Geptop 2.0: Accurately Select Essential Genes from the List of Protein-Coding Genes in Prokaryotic Genomes

2021 ◽  
pp. 423-430
Author(s):  
Qing-Feng Wen ◽  
Wen Wei ◽  
Feng-Biao Guo
2013 ◽  
Vol 42 (D1) ◽  
pp. D574-D580 ◽  
Author(s):  
Hao Luo ◽  
Yan Lin ◽  
Feng Gao ◽  
Chun-Ting Zhang ◽  
Ren Zhang

2016 ◽  
Vol 113 (41) ◽  
pp. 11399-11407 ◽  
Author(s):  
Itamar Sela ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin

Bacteria and archaea typically possess small genomes that are tightly packed with protein-coding genes. The compactness of prokaryotic genomes is commonly perceived as evidence of adaptive genome streamlining caused by strong purifying selection in large microbial populations. In such populations, even the small cost incurred by nonfunctional DNA because of extra energy and time expenditure is thought to be sufficient for this extra genetic material to be eliminated by selection. However, contrary to the predictions of this model, there exists a consistent, positive correlation between the strength of selection at the protein sequence level, measured as the ratio of nonsynonymous to synonymous substitution rates, and microbial genome size. Here, by fitting the genome size distributions in multiple groups of prokaryotes to predictions of mathematical models of population evolution, we show that only models in which acquisition of additional genes is, on average, slightly beneficial yield a good fit to genomic data. These results suggest that the number of genes in prokaryotic genomes reflects the equilibrium between the benefit of additional genes that diminishes as the genome grows and deletion bias (i.e., the rate of deletion of genetic material being slightly greater than the rate of acquisition). Thus, new genes acquired by microbial genomes, on average, appear to be adaptive. The tight spacing of protein-coding genes likely results from a combination of the deletion bias and purifying selection that efficiently eliminates nonfunctional, noncoding sequences.


2016 ◽  
Author(s):  
Nikki E Freed ◽  
Dirk Bumann ◽  
Olin K Silander

Gene essentiality - whether or not a gene is necessary for cell growth - is a fundamental component of gene function. It is not well established how quickly gene essentiality can change, as few studies have compared empirical measures of essentiality between closely related organisms. Here we present the results of a Tn-seq experiment designed to detect essential protein coding genes in the bacterial pathogen Shigella flexneri 2a 2457T on a genome-wide scale. Superficial analysis of this data suggested that 451 protein-coding genes in this Shigella strain are critical for robust cellular growth on rich media. Comparison of this set of genes with a gold-standard data set of essential genes in the closely related Escherichia coli K12 BW25113 suggested that an excessive number of genes appeared essential in Shigella but non-essential in E. coli. Importantly, and in converse to this comparison, we found no genes that were essential in E. coli and non-essential in Shigella, suggesting that many genes were artefactually inferred as essential in Shigella. Controlling for such artefacts resulted in a much smaller set of discrepant genes. Among these, we identified three sets of functionally related genes, two of which have previously been implicated as critical for Shigella growth, but which are dispensable for E. coli growth. The data presented here highlight the small number of protein coding genes for which we have strong evidence that their essentiality status differs between the closely related bacterial taxa E. coli and Shigella. A set of genes involved in acetate utilization provides a canonical example. These results leave open the possibility of developing strain-specific antibiotic treatments targeting such differentially essential genes, but suggest that such opportunities may be rare in closely related bacteria.


2019 ◽  
Vol 35 (21) ◽  
pp. 4344-4349 ◽  
Author(s):  
Yuwei Zhang ◽  
Yang Tao ◽  
Huihui Ji ◽  
Wei Li ◽  
Xingli Guo ◽  
...  

Abstract Motivation Genome-scale CRISPR/Cas9 system has been a democratized gene editing technique and widely used to investigate gene functions in some biological processes and diseases especially cancers. Aiming to characterize gene aberrations and assess their effects on cancer, we designed a pipeline to identify the essential genes for pan-cancer. Methods CRISPR screening data were used to identify the essential genes that were collected from published data and integrated by Robust Rank Aggregation algorithm. Then, hypergeometrics test and random walks with restart (RWR) were used to predict additional essential genes on broader scale. Finally, the expression status and potential roles of these genes were explored based on TCGA portal and regulatory network analysis. Results We collected 926 samples from 10 CRISPR-based screening studies involving 33 different types of cancer to identify cancer-essential genes, which consists of 799 protein-coding genes (PCGs) and 97 long non-coding RNAs (lncRNAs). Then, we constructed a ‘bi-colored’ network with both PCGs and lncRNAs and applied it to predict additional essential genes including 495 PCGs and 280 lncRNAs on a broader scale using hypergeometrics test and RWR. After obtaining all essential genes, we further investigated their potential roles in cancer and found that essential genes have higher and more stable expression levels, and are associated with multiple cancer-associated biological processes and survival time. The regulatory network analysis detected two intriguing modules of essential genes participating in the regulation of cell cycle and ribosome biogenesis in cancer. Availability and implementation   Supplementary information Supplementary data are available at Bioinformatics online.


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