SL-Cloud: A Cloud-based Resource to Support Synthetic Lethal Interaction Discovery

Overview

Journal F1000Res

Specialties Biomedical Engineering
Science

Date 2023 Feb 13

PMID 36761837

Authors

Bahar Tercan

Guangrong Qin

Taek-Kyun Kim

Boris Aguilar

John Phan

William Longabaugh

David Pot

Christopher J Kemp

Nyasha Chambwe

Ilya Shmulevich

Affiliations

Soon will be listed here.

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.

Citing Articles

Predicting host-based, synthetic lethal antiviral targets from omics data.

Staheli J, Neal M, Navare A, Mast F, Aitchison J NAR Mol Med. 2024; 1(1):ugad001.

PMID: 38994440 PMC: 11233254. DOI: 10.1093/narmme/ugad001.

Predicting host-based, synthetic lethal antiviral targets from omics data.

Staheli J, Neal M, Navare A, Mast F, Aitchison J bioRxiv. 2023; .

PMID: 37645861 PMC: 10462099. DOI: 10.1101/2023.08.15.553430.

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