Current Issue : July-September Volume : 2023 Issue Number : 3 Articles : 5 Articles
Background: Biomedical researchers use alignments produced by BLAST (Basic Local Alignment Search Tool) to categorize their query sequences. Producing such alignments is an essential bioinformatics task that is well suited for the cloud. The cloud can perform many calculations quickly as well as store and access large volumes of data. Bioinformaticians can also use it to collaborate with other researchers, sharing their results, datasets and even their pipelines on a common platform. Results: We present ElasticBLAST, a cloud native application to perform BLAST alignments in the cloud. ElasticBLAST can handle anywhere from a few to many thousands of queries and run the searches on thousands of virtual CPUs (if desired), deleting resources when it is done. It uses cloud native tools for orchestration and can request discounted instances, lowering cloud costs for users. It is supported on Amazon Web Services and Google Cloud Platform. It can search BLAST databases that are user provided or from the National Center for Biotechnology Information. Conclusion: We show that ElasticBLAST is a useful application that can efficiently perform BLAST searches for the user in the cloud, demonstrating that with two examples. At the same time, it hides much of the complexity of working in the cloud, lowering the threshold to move work to the cloud....
Background. Renal ischemia-reperfusion injury (RIRI) plays an important role in the poor prognosis of patients with renal transplants. However, the potential targets and mechanism of IRI are still unclear. Method. Differential gene expression (DEG) analysis and weighted correlation network analysis (WGCNA) were performed on the GSE27274 dataset. Pathway enrichment analysis on the DEGs was performed. To identify the hub DEGs, we constructed a protein-protein interaction (PPI) network. Finally, the hub genes were verified, and candidate drugs were screened from the DsigDB database. Results. A hundred DEGs and four hub genes (Atf3, Psmb6, Psmb8, and Psmb10) were screened out. Pathway enrichment analysis revealed that 100 DEGs were mainly enriched in apoptosis and the TNF signaling pathway. The four hub genes were verified in animal models and another dataset (GSE148420). Thereafter, a PPI network was used to identify the four hub genes (Atf3, Psmb6, Psmb8, and Psmb10). Finally, eight candidate drugs were identified as potential drugs. Conclusion. Three hub genes (Psmb6, Psmb8, and Psmb10) were associated with RIRI and could be potential novel biomarkers for RIRI....
Background Thoracic acute aortic dissection (TAAD), one of the most fatal cardiovascular diseases, leads to sudden death, however, its mechanism remains unclear. Methods Three Gene Expression Omnibus datasets were employed to detect differentially expressed genes (DEGs). A similar function and co-expression network was identified by weighted gene co-expression network analysis. The least absolute shrinkage and selection operator, random forest, and support vector machines-recursive feature elimination were utilized to filter diagnostic TAAD markers, and then screened markers were validated by quantitative real-time PCR and another independent dataset. CIBERSORT was deployed to analyze and evaluate immune cell infiltration in TAAD tissues. Results Twenty-five DEGs were identified and narrowed down to three after screening. Finally, two genes, SLC11A1 and FGL2, were verified by another dataset and qRT-PCR. Function analysis revealed that SLC11A1 and FGL2 play significant roles in immune-inflammatory responses. Conclusion SLC11A1 and FGL2 are differently expressed in aortic dissection and may be involved in immune-inflammatory responses....
Purpose. Papillary renal cell carcinoma (pRCC) is the second most common histological subtype of adult kidney tumors, with a poor prognosis due to limited understanding of the disease mechanism. Herein, we have performed high-throughput bioinformatic screening to explore and identify potential biomarkers of DNA damage and oxidative stress for pRCC. Methods. RNA sequencing data related to pRCC were downloaded from the TCGA database, and differentially expressed genes (DEG) were identified by a wide variety of clustering and classification algorithms, including self-organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and hyphenated techniques such as neuro-fuzzy networks. Then DAVID and STRING online biological information tools were used to analyze functional enrichment of the regulatory networks of DEG and construct a protein-protein interaction (PPI) network, and then the Cytoscape software was used to identify hub genes. The importance of key genes was assessed by the analysis of the Kaplan–Meier survival curves using the R software. Lastly, we have analyzed the expression of hub genes of DNA damage and oxidative stress (BDKRB1, NMUR2, PMCH, and SAA1) in pRCC tissues and adjacent normal tissues, as well as the relationship between the expression of hub genes in pRCC tissues and pathological characteristics and prognosis of pRCC patients. Results. A total of 1,992 DEGs for pRCC were identified, with 1,142 upregulated ones and 850 downregulated ones. The DEGs were significantly enriched in activities including DNA damage and oxidative stress, chemical synaptic transmission, an integral component of the membrane, calcium ion binding, and neuroactive ligand-receptor interaction. cytoHubba in the Cytoscape software was used to determine the top 10 hub genes in the PPI network as BDKRB2, NMUR2, NMU, BDKRB1, LPAR5, KNG1, LPAR3, SAA1, MCHR1, PMCH, and NCAPH. Furthermore, the expression level of hub genes BDKRB1, NMUR2, PMCH, and SAA1 in pRCC tissues was significantly higher than that in the adjacent normal tissues. Meanwhile, the expression level of hub genes BDKRB1, NMUR2, PMCH, and SAA1 in pRCC tissues was significantly positively correlated with tumor stage, lymph node metastasis, and the histopathology grade of pRCC. In addition, high expression levels of hub genes BDKRB1, NMUR2, PMCH, and SAA1 were associated with a poor prognosis for patients with pRCC. Univariate and multivariate analyses showed that the expression of hub genes BDKRB1, NMUR2, PMCH, and SAA1 were independent risk factors for the prognosis of patients with pRCC. Conclusion. The results of this analysis suggested that BDKRB1, NMUR2, PMCH, and SAA1 might be potential prognostic biomarkers and novel therapeutic targets for pRCC....
Background: Gene co-expression networks represent modules of genes with shared biological function, and have been widely used to model biological pathways in gene expression data. Co-expression networks associated with a specific trait can be constructed and identified using weighted gene co-expression network analysis (WGCNA), which is especially useful for the study of transcriptional signatures in disease. WGCNA networks are typically constructed using both disease and wildtype samples, so molecular pathways associated with disease are identified. However, it would be advantageous to study such co-expression networks in their disease context across spatiotemporal conditions, but currently there is no comprehensive software implementation for this type of analysis. Results: Here, we introduce a WGCNA-based procedure, multiWGCNA, that is tailored to datasets with variable spatial or temporal traits. As well as constructing the combined network, multiWGCNA also generates a network for each condition separately, and subsequently maps these modules between and across designs, and performs relevant downstream analyses, including module-trait correlation and module preservation. When applied to astrocyte-specific RNA-sequencing (RNA-seq) data from various brain regions of mice with experimental autoimmune encephalitis, multiWGCNA resolved the de novo formation of the neurotoxic astrocyte transcriptional program exclusively in the disease setting. Using time-course RNA-seq from mice with tau pathology (rTg4510), we demonstrate how multiWGCNA can also be used to study the temporal evolution of pathological modules over the course of disease progression. Conclusion: The multiWGCNA R package can be applied to expression data with two dimensions, which is especially useful for the study of disease-associated modules across time or space. The source code and functions are freely available at: https:// github. com/ fogel lab/ multi WGCNA....
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