Genomic GC content prediction in prokaryotes from a sample of genes

GC level is a key feature in prokaryotic genomes. Widely employed in evolutionary studies, new insights appear however limited because of the relatively low number of characterized genomes. Since public databases mainly comprise several hundreds of prokaryotes with a low number of sequences per genome, a reliable prediction method based on available sequences may be useful for studies that need a trustworthy estimation of whole genomic GC. As the analysis of completely sequenced genomes shows a great variability in distributional shapes, it is of interest to compare different estimators. Our analysis shows that the mean of GC values of a random sample of genes is a reasonable estimator, based on simplicity of the calculation and overall performance. However, usually sequences come from a process that cannot be considered as random sampling. When we analyzed two introduced sources of bias (gene length and protein functional categories) we were able to detect an additional bias in the estimation for some cases, although the precision was not affected. We conclude that the mean genic GC level of a sample of 10 genes is a reliable estimator of genomic GC content, showing comparable accuracy with many widely employed experimental methods.     

Searching for RNA genes using base-composition statistics

The hypothesis that genomic regions rich in non-protein-coding RNAs (ncRNAs) can be identified using local variations in single-base and dinucleotide statistics has been investigated. (G+C)%, (G-C)% difference, (A-T)% difference and dinucleotide-frequency statistics were compared among seven classes of ncRNAs and three genomes. Significant variations were observed in (G+C)% and, in Methanococcus jannaschii, in the frequency of the dinucleotide 'CG'. Screening programs based on these two base-composition statistics were developed. With (G+C)% screening alone, a 1% fraction of the M.jannaschii genome containing all 44 known transfer RNAs, ribosomal RNAs and signal recognition particle RNAs could be identified. When (G+C)% combined with CG dinucleotide-frequency screening was used, 43 of the 44 known M.jannaschii structural ncRNAs were again identified, while the number of presumably false hits overlapping a known or putative protein-coding gene was reduced from 15 to 6. In addition, 19 candidate ncRNAs were identified including one with significant homology to several known archaeal RNaseP RNAs.     

Silent genes in prokaryotes

Abstract DNA sequence analysis provides excellent evidence for the origin of new genes, encoding new enzyme specificities or isozymes, via gene-duplication. New genes which arise in this way are likely to have arisen via silent gene intermediates. Such 'silent' genes are conceptually distinct from 'cryptic' genes which may also be silent; whereas cryptic genes are thought to be retained due to periodic selection, silent genes would be expected to have only a transient existence in the genome. Only very few of the known inactive genes are possibly (and with varying degrees of likelihood) of the 'silent' type.     

植物长链非编码RNA的生物信息学预测与分析研究进展

长链非编码RNA(Long non-coding RNAs,lncRNAs)是一类广泛存在于真核生物中,长度大于200个核苷酸、无蛋白编码功能,具有调控基因转录后表达的RNA转录本。新近研究表明,lncRNA在多种生物途径中起着重要调节作用。生物信息学由生物、数学、计算机科学,统计学等多学科交叉产生,能从全局和系统水平对大数据信息进行深入挖掘与分析。采用生物信息学方法预测与分析lncRNA是当前发现和鉴定植物lncRNA的重要策略之一。本文梳理和总结了近年来采用生物信息学预测植物lncRNA及其靶基因的方法策略,以期为今后深入认知植物lncRNA在植物的生长发育过程、抗逆境胁迫及系统进化等过程中的作用研究提供一定参考。     

ADAR-mediated RNA editing in non-coding RNA sequences

Adenosine to inosine (A-to-I) RNA editing is the most abundant editing event in animals. It converts adenosine to inosine in double-stranded RNA regions through the action of the adenosine deaminase acting on RNA (ADAR) proteins. Editing of pre-mRNA coding regions can alter the protein codon and increase functional diversity. However, most of the A-to-I editing sites occur in the non-coding regions of pre-mRNA or mRNA and non-coding RNAs. Untranslated regions (UTRs) and introns are located in pre-mRNA non-coding regions, thus A-to-I editing can influence gene expression by nuclear retention, degradation, alternative splicing, and translation regulation. Non-coding RNAs such as microRNA (miRNA), small interfering RNA (siRNA) and long non-coding RNA (lncRNA) are related to pre-mRNA splicing, translation, and gene regulation. A-to-I editing could therefore affect the stability, biogenesis, and target recognition of non-coding RNAs. Finally, it may influence the function of non-coding RNAs, resulting in regulation of gene expression. This review focuses on the function of ADAR-mediated RNA editing on mRNA non-coding regions (UTRs and introns) and non-coding RNAs (miRNA, siRNA, and lncRNA).     

Features for computational operon prediction in prokaryotes

Accurate prediction of operons can improve the functional annotation and application of genes within operons in prokaryotes. Here, we review several features: (i) intergenic distance, (ii) metabolic pathways, (iii) homologous genes, (iv) promoters and terminators, (v) gene order conservation, (vi) microarray, (vii) clusters of orthologous groups, (viii) gene length ratio, (ix) phylogenetic profiles, (x) operon length/size and (xi) STRING database scores, as well as some other features, which have been applied in recent operon prediction methods in prokaryotes in the literature. Based on a comparison of the prediction performances of these features, we conclude that other, as yet undiscovered features, or feature selection with a receiver operating characteristic analysis before algorithm processing can improve operon prediction in prokaryotes.     

Vitamin U and RNA metabolism in prokaryotes

The paper is concerned with a study of the vitamin U effect on the rate of 14C-uridine incorporation into various categories of RNA in E. coli MRE-600 cells. It is found that cells grown with vitamin U (0.06 mg/ml) and incubated with 14C-uridine for 5 min are able to produce a 10-12-fold increase of the label incorporation into 4 S and 5 S RNA and a 14-fold increase into high polymeric RNA in comparison with the control cells. Under longer intervals of incubation (20 min) the intensity of high-polymeric RNA formation was half as high as for 4 S and 5 S RNA formation. MAK column chromatography of high-polymeric RNA in salt and temperature gradients showed the presence of the RNA temperature fraction in bacteria cells. Vitamin U stimulates the formation of various categories of RNA and causes a quantitative increase in the RNA temperature fraction.     

DNA-energetics-based analyses suggest additional genes in prokaryotes

We present here a novel methodology for predicting new genes in prokaryotic genomes on the basis of inherent energetics of DNA. Regions of higher thermodynamic stability were identified, which were filtered based on already known annotations to yield a set of potentially new genes. These were then processed for their compatibility with the stereo-chemical properties of proteins and tripeptide frequencies of proteins in Swissprot data, which results in a reliable set of new genes in a genome. Quite surprisingly, the methodology identifies new genes even in well-annotated genomes. Also, the methodology can handle genomes of any GC-content, size and number of annotated genes.     

Subcellular localization of RNA and proteins in prokaryotes

The field of bacterial cell biology has been revolutionized in the last decade by improvements in imaging capabilities which have revealed that bacterial cells, previously thought to be non-compartmentalized, possess an intricate higher-order organization. Many bacterial proteins localize to specific subcellular domains and regulate the spatial deployment of other proteins, DNA and lipids. Recently, the surprising discovery was made that bacterial RNA molecules are also specifically localized. However, the mechanisms that underlie bacterial cell architecture are just starting to be unraveled. The limited number of distribution patterns observed thus far for bacterial proteins and RNAs, and the similarity between the patterns exhibited by these macromolecules, suggest that the processes that underlie their localization are inextricably linked. We discuss these spatial arrangements and the insights that they provide on processes, such as localized translation, protein complex formation, and crosstalk between bacterial machineries.