Your browser doesn't support javascript.
loading
SL-Miner: a web server for mining evidence and prioritization of cancer-specific synthetic lethality.
Liu, Xin; Hu, Jieni; Zheng, Jie.
Afiliación
  • Liu X; School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
  • Hu J; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
  • Zheng J; School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Bioinformatics ; 40(2)2024 02 01.
Article en En | MEDLINE | ID: mdl-38244572
ABSTRACT

SUMMARY:

Synthetic lethality (SL) refers to a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect cell viability. It significantly expands the range of potential therapeutic targets for anti-cancer treatments. SL interactions are primarily identified through experimental screening and computational prediction. Although various computational methods have been proposed, they tend to ignore providing evidence to support their predictions of SL. Besides, they are rarely user-friendly for biologists who likely have limited programming skills. Moreover, the genetic context specificity of SL interactions is often not taken into consideration. Here, we introduce a web server called SL-Miner, which is designed to mine the evidence of SL relationships between a primary gene and a few candidate SL partner genes in a specific type of cancer, and to prioritize these candidate genes by integrating various types of evidence. For intuitive data visualization, SL-Miner provides a range of charts (e.g. volcano plot and box plot) to help users get insights from the data. AVAILABILITY AND IMPLEMENTATION SL-Miner is available at https//slminer.sist.shanghaitech.edu.cn.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Mutaciones Letales Sintéticas / Neoplasias Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Mutaciones Letales Sintéticas / Neoplasias Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: China