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Sonam Dolma
Researcher at University of Toronto
Publications - 11
Citations - 2289
Sonam Dolma is an academic researcher from University of Toronto. The author has contributed to research in topics: Stem cell & Cellular differentiation. The author has an hindex of 8, co-authored 8 publications receiving 1325 citations. Previous affiliations of Sonam Dolma include Hospital for Sick Children & Lunenfeld-Tanenbaum Research Institute.
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The BioGRID interaction database: 2019 update
Rose Oughtred,Chris Stark,Bobby-Joe Breitkreutz,Jennifer M. Rust,Lorrie Boucher,Christie S. Chang,Nadine Kolas,Lara O'Donnell,Genie Leung,Rochelle McAdam,Frederick Zhang,Sonam Dolma,Andrew Willems,Jasmin Coulombe-Huntington,Andrew Chatr-aryamontri,Kara Dolinski,Mike Tyers,Mike Tyers +17 more
TL;DR: A new dedicated aspect of BioGRID annotates genome-wide CRISPR/Cas9-based screens that report gene–phenotype and gene–gene relationships, and captures chemical interaction data, including chemical–protein interactions for human drug targets drawn from the DrugBank database and manually curated bioactive compounds reported in the literature.
Journal ArticleDOI
The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions.
Rose Oughtred,Jennifer M. Rust,Christie S. Chang,Bobby-Joe Breitkreutz,Chris Stark,Andrew Willems,Lorrie Boucher,Genie Leung,Nadine Kolas,Frederick Zhang,Sonam Dolma,Jasmin Coulombe-Huntington,Andrew Chatr-aryamontri,Kara Dolinski,Mike Tyers,Mike Tyers +15 more
TL;DR: The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open‐access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human.
Journal ArticleDOI
Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy
Xiaoyang Lan,David J. Jörg,Florence M.G. Cavalli,Laura M. Richards,Laura M. Richards,Long V. Nguyen,Robert Vanner,Paul Guilhamon,Paul Guilhamon,Paul Guilhamon,Lilian Lee,Michelle Kushida,Davide Pellacani,Davide Pellacani,Nicole I. Park,Fiona J. Coutinho,Heather Whetstone,Hayden J. Selvadurai,Clare Che,Betty Luu,Annaick Carles,Michelle Moksa,Naghmeh Rastegar,Renee Head,Sonam Dolma,Panagiotis Prinos,Panagiotis Prinos,Michael D. Cusimano,Michael D. Cusimano,Sunit Das,Sunit Das,Mark Bernstein,Mark Bernstein,Cheryl H. Arrowsmith,Cheryl H. Arrowsmith,Andrew J. Mungall,Richard A. Moore,Yussanne Ma,Marco Gallo,Mathieu Lupien,Mathieu Lupien,Mathieu Lupien,Trevor J. Pugh,Trevor J. Pugh,Michael D. Taylor,Martin Hirst,Martin Hirst,Connie J. Eaves,Connie J. Eaves,Benjamin D. Simons,Benjamin D. Simons,Peter B. Dirks +51 more
TL;DR: The clonal evolution of barcoded glioblastoma cells in an unbiased way following serial xenotransplantation is studied to define their individual fate behaviours, and it is shown that chemotherapy facilitates the expansion of pre-existing drug-resistant gliOBlastoma stem cells.
Journal ArticleDOI
Inhibition of Dopamine Receptor D4 Impedes Autophagic Flux, Proliferation, and Survival of Glioblastoma Stem Cells
Sonam Dolma,Hayden J. Selvadurai,Xiaoyang Lan,Lilian Lee,Michelle Kushida,Veronique Voisin,Heather Whetstone,Milly So,Tzvi Aviv,Nicole I. Park,Xueming Zhu,Changjiang Xu,Renee Head,Katherine J. Rowland,Mark Bernstein,Ian D. Clarke,Gary D. Bader,Lea Harrington,John H. Brumell,Mike Tyers,Peter B. Dirks +20 more
TL;DR: A role for neurochemical pathways in governing GBM stem cell proliferation and suggest therapeutic approaches for GBM is demonstrated and a role for dopamine receptor D4 antagonists is demonstrated.
Journal ArticleDOI
Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.
Jan Wildenhain,Michaela Spitzer,Sonam Dolma,Nick Jarvik,R. L. White,Marcia Roy,Emma Griffiths,David S. Bellows,Gerard D. Wright,Mike Tyers,Mike Tyers +10 more
TL;DR: This work identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens and demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.