Synthetic lethality prediction software tools | Protein interaction data analysis
Two genes are said to be in a synthetic lethality (SL) relationship if a perturbation of either gene alone is not lethal but perturbations of both genes lead to cell death or a dramatic decrease in cell viability (Boone et al., 2007). Source text: Guo et al., 2015.
Proposes a targeted enumeration procedure for identification of synthetic lethal (SL) genes or reactions using genome-scale metabolic models. SL Finder relies on the solution of a bilevel optimization framework that utilizes flux balance analysis to identify all multi-reaction/gene lethals. The user needs to first specify a parameter n, indicating the order of synthetic lethals. This bilevel formulation then identifies the set of n gene/reaction deletions that minimizes the maximum biomass formation potential of the network. If the minimal value of the maximum biomass is found to be below a pre-specified viability threshold (e.g., one percent of maximum biomass) then the corresponding combination of n gene/reaction deletions forms a SL. All alternative SL gene/reaction sets of size n are successively obtained by excluding the previously identified SLs using integer cuts and resolving the bilevel formulation.
Predicts cancer-specific synthetic lethal (SL) interactions of any given susceptibility gene using a machine learning approach. DiscoverSL provides an integrative computational pipeline for prediction and in silico validation of SL interactions derived from patient-specific mutations in cancer. The software also includes additional plot modules for intuitive visualization. It can be useful for discovering clinically relevant and targetable synthetic lethal interactions in cancer.
A computational approach to infer synthetic lethality interactions directly from frequently altered genes in human cancers. MutExSL is based on the observation that pairs of genes that are altered in a (significantly) mutually exclusive manner in cancers are likely to constitute lethal combinations.
An approach for predicting yeast synthetic lethality, which integrates 17 genomic and proteomic features and the outputs of 10 classification methods. MetaSL thus combines the strengths of existing methods and achieves the highest area under the receiver operating characteristics (ROC) curve (AUC) of 87.1% among all competitors on yeast data.
Allows users to automatically extract median time of death from “death fluorescence” DF curves. LFASS enables new insights about stress resistance and to detect ageing. The software runs without a strict sample size and added reagents. It is compatible with transgenic, frail or immobile worms and is applicable to modern screening platforms employing transparent mate.
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