GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data

BACKGROUND The interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task, where the essential information is distributed among different tabular and list formats—normalized expression values, results from differential expression analysis, and results from functional enrichment analyses. A number of tools and databases are widely used for the purpose of identification of relevant functional patterns, yet often their contextualization within the data and results at hand is not straightforward, especially if these analytic components are not combined together efficiently. RESULTS We developed the GeneTonic software package, which serves as a comprehensive toolkit for streamlining the interpretation of functional enrichment analyses, by fully leveraging the information of expression values in a differential expression context. GeneTonic is implemented in R and Shiny, leveraging packages that enable HTML-based interactive visualizations for executing drilldown tasks seamlessly, viewing the data at a level of increased detail. GeneTonic is integrated with the core classes of existing Bioconductor workflows, and can accept the output of many widely used tools for pathway analysis, making this approach applicable to a wide range of use cases. Users can effectively navigate interlinked components (otherwise available as flat text or spreadsheet tables), bookmark features of interest during the exploration sessions, and obtain at the end a tailored HTML report, thus combining the benefits of both interactivity and reproducibility. CONCLUSION GeneTonic is distributed as an R package in the Bioconductor project (https://bioconductor.org/packages/GeneTonic/) under the MIT license. Offering both bird’s-eye views of the components of transcriptome data analysis and the detailed inspection of single genes, individual signatures, and their relationships, GeneTonic aims at simplifying the process of interpretation of complex and compelling RNA-seq datasets for many researchers with different expertise profiles

Interactive and reproducible workflows for exploring and modeling RNA-seq data with pcaExplorer, ideal, and GeneTonic

The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC

Transforming growth factor-β activated kinase 1 (Tak1) is activated in hepatocellular carcinoma, mediates tumor progression, and predicts unfavorable outcome

Although knowledge on inflammatory signaling pathways driving cancer initiation and progression has been increasing, molecular mechanisms in hepatocarcinogenesis are still far from being completely understood. Hepatocyte-specific deletion of the MAPKKK Tak1 in mice recapitulates important steps of hepatocellular carcinoma (HCC) development, including the occurrence of cell death, steatohepatitis, dysplastic nodules, and HCCs. However, overactivation of Tak1 in mice upon deletion of its deubiquitinase Cyld also results in steatohepatitis and HCC development. To investigate Tak1 and Cyld in human HCCs, we created a tissue microarray to analyze their expression by immunohistochemistry in a large and well-characterized cohort of 871 HCCs of 561 patients. In the human liver and HCC, Tak1 is predominantly present as its isoform Tak1A and predominantly localizes to cell nuclei. Tak1 is upregulated in diethylnitrosamine-induced mouse HCCs as well as in human HCCs independent of etiology and is further induced in distant metastases. A high nuclear Tak1 expression is associated with short survival and vascular invasion. When we overexpressed Tak1A in Huh7 cells, we observed increased tumor cell migration, whereas overexpression of full-length Tak1 had no significant effect. A combined score of low Cyld and high Tak1 expression was an independent prognostic marker in a multivariate Cox regression model

Additional file 1 of Magnetique: an interactive web application to explore transcriptome signatures of heart failure

Supplementary Material

Transforming Growth Factor-β Activated Kinase 1 (Tak1) Is Activated in Hepatocellular Carcinoma, Mediates Tumor Progression, and Predicts Unfavorable Outcome

Although knowledge on inflammatory signaling pathways driving cancer initiation and progression has been increasing, molecular mechanisms in hepatocarcinogenesis are still far from being completely understood. Hepatocyte-specific deletion of the MAPKKK Tak1 in mice recapitulates important steps of hepatocellular carcinoma (HCC) development, including the occurrence of cell death, steatohepatitis, dysplastic nodules, and HCCs. However, overactivation of Tak1 in mice upon deletion of its deubiquitinase Cyld also results in steatohepatitis and HCC development. To investigate Tak1 and Cyld in human HCCs, we created a tissue microarray to analyze their expression by immunohistochemistry in a large and well-characterized cohort of 871 HCCs of 561 patients. In the human liver and HCC, Tak1 is predominantly present as its isoform Tak1A and predominantly localizes to cell nuclei. Tak1 is upregulated in diethylnitrosamine-induced mouse HCCs as well as in human HCCs independent of etiology and is further induced in distant metastases. A high nuclear Tak1 expression is associated with short survival and vascular invasion. When we overexpressed Tak1A in Huh7 cells, we observed increased tumor cell migration, whereas overexpression of full-length Tak1 had no significant effect. A combined score of low Cyld and high Tak1 expression was an independent prognostic marker in a multivariate Cox regression model