TABLE 2.

Essential gene databases and computational programs to predict essential genes and design genomic deletions.

Program	Function	URL	References
DEG 15	Database of essential genomic regions based on experimental data with embedded BLAST tools for homology searches	www.essentialgene.org/	Luo et al. (2021)
pDEG	Database of predicted essential genes within Mycoplasma strains	http://tubic.org/pdeg/	Lin and Zhang, (2011)
NetGenes	Database of predicted essential genes for more than 2,700 organisms based on protein-protein interaction network features	https://rbc-dsai-iitm.github.io/NetGenes/	Senthamizhan et al. (2021)
ePath	Database of predicted essential genes for more than 4,000 organisms based on previous experimental data and functional KEGG orthologs	https://www.pubapps.vcu.edu/epath/	Kong et al. (2019)
OGEE	Database of essential genes from experimental data for 91 bacterial strains with additional gene features like expression profiles, conservation, evolutionary origins etc.	http://ogeedb.embl.de	Chen et al. (2017)
EGGS	Database of essential genes from experimental data for 11 different species with visualization and analysis on a subsystem diagram	https://pubseed.theseed.org/FIG/eggs.cgi	Gerdes et al. (2006)
			Overbeek et al. (2005)
CEG	Database of essential genes based on data from DEG that are clustered based on function	http://cefg.uestc.cn/ceg	Liu et al. (2020a)
			Ye et al. (2013)
CEG_Match	Within the CEG database, bases the prediction of essential genes on function	http://cefg.uestc.cn/ceg	Liu et al. (2020a)
			Ye et al. (2013)
ZCURVE 3.0	Predicts genes from an unannotated genome and will also predict essential genes from that	http://guolab.whu.edu.cn/zcurve/	Hua et al. (2015)
Geptop 2.0	Predicts gene essentiality based on sequence conservation and orthology with comparing a fully sequenced organism to 37 other species	http://guolab.whu.edu.cn/geptop/	Wen et al. (2019)
EGP	Machine learning-based method applying sequence composition features to predict essential genes with the input of nucleotide sequence	http://cefg.uestc.edu.cn:9999/egp	Ning et al. (2014)
Deeply Essential	Deep neural network to identify essential genes in bacteria based on sequence features only	https://github.com/ucrbioinfo/DeeplyEssential	Hasan and Lonardi, (2020)
DELEAT	Prediction of essential genes based on 6 features not dependent on experimental or functional data with design of large genomic deletions to minimize the organism’s genome	https://github.com/jime-sg/deleat	Solana et al. (2021)
MinGenome	Minimal genome design with large deletion predictions using whole cell models using biological knowledge on gene locations and essentiality	https://github.com/maranasgroup/MinimalGenome	Wang and Maranas, (2018)
GAMA	Comprehensive and computationally intensive simulation of gene essentiality with gene deletions to construct a true minimal cell that cannot be run on a single computer	https://github.com/GriersonMarucciLab	Rees-Garbutt et al. (2020)
Minesweeper	Simultaneously assesses genomic regions that can be deleted, starting with larger deletion regions moving to individual genes	https://github.com/GriersonMarucciLab	Rees-Garbutt et al. (2020)