The Cancer Survivorship Community shares high quality genomic, clinical, and patient-reported data from survivors of pediatric cancer. To accelerate the rate of discovery in survivorship research we have developed this SJLIFE Survivorship Portal, a data-sharing platform for genomic and clinical data from the St. Jude Lifetime Cohort hosted on the St. Jude Cloud. The Portal features the Clinical Data Browser, GenomePaint, a genetic variant browser, for browsing, visualizing and analyzing clinical and genetic data integratively. Additionally, the Survivorship Portal will serve as a site for an expanding portfolio of risk-prediction tools developed using the SJLIFE cohort, including the recently developed Cumulative Burden Risk-Prediction Tool. Reference: Epigenetic Age Acceleration and Chronic Health Conditions among Adult Survivors of Childhood Cancer. JNCI 2021
The Neuro-Oncology Community is a site designed to share clinical and molecular data from pediatric brain and spinal cord tumors in a visually interactive fashion that makes it more accessible and understandable to providers and researchers. Our hope is that this ever-expanding site will serve to advance the understanding and improve the treatment of these debilitating and, often, deadly diseases.
The Audacious Goals Initiative (AGI) for Regenerative Medicine is an effort by the National Eye Institute (NEI) to push the boundaries of vision science and restore vision through regeneration of the retina. By facilitating cross-disciplinary research, we are tackling the most devastating and difficult-to-treat eye diseases.
The Sickle Cell Disease Community, an initiative led by St. Jude Children’s Research Hospital, shares whole genome sequencing data and clinical data with research and clinical communities. Our goal is to promote global collaborative efforts to identify genetic modifiers of sickle cell disease complications, improve clinical management for patients, and develop more effective treatments.
NetAD: Network modeling of bulk and single-cell multi-omics data to identify drivers for Alzheimer's Disease. To increase our understanding of Alzheimer's disease (AD) by using network-based systems biology tools to harmonize cross-studied multi-omics data at the bulk and single-cell levels, thereby providing new therapeutic targets and biomarkers towards AD prevention and treatment.