High Performance Computing Publications
Publications by Patricia Kovatch
Dean for Scientific Computing and Data Patricia Kovatch contributes to scientific journals and publications as a collaborative and primary author. Read more publications.
Excerpt: Preserving Data: Who’s Got Your Back(up)?
by Patricia Kovatch | Data management has become an essential part of research. Scientists need to be able to rely on their data infrastructures to recover data in case of disaster or to assist with reproducibility of their results. Ensuring a reliable data infrastructure and backup processes may not be the most exciting part of research, but consequences… Read more
Excerpt: An Inflammatory Cytokine Signature Helps Predict COVID-19 Severity and Death
The COVID-19 pandemic caused by infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to more than 100,000 deaths in the United States. Several studies have revealed that the hyper-inflammatory response induced by… Read more
Publications Through Minerva Computing
Minerva’s computing power helps scientists and researchers advance their studies. Many publications feature work made possible through Minerva.
Abstract: Functional Common and Rare ERBB2 Germline Variants Cooperate in Familial and Sporadic Cancer Susceptibility
by Bao, R. et al. 2021 January | We investigated a Spanish and Catalan family in which multiple cancer types tracked across three generations, but for which no genetic etiology had been identified. Whole-exome sequencing of germline DNA from multiple affected family members was performed to identify candidate variants to explain this occurrence of familial cancer. We discovered in all cancer-affected family members a single rare heterozygous germline variant (I654V, rs1801201) in ERBB2/HER2, which is located in a transmembrane glycine zipper motif critical for ERBB2-mediated signaling and in complete linkage disequilibrium (D‘ = 1) with a common polymorphism (I655V, rs1136201) previously reported in some populations as associated with cancer risk. Because multiple cancer types occurred in this family, we tested both the I654V and the I655V variants for association with cancer across multiple tumor types in 6,371 cases of Northern European ancestry drawn from The Cancer Genome Atlas and 6,647 controls, and found that the rare variant (I654V) was significantly associated with an increased risk for cancer (OR = 1.40; P = 0.021; 95% confidence interval (CI), 1.05-1.89). Functional assays performed in HEK 293T cells revealed that both the I655V single mutant (SM) and the I654V;I655V double mutant (DM) stabilized ERBB2 protein and activated ERBB2 signaling, with the DM activating ERBB2 significantly more than the SM alone. Thus, our results suggest a model whereby heritable genetic variation in the transmembrane domain activating ERBB2 signaling is associated with both sporadic and familial cancer risk, with increased ERBB2 stabilization and activation associated with increased cancer risk. PREVENTION RELEVANCE: By performing whole-exome sequencing on germline DNA from multiple cancer-affected individuals belonging to a family in which multiple cancer types track across three generations, we identified and then characterized functional common and rare variation in ERBB2 associated with both sporadic and familial cancer. Our results suggest that heritable variation activating ERBB2 signaling is associated with risk for multiple cancer types, with increases in signaling correlated with increases in risk, and modified by ancestry or family history. DOI: 10.1158/1940-6207.CAPR-20-0094
Research at Mount Sinai
Examples of incredible research being done on Mount Sinai Scientific Computing machines include:
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Research Data Services
Mount Sinai’s Research Data Services department provides services and resources for data management, specimen management, scheduling for shared resource facilities, and data mining and data marts. We provide both self-service and custom electronic data capture systems for clinical trials.
Mount Sinai Data Warehouse
The Mount Sinai Data Warehouse (MSDW) consists of clinical, operational, and financial data derived from the patient care processes of The Mount Sinai Hospital and The Mount Sinai Faculty Practice Associates. Detailed inpatient and outpatient data are extracted from transactional systems, transformed and loaded into MSDW nightly. MSDW contains data on over two million patients sourced from over 20 transactional systems since 2003.