determinants are required for inter-array interactions and suggesting that in vivo multiple interactions of short runs of nucleosomes might contribute to complex fiber-fiber interactions. Additionally, we find that the stability of nucleosomes toward octamer loss increases with array length and saturation, suggesting that in vivo stretches of ordered, saturated nucleosomes could serve to protect these regions from histone ejection

The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment

ObjectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.ResultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.ConclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19