CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms.

Essential genes are key elements for organisms to maintain their living. Building databases that store essential genes in the form of homologous clusters, rather than storing them as a singleton, can provide more enlightening information such as the general essentiality of homologous genes in multiple organisms. In 2013, the first database to store prokaryotic essential genes in clusters, CEG (Clusters of Essential Genes), was constructed. Afterward, the amount of available data for essential genes increased by a factor >3 since the last revision. Herein, we updated CEG to version 2, including more prokaryotic essential genes (from 16 gene datasets to 29 gene datasets) and newly added eukaryotic essential genes (nine species), specifically the human essential genes of 12 cancer cell lines. For prokaryotes, information associated with drug targets, such as protein structure, ligand-protein interaction, virulence factor and matched drugs, is also provided. Finally, we provided the service of essential gene prediction for both prokaryotes and eukaryotes. We hope our updated database will benefit more researchers in drug targets and evolutionary genomics. Database URL: http://cefg.uestc.cn/ceg.

Identifying Bacterial Essential Genes Based on a Feature-Integrated Method.

Essential genes are those genes of an organism that are considered to be crucial for its survival. Identification of essential genes is therefore of great significance to advance our understanding of the principles of cellular life. We have developed a novel computational method, which can effectively predict bacterial essential genes by extracting and integrating homologous features, protein domain feature, gene intrinsic features, and network topological features. By performing the principal component regression (PCR) analysis for Escherichia coli MG1655, we established a classification model with the average area under curve (AUC) value of 0.992 in ten times 5-fold cross-validation tests. Furthermore, when employing this new model to a distantly related organism-Streptococcus pneumoniae TIGR4, we still got a reliable AUC value of 0.788. These results indicate that our feature-integrated approach could have practical applications in accurately investigating essential genes from broad bacterial species, and also provide helpful guidelines for the minimal cell.

Various approaches of laparoscopic common bile duct exploration plus primary duct closure for choledocholithiasis: A systematic review and meta-analysis.

BACKGROUND: Common bile duct (CBD) stones may occur in up to 3%-14.7% of all patients with cholecystectomy. Various approaches of laparoscopic CBD exploration plus primary duct closure (PDC) are the most commonly used and the best methods to treat CBD stone. This systematic review was to compare the effectiveness and safety of the various approaches of laparoscopic CBD exploration plus PDC for choledocholithiasis. DATA SOURCES: Randomized controlled trials (RCTs) and non-randomized controlled trials (NRCTs) (case-control studies or cohort studies) were searched from Cochrane library (until Issue 2, 2015), Web of Science (1980-January 2016), PubMed (1966-January 2016), and Baidu search engine. After independent quality assessment and data extraction, meta-analysis was conducted using RevMan 5.1 software. RESULTS: Four RCTs and 18 NRCTs were included. When compared with choledochotomy exploration (CE) plus T-tube drainage (TTD) (CE + TTD), CE plus PDC (CE + PDC) and CE + PDC with biliary drainage (BD) (CE + PDC + BD) had a lower rate of postoperative biliary peritonitis (OR = 0.22; 95% CI: 0.06, 0.88; P < 0.05; OR = 0.27; 95% CI: 0.08, 0.84; P < 0.05; respectively) where T-tubes were removed more than 3 weeks. The operative time of CE + PDC was significantly shorter (WMD = -24.82; 95% CI: -27.48, -22.16; P < 0.01) than that of CE + TTD in RCTs. Cystic duct exploration (CDE) plus PDC (CDE + PDC) has a lower rate of postoperative complications (OR = 0.39; 95% CI: 0.23, 0.67; P < 0.01) when compared with CE + PDC. Confluence part micro-incision exploration (CME) plus PDC (CME + PDC) has a lower rate of postoperative bile leakage (OR = 0.17; 95% CI: 0.04, 0.74; P < 0.05) when compared with CE + PDC. CONCLUSION: PDC with other various approaches are better than TTD in the treatment of choledocholithiasis.

Identification and analysis of genomic islands in Burkholderia cenocepacia AU 1054 with emphasis on pathogenicity islands.

BACKGROUND: Genomic islands (GIs) are genomic regions that reveal evidence of horizontal DNA transfer. They can code for many functions and may augment a bacterium's adaptation to its host or environment. GIs have been identified in strain J2315 of Burkholderia cenocepacia, whereas in strain AU 1054 there has been no published works on such regions according to our text mining and keyword search in Medline. RESULTS: In this study, we identified 21 GIs in AU 1054 by combining two computational tools. Feature analyses suggested that the predictions are highly reliable and hence illustrated the advantage of joint predictions by two independent methods. Based on putative virulence factors, four GIs were further identified as pathogenicity islands (PAIs). Through experiments of gene deletion mutants in live bacteria, two putative PAIs were confirmed, and the virulence factors involved were identified as lipA and copR. The importance of the genes lipA (from PAI 1) and copR (from PAI 2) for bacterial invasion and replication indicates that they are required for the invasive properties of B. cenocepacia and may function as virulence determinants for bacterial pathogenesis and host infection. CONCLUSIONS: This approach of in silico prediction of GIs and subsequent identification of potential virulence factors in the putative island regions with final validation using wet experiments could be used as an effective strategy to rapidly discover novel virulence factors in other bacterial species and strains.

An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms.

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots.

Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.

Multiple Factors Drive Replicating Strand Composition Bias in Bacterial Genomes.

Composition bias from Chargaff's second parity rule (PR2) has long been found in sequenced genomes, and is believed to relate strongly with the replication process in microbial genomes. However, some disagreement on the underlying reason for strand composition bias remains. We performed an integrative analysis of various genomic features that might influence composition bias using a large-scale dataset of 1111 genomes. Our results indicate (1) the bias was stronger in obligate intracellular bacteria than in other free-living species (p-value=0.0305); (2) Fusobacteria and Firmicutes had the highest average bias among the 24 microbial phyla analyzed; (3) the strength of selected codon usage bias and generation times were not observably related to strand composition bias (p-value=0.3247); (4) significant negative relationships were found between GC content, genome size, rearrangement frequency, Clusters of Orthologous Groups (COG) functional subcategories A, C, I, Q, and composition bias (p-values<1.0×10(-8)); (5) gene density and COG functional subcategories D, F, J, L, and V were positively related with composition bias (p-value<2.2×10(-16)); and (6) gene density made the most important contribution to composition bias, indicating transcriptional bias was associated strongly with strand composition bias. Therefore, strand composition bias was found to be influenced by multiple factors with varying weights.