Iversenlewis4288

The method extracts features from sequence conservation information through a grey system model, as well as functional domain annotation and subcellular localization. Results Together with the feature analysis and application of the relief feature selection algorithm, the results of 5-fold cross-validation on three datasets achieved a high accuracy of 90.13%, with Matthew's correlation coefficient of 80.34%. The predicted results on an independent test data achieved 87.71% as accuracy and 75.43% of Matthew's correlation coefficient, better than the prediction from the best ubiquitination site prediction tool available. Conclusion Our study may guide experimental design and provide useful insights for studying the mechanisms and modulation of ubiquitination pathways. The code is available at https//github.com/Chunhuixu/UBIPredic_QWRCHX.Background The TIFY gene family is a group of plant-specific proteins involved in the jasmonate (JA) metabolic process, which plays a vital role in plant growth and development as well as stress response. Although it has been extensively studied in many species, the significance of this family is not well studied in wheat. Objective To comprehensively understand the genome organization and evolution of TIFY family in wheat, a genome-wide identification was performed in wheat and its two progenitors using updated genome information provided here. click here Results In total, 63, 13 and 17 TIFY proteins were identified in wheat, Triticum urartu and Aegilops tauschii respectively. Phylogenetic analysis clustered them into 18 groups with 14 groups possessing A, B and D copies in wheat, demonstrating the completion of the genome as well as the two rounds of allopolyploidization events. Gene structure, conserved protein motif and cis-regulatory element divergence of A, B, D homoeologous copies were also investigated to gain insight into the evolutionary conservation and divergence of homoeologous genes. Furthermore, the expression profiles of the genes were detected using the available RNA-seq and the expression of 4 drought-responsive candidates was further validated through qRT-PCR analysis. Finally, the co-expression network was constructed and a total of 22 nodes with 121 edges of gene pairs were found. Conclusion This study systematically reported the characteristics of the wheat TIFY family, which ultimately provided important targets for further functional analysis and also facilitated the elucidation of the evolution mechanism of TIFY genes in wheat and more.Background Lysine lipoylation which is a rare and highly conserved post-translational modification of proteins has been considered as one of the most important processes in the biological field. To obtain a comprehensive understanding of regulatory mechanism of lysine lipoylation, the key is to identify lysine lipoylated sites. The experimental methods are expensive and laborious. Due to the high cost and complexity of experimental methods, it is urgent to develop computational ways to predict lipoylation sites. Methodology In this work, a predictor named LipoSVM is developed to accurately predict lipoylation sites. To overcome the problem of an unbalanced sample, synthetic minority over-sampling technique (SMOTE) is utilized to balance negative and positive samples. Furthermore, different ratios of positive and negative samples are chosen as training sets. Results By comparing five different encoding schemes and five classification algorithms, LipoSVM is constructed finally by using a training set with positive and negative sample ratio of 11, combining with position-specific scoring matrix and support vector machine. The best performance achieves an accuracy of 99.98% and AUC 0.9996 in 10-fold cross-validation. The AUC of independent test set reaches 0.9997, which demonstrates the robustness of LipoSVM. The analysis between lysine lipoylation and non-lipoylation fragments shows significant statistical differences. Conclusion A good predictor for lysine lipoylation is built based on position-specific scoring matrix and support vector machine. Meanwhile, an online webserver LipoSVM can be freely downloaded from https//github.com/stars20180811/LipoSVM.Background Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive. Objective This study aims to mine hub genes associated with HCC using multiple databases. Methods Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients' information from TCGA database by survminer R package. Results From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients. Conclusion TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.Background In the current study, we aimed to analyze the hypothesis that human myocardial-specific extracellular RNAs expression could be used for acute myocardial injury(AMI) diagnosis. Methodology We used bioinformatics' analysis to identify RNAs linked to ubiquitin system and specific to AMI, named, (lncRNA-RP11-175K6.1), (LOC101927740), microRNA-106b-5p (miR-106b-5p) and Anaphase, promoting complex 11 (ANapc11mRNA). We measured the serum expression of the chosen RNAs in 69 individuals with acute coronary syndromes, 31 individuals with angina pectoris without MI and non-cardiac chest pain and 31 healthy control individuals by real-time reverse-transcription PCR. Results Our study revealed a significant decrease in both lncRNA-RP11-175K6.1 and ANapc11mRNA expression of in the sera samples of AMI patients compared to that of the two control groups alongside with significant upregulation of miR-106b-5p. Conclusion Of note, the investigated serum RNAs decrease the false discovery rate of AMI to 3.2%.