TCGAbiolinksGUI: A graphical user interface to analyze GDC cancer molecular and clinical data

Silva, Tiago C.; Colaprico, Antonio; Olsen, Catharina; Bontempi, Gianluca; Ceccarelli, Michele; Berman, Benjamin P.; Noushmehr, Houtan

doi:10.1101/147496

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…To enable user access to the methodologies offered in ELMER and to give users the flexibility of point-and-click style analysis without the need to learn R, we have implemented a full graphical user interface (GUI) through the R/Bioconductor package TCGAbiolinksGUI (Silva et al, 2017) available at http://bioconductor.org/packages/TCGAbiolinksGUI/. This tool allows definition of sample groupings based on user-defined clinical attributes in the supported databanks, including TCGA and TARGET, and GDC.…”

Section: Graphical User Interfacementioning

confidence: 99%

ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Silva

Coetzee

Yao

et al. 2017

Preprint

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Recent studies indicate that DNA methylation can be used to identify changes at transcriptional enhancers and other cis-regulatory elements in primary human samples. A systematic approach to inferring gene regulatory networks has been provided by the R/Bioconductor package ELMER (Enhancer Linking by Methylation/Expression Relationships), which first identifies DNA methylation changes in distal regulatory elements and correlates these with the expression of nearby genes to identify direct transcriptional targets. Next, ELMER performs a transcription factor binding motif analysis and integrates with expression profiling of all human transcription factors, to identify master regulatory TFs and place each differentially methylated regulatory element into the context of an altered gene regulatory network (GRN).Here we present a completely updated version of the package (ELMER v. 2.0), which uses the latest Bioconductor data structures including the popular MultiAsssayExperiment, supports multiple reference genome assemblies as well as the DNA methylation platforms Infinium MethylationEPIC and Infinium HumanMethylation450, and provides a "Supervised" analysis mode for paired sample study designs (such as treated vs. untreated replicate samples). It also supports data import from the new NCI Genomic Data Commons (GDC) database. The new version is substantially re-written, improving stability, performance, and extensibility. It also uses improved databases for transcription factor binding domain families and binding motif specificities, and has newly designed output plots for publication-quality figures.Below, we describe the methods and new features of ELMER v. 2.0 and present two use case demonstrating how the tool can be used to analyze TCGA data in either Unsupervised or Supervised mode. ELMER (v2.0.0) is available as an R/Bioconductor package at

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Section: Graphical User Interfacementioning

confidence: 99%

ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Silva

Coetzee

Yao

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Malta

Sokolov

Gentles

et al. 2018

Cell

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Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

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TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data

et al. 2018

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The GDC (Genomic Data Commons) data portal provides users with data from cancer genomics studies. Recently, we developed the R/Bioconductor package, which allows users to search, download and prepare TCGAbiolinks cancer genomics data for integrative data analysis. The use of this package requires users to have advanced knowledge of R thus limiting the number of users. To overcome this obstacle and improve the accessibility of the package by a wider range of users, we developed a graphical user interface (GUI) using Shiny available through the package The TCGAbiolinksGUI. TCGAbiolinksGUI package is freely available within the Bioconductor project at http://bioconductor.org/packages/TCGAbiolinksGUI/. Links to the GitHub repository, a demo version of the tool, a docker image and PDF/video tutorials are available from the site. TCGAbiolinksGUI

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TCGAbiolinksGUI: A graphical user interface to analyze GDC cancer molecular and clinical data

Cited by 3 publications

References 13 publications

ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

TCGAbiolinksGUI: A graphical user interface to analyze cancer molecular and clinical data

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