Table 8.
Tools and applications reviewed in this study
| Tool | Description | Availability | Website |
|---|---|---|---|
| G2G | Predict SL interactions based on mapping genes to GO terms | Online | http://bnet.cs.tau.ac.il/g2g/ |
| SPAGE-Finder | Predict SL interactions from TCGA data | Online | https://amagen.shinyapps.io/spage/ |
| SynLeGG | Predict SL interactions utilizing multiSEp gene expression clusters to Partition CRISPR essentiality scores and mutations from whole-exome sequencing | Online | www.overton-lab.uk/synlegg |
| SL-BioDP | Predict SL interactions from hallmark cancer pathways by mining cancer’s genomic and chemical interactions | Online | https://sl-biodp.nci.nih.gov/sl_index.php |
| DiscoverSL | R package for multiomic data-driven prediction of SL interactions in cancer | Standalone | https://github.com/shaoli86/DiscoverSL/releases/tag/V1.0 |
| ISLE | Identify the most likely clinically relevant SL interactions by mining TCGA cohort | Standalone | https://github.com/jooslee/ISLE/ |
| GEMINI | Identify SL interactions from combinatorial CRISPR experiments | Standalone | https://github.com/sellerslab/gemini |
| Fast-SL | identify synthetic lethal sets in metabolic networks | Standalone | https://github.com/RamanLab/FastSL |
Note: SynLeGG, Synthetic Lethality using Gene expression and Genomics; SL-BioDP, Synthetic Lethality BioDiscovery portal.