ArticleNucleic acids research2024
SLKB: synthetic lethality knowledge base.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Explainable/interpretable artificial intelligence and knowledge graph neural network for synthetic lethality prediction: overview, challenges, and future directions.Briefings in bioinformatics · 2026Review
- PARP1-BCAT2 axis upregulates ABCG1 via histone lactylation to drive acquired PARP inhibitor resistance in prostate cancer.Journal of experimental & clinical cancer research : CR · 2026Article
- Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention set.Bioinformatics advances · 2026Article
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Article
- A multi-layer encoder prediction model for individual sample specific gene combination effect (MLEC-iGeneCombo).PLoS computational biology · 2025Article
- A compendium of synthetic lethal gene pairs defined by extensive combinatorial pan-cancer CRISPR screening.Genome biology · 2025Article
- Benchmarking genetic interaction scoring methods for identifying synthetic lethality from combinatorial CRISPR screens.NAR genomics and bioinformatics · 2025Article
- Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.Briefings in bioinformatics · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.