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SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery [version 2; peer review: 2 approved]

Authors :
Bahar Tercan
Guangrong Qin
Taek-Kyun Kim
Boris Aguilar
John Phan
William Longabaugh
David Pot
Christopher J. Kemp
Nyasha Chambwe
Ilya Shmulevich
Author Affiliations :
<relatesTo>1</relatesTo>Institute for Systems Biology, Seattle, WA, 98109, USA<br /><relatesTo>2</relatesTo>General Dynamics Information Technology, Rockville, MD, 20852, USA<br /><relatesTo>3</relatesTo>Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA<br /><relatesTo>4</relatesTo>Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
Source :
F1000Research. 11:493
Publication Year :
2022
Publisher :
London, UK: F1000 Research Limited, 2022.

Abstract

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.

Details

ISSN :
20461402
Volume :
11
Database :
F1000Research
Journal :
F1000Research
Notes :
Revised Amendments from Version 1 In this revision, we refine our discussion to address reviewer's comments. Specifically, we articulate the rationale and selection behind the representative workflows selected for re-implementation in SL-Cloud, address the question of false positives/false negatives and further expand the discussion to address questions about how SL-Cloud can be applied in other organisms., , [version 2; peer review: 2 approved]
Publication Type :
Academic Journal
Accession number :
edsfor.10.12688.f1000research.110903.2
Document Type :
software-tool
Full Text :
https://doi.org/10.12688/f1000research.110903.2