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SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery
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 p...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880341/ https://www.ncbi.nlm.nih.gov/pubmed/36761837 http://dx.doi.org/10.12688/f1000research.110903.2 |
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author | Tercan, Bahar Qin, Guangrong Kim, Taek-Kyun Aguilar, Boris Phan, John Longabaugh, William Pot, David Kemp, Christopher J. Chambwe, Nyasha Shmulevich, Ilya |
author_facet | Tercan, Bahar Qin, Guangrong Kim, Taek-Kyun Aguilar, Boris Phan, John Longabaugh, William Pot, David Kemp, Christopher J. Chambwe, Nyasha Shmulevich, Ilya |
author_sort | Tercan, Bahar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9880341 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-98803412023-02-08 SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery Tercan, Bahar Qin, Guangrong Kim, Taek-Kyun Aguilar, Boris Phan, John Longabaugh, William Pot, David Kemp, Christopher J. Chambwe, Nyasha Shmulevich, Ilya F1000Res Software Tool Article 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. F1000 Research Limited 2022-12-22 /pmc/articles/PMC9880341/ /pubmed/36761837 http://dx.doi.org/10.12688/f1000research.110903.2 Text en Copyright: © 2022 Tercan B et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Tercan, Bahar Qin, Guangrong Kim, Taek-Kyun Aguilar, Boris Phan, John Longabaugh, William Pot, David Kemp, Christopher J. Chambwe, Nyasha Shmulevich, Ilya SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery |
title | SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery |
title_full | SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery |
title_fullStr | SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery |
title_full_unstemmed | SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery |
title_short | SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery |
title_sort | sl-cloud: a cloud-based resource to support synthetic lethal interaction discovery |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9880341/ https://www.ncbi.nlm.nih.gov/pubmed/36761837 http://dx.doi.org/10.12688/f1000research.110903.2 |
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