SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery

Autor: Bahar Tercan, Guangrong Qin, Taek-Kyun Kim, Boris Aguilar, John Phan, William Longabaugh, David Pot, Christopher J. Kemp, Nyasha Chambwe, Ilya Shmulevich
Rok vydání: 2022
Předmět:
Zdroj: F1000Research. 11:493
ISSN: 2046-1402
DOI: 10.12688/f1000research.110903.1
Popis: Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.
Databáze: OpenAIRE