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Summary Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. Availability and implementation The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide OBJECTIVES Thoracic endovascular aortic repair (TEVAR) for aortic arch aneurysms is challenging because of anatomical restrictions and the presence of cervical branches. Revascularization of the cervical branch is required when conventional commercial stent grafts are used. TEVAR using fenestrated stent grafts (FSG) often does not require additional procedures to revascularize cervical branches. This study aimed to evaluate the features and initial and midterm outcomes of TEVAR using fenestrated stent grafts. METHODS From April 2007 to December 2016, 101 consecutive patients underwent TEVAR using fenestrated stent grafts for distal aortic arch aneurysms at a single centre. Technical success, complications, freedom from aneurysm-related death, secondary intervention and aneurysm progression were retrospectively investigated. RESULTS All the patients underwent TEVAR using fenestrated stent grafts. The 30-day mortality rate was zero. Cerebral infarction, access route problems and spinal cord injury occurred in 4, 3 and 2 patients, respectively. Each type of endoleak was observed in 38 of the 101 patients during the course of the study; 20/38 patients had minor type 1 endoleaks at the time of discharge. The endoleak disappeared in 2 patients and showed no significant change in 8 patients; however, the aneurysm expanded over time in 10 patients. Additional treatment was performed in 8 of the 10 patients with type 1 endoleaks and dilatation of the aneurysm. The rate of freedom from aneurysm-related death during the observation period was 98%. CONCLUSIONS TEVAR with FSG is a simple procedure, with few complications. Additional treatment has been observed to reduce aneurysm-related deaths, even in patients with endoleaks and enlarged aneurysms. Based on this study, the outcomes of endovascular repair of aortic arch aneurysms using a fenestrated stent graft seem acceptable. [ABSTRACT FROM AUTHOR]

Summary The Kyoto Encyclopedia of Genes and Genomes (KEGG) database serves as a valuable systems biology resource and is widely utilized in diverse research fields. However, existing software does not allow flexible visualization and network analyses of the vast and complex KEGG data. We developed ggkegg , an R package that integrates KEGG information with ggplot2 and ggraph. ggkegg enables enhanced visualization and network analyses of KEGG data. We demonstrate the utility of the package by providing examples of its application in single-cell, bulk transcriptome, and microbiome analyses. ggkegg may empower researchers to analyze complex biological networks and present their results effectively. Availability and implementation The package and user documentation are available at: https://github.com/noriakis/ggkegg. [ABSTRACT FROM AUTHOR]

Background The relationship between chronic kidney disease (CKD) and the gut microbiome, which interact through chronic inflammation, uraemic toxin production and immune response regulation, has gained interest in the development of CKD therapies. However, reports using shotgun metagenomic analysis of the gut microbiome are scarce, especially for early CKD. Here we characterized gut microbiome differences between non-CKD participants and ones with early CKD using metagenomic sequencing. Methods In total, 74 non-CKD participants and 37 participants with early CKD were included based on propensity score matching, controlling for various factors including dietary intake. Stool samples were collected from participants and subjected to shotgun sequencing. Bacterial and pathway abundances were profiled at the species level with MetaPhlAn2 and HUMAnN2, respectively, and overall microbiome differences were determined using Bray–Curtis dissimilarities. Diabetic and non-diabetic populations were analysed separately. Results For diabetic and non-diabetic participants, the mean estimated glomerular filtration rates of the CKD group were 53.71 [standard deviation (SD) 3.87] and 53.72 (SD 4.44), whereas those of the non-CKD group were 72.63 (SD 7.72) and 76.10 (SD 9.84), respectively. Alpha and beta diversities were not significantly different between groups. Based on taxonomic analysis, butyrate-producing species Roseburia inulinivorans , Ruminococcus torques and Ruminococcus lactaris were more abundant in the non-CKD group, whereas Bacteroides caccae and Bacteroides coprocora were more abundant in the non-diabetic CKD group. Conclusions Although gut microbiome changes in individuals with early CKD were subtle, the results suggest that changes related to producing short-chain fatty acids can already be observed in early CKD. [ABSTRACT FROM AUTHOR]

Summary When investigating gene expression profiles, determining important directed edges between genes can provide valuable insights in addition to identifying differentially expressed genes. In the subsequent functional enrichment analysis (EA), understanding how enriched pathways or genes in the pathway interact with one another can help infer the gene regulatory network (GRN), important for studying the underlying molecular mechanisms. However, packages for easy inference of the GRN based on EA are scarce. Here, we developed an R package, CBNplot , which infers the Bayesian network (BN) from gene expression data, explicitly utilizing EA results obtained from curated biological pathway databases. The core features include convenient wrapping for structure learning, visualization of the BN from EA results, comparison with reference networks, and reflection of gene-related information on the plot. As an example, we demonstrate the analysis of bladder cancer-related datasets using CBNplot , including probabilistic reasoning, which is a unique aspect of BN analysis. We display the transformability of results obtained from one dataset to another, the validity of the analysis as assessed using established knowledge and literature, and the possibility of facilitating knowledge discovery from gene expression datasets. Availability and implementation The library, documentation and web server are available at https://github.com/noriakis/CBNplot. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]