Pathology Trainee Redeployment and Education During the COVID-19 Pandemic: An Institutional Experience.

Pathology training programs throughout the United States have endured unprecedented challenges dealing with the ongoing coronavirus disease 2019 pandemic. At Houston Methodist Hospital, the Department of Pathology and Genomic Medicine planned and executed a trainee-oriented, stepwise emergency response. The focus was on optimizing workflows among areas of both clinical and anatomic pathology, maintaining an excellent educational experience, and minimizing trainee exposure to coronavirus disease 2019. During the first phase of the response, trainees were divided into 2 groups: one working on-site and the other working remotely. With the progression of the pandemic, all trainees were called back on-site and further redeployed within our department to meet the significantly increased workload demands of our clinical laboratory services. Adjustments to trainee educational activities included, among others, the organization of a daily coronavirus disease 2019 virtual seminar series. This series served to facilitate communication between faculty, laboratory managers, and trainees. Moreover, it became a forum for trainees to provide updates on individual service workflows and volumes, ongoing projects and research, as well as literature reviews on coronavirus disease 2019-related topics. From our program's experience, redeploying pathology trainees within our department during the coronavirus disease 2019 pandemic resulted in optimization of patient care while ensuring trainee safety, and importantly, helped to maintain continuous high-quality education through active involvement in unique learning opportunities.

GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets.

MOTIVATION: The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets. RESULTS: We developed the GenomeRunner web server to automate systematic statistical analysis of SNP sets within a regulatory context. Besides defining the functional impact of SNP sets, GenomeRunner implements novel regulatory similarity/differential analyses, and cell type-specific regulatory enrichment analysis. Validated against literature- and disease ontology-based approaches, analysis of 39 disease/trait-associated SNP sets demonstrated that the functional impact of SNP sets corresponds to known disease relationships. We identified a group of autoimmune diseases with SNPs distinctly enriched in the enhancers of T helper cell subpopulations, and demonstrated relevant cell type-specificity of the functional impact of other SNP sets. In summary, we show how systematic analysis of genomic data within a regulatory context can help interpreting the functional impact of SNP sets. AVAILABILITY AND IMPLEMENTATION: GenomeRunner web server is freely available at http://www.integrativegenomics.org/ CONTACT: mikhail.dozmorov@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GenomeRunner: automating genome exploration.

MOTIVATION: One of the challenges in interpreting high-throughput genomic studies such as a genome-wide associations, microarray or ChIP-seq is their open-ended nature-once a set of experimentally identified regions is identified as statistically significant, at least two questions arise: (i) besides P-value, do any of these significant regions stand out in terms of biological implications? (ii) Does the set of significant regions, as a whole, have anything in common genome wide? These issues are difficult to address because of the growing number of annotated genomic features (e.g. single nucleotide polymorphisms, transcription factor binding sites, methylation peaks, etc.), and it is difficult to know a priori which features would be most fruitful to analyze. Our goal is to provide partial automation of this process to begin examining associations between experimental features and annotated genomic regions in a hypothesis-free, data-driven manner. RESULTS: We created GenomeRunner-a tool for automating annotation and enrichment of genomic features of interest (FOI) with annotated genomic features (GFs), in different organisms. Besides simple association of FOIs with known GFs GenomeRunner tests whether the enriched FOIs, as a group, are statistically associated with a large and growing set of genomic features. AVAILABILITY: GenomeRunner setup files and source code are freely available at http://sourceforge.net/projects/genomerunner. CONTACT: mikhail-dozmorov@omrf.org; Jonathan-Wren@omrf.org; jdwren@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.