OGEE is an Online GEne Essentiality database. Gene essentiality is not a static and binary property, rather a context-dependent and evolvable property in all forms of life. In OGEE we collect not only experimentally tested essential and non-essential genes, but also associated gene properties that contributes to gene essentiality. We tagged conditionally essential genes that show variable essentiality statuses across datasets to highlight complex interplays between gene functions and environmental/experimental perturbations. OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581. These human cancer cell lines were tested with high-throughput experiments such as CRISPR-Cas9 and RNAi; in total, 150 of which were tested by both techniques. We also included factors known to contribute to gene essentiality for these cell lines, such as genomic mutation, methylation and gene expression, along with extensive graphical visualizations for ease of understanding of these factors. OGEE v3 can be accessible freely at https://v3.ogee.info.

Phages invade microbes, accomplish host lysis and are of vital importance in shaping the community structure of environmental microbiota. More importantly, most phages have very specific hosts; they are thus ideal tools to manipulate environmental microbiota at species-resolution. The main purpose of MVP (Microbe Versus Phage) is to provide a comprehensive catalog of phage-microbe interactions and assist users to select phage(s) that can target (and potentially to manipulate) specific microbes of interest. We first collected 50 782 viral sequences from various sources and clustered them into 33 097 unique viral clusters based on sequence similarity. We then identified 26 572 interactions between 18 608 viral clusters and 9245 prokaryotes (i.e. bacteria and archaea); we established these interactions based on 30 321 evidence entries that we collected from published datasets, public databases and re-analysis of genomic and metagenomic sequences. Based on these interactions, we calculated the host range for each of the phage clusters and accordingly grouped them into subgroups such as 'species-', 'genus-' and 'family-' specific phage clusters. MVP is equipped with a modern, responsive and intuitive interface, and is freely available at: http://mvp.medgenius.info.

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.

We developed a comprehensive resource for the genome-reduced bacterium Mycoplasma pneumoniae comprising 1748 consistently generated '-omics' data sets, and used it to quantify the power of antisense non-coding RNAs (ncRNAs), lysine acetylation, and protein phosphorylation in predicting protein abundance (11%, 24% and 8%, respectively). These factors taken together are four times more predictive of the proteome abundance than of mRNA abundance. In bacteria, post-translational modifications (PTMs) and ncRNA transcription were both found to increase with decreasing genomic GC-content and genome size. Thus, the evolutionary forces constraining genome size and GC-content modify the relative contributions of the different regulatory layers to proteome homeostasis, and impact more genomic and genetic features than previously appreciated. Indeed, these scaling principles will enable us to develop more informed approaches when engineering minimal synthetic genomes.

The thermophilic fungus Chaetomium thermophilum holds great promise for structural biology. To increase the efficiency of its biochemical and structural characterization and to explore its thermophilic properties beyond those of individual proteins, we obtained transcriptomics and proteomics data, and integrated them with computational annotation methods and a multitude of biochemical experiments conducted by the structural biology community. We considerably improved the genome annotation of Chaetomium thermophilum and characterized the transcripts and expression of thousands of genes. We furthermore show that the composition and structure of the expressed proteome of Chaetomium thermophilum is similar to its mesophilic relatives. Data were deposited in a publicly available repository and provide a rich source to the structural biology community.

OGEE is an Online GEne Essentiality database. Its main purpose is to enhance our understanding of the essentiality of genes. This is achieved by collecting not only experimentally tested essential and non-essential genes, but also associated gene features such as expression profiles, duplication status, conservation across species, evolutionary origins and involvement in embryonic development. We focus on large-scale experiments and complement our data with text-mining results. Genes are organized into data sets according to their sources. Genes with variable essentiality status across data sets are tagged as conditionally essential, highlighting the complex interplay between gene functions and environments. Linked tools allow the user to compare gene essentiality among different gene groups, or compare features of essential genes to non-essential genes, and visualize the results. OGEE is freely available at http://ogeedb.embl.de.