OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines

D940–D944 Nucleic Acids Research, 2017, Vol. 45, Database issue doi: 10.1093/nar/gkw1013 Published online 30 October 2016 OGEE v2: an update of the online gene essentiality database with special focus on differentially essential genes in human cancer cell lines Wei-Hua Chen1,*,† , Guanting Lu2,† , Xiao Chen3 , Xing-Ming Zhao3 and Peer Bork4,5,6,7 1 Received August 23, 2016; Revised October 14, 2016; Editorial Decision October 15, 2016; Accepted October 18, 2016 ABSTRACT OGEE is an Online GEne Essentiality database. To enhance our understanding of the essentiality of genes, in OGEE we collected experimentally tested essential and non-essential genes, as well as associated gene properties known to contribute to gene essentiality. We focus on large-scale experiments, and complement our data with text-mining results. We organized tested genes into data sets according to their sources, and tagged those with variable essentiality statuses across data sets as conditionally essential genes, intending to highlight the complex interplay between gene functions and environments/experimental perturbations. Developments since the last public release include increased numbers of species and gene essentiality data sets, inclusion of non-coding essential sequences and genes with intermediate essentiality statuses. In addition, we included 16 essentiality data sets from cancer cell lines, corresponding to 9 human cancers; with OGEE, users can easily explore the shared and differentially essential genes within and between cancer types. These genes, especially those derived from cell lines that are similar to tumor samples, could reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer types, and can be further screened to identify targets for cancer therapy and/or new drug development. OGEE is freely available at http:// ogee.medgenius.info . INTRODUCTION Essential genes are those genes of an organism that are critical for its survival; essential genes are of particular importance because of their theoretical and practical applications such as studying the robustness of a biological system (1), defining a minimal genome/organism (2,3) and identifying effective therapeutic targets in pathogens (4–6) and human cancers (7–11). In recent years, the technologies used for gene essentiality studies have been evolving rapidly, ranging from low-throughput single gene knockout experiment (12,13) to high-throughput mutagenesis (3), RNAi (7,8) and more recently CRISPR-based genome editing methods (14– 18); recent studies showed that CRISPR technology outperformed other methods (14,19), featuring low noise and minimal off-target effects (19). Being essential is not an intrinsic property of a gene; rather, it is highly dependent on a variety of factors including the function and expression pattern of the gene, the genetic background of the host, the environment and other settings. For example, genes coding for proteins involved in the biosynthesis of amino acids, nucleic acids and vitamins are essential for cell survival in minimal media, but not in rich media where the corresponding metabolites are supplied (20). In addition, different experimental methods may generate different results. For example, CRISPR-based methods could identify more essential genes than siRNAbased methods (21), while cell lines generate lower propor- * To whom correspondence should be addressed. Tel: +86 2787542127; Fax: +86 2787542527; Email: † These authors contributed equally to the paper as first authors.  C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology (HUST), 430074 Wuhan, Hubei, China, 2 Department of Blood Transfusion, Tangdu Hospital, the Fourth Military Medical University, No 1, Xinsi Road, Chanba District, 710000 Xi’an, China, 3 Department of Computer Science and Technology, Tongji University, Shanghai 201804, China, 4 European molecular biology laboratory (EMBL), Meyerhofstrasse 1, 69117 Heidelberg, Germany, 5 Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69120 Heidelberg, Germany, 6 Max-Delbrück-Centre for Molecular Medicine, Robert-Rössle-Straße 10, 13125 Berlin, Germany and 7 Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany Nucleic Acids Research, 2017, Vol. 45, Database issue D941 A Developmental genes Non-developmental genes 79.04 40.86 0.00 79.04 PE% (proportion of essential genes) B Duplicates Singlets 44.07 53.49 PE% (proportion of essential genes) 50.00 53.49 Figure 1. Screenshots taken from the ‘Analyze’ page. With integrated tools, users can easily analyze the collected data and visualize the results. Shown here are the proportion of essential genes (PE ) as a function of involvement in development (developmental versus non-developmental genes, panel (A) and duplication statuses (duplicates versus singlets, panel (B)) in mouse. tion of essential genes than in vivo if the same multi-cellular organism is used (22). Genes with variable essentiality statuses under different circumstances are referred to ‘conditionally essential genes (CEGs)’ or ‘differentially essential genes (DEGs)’ (14,22). CEG is a biologically meaningful and very important concept; e.g. genes that are essential in a cancer cell line but are non-essential in human tissues can reveal the oncogenic drivers, paralogous gene expression pattern and chromosomal structure of the corresponding cancer type (14). In 2012, we introduced OGEE v1 (22) to promote the concept of ‘conditional essentiality’, which had not been widely adopted by existing essential gene databases at the time, and to advance our understanding on gene essentiality. We did so by including not only essential and non-essential genes, but also associated gene properties that are known to affect gene essentiality; we provided tools that allow users to compare gene essentiality among different gene groups, or compare properties of essential genes to non-essential genes. In addition, we organized experimentally tested genes into data sets according to their sources and tagged those with variable essentiality statuses across data sets as CEGs. In this study we introduce an updated version of OGEE. In this new version we added new species and new data sets; we added (...truncated)