The START App: a web-based RNAseq analysis and visualization resource

Nelson, Jonathan W.; Sklenar, Jiri; Barnes, Anthony P.; Minnier, Jessica

doi:10.1093/bioinformatics/btw624

Cited by 92 publications

(80 citation statements)

References 9 publications

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“…Differentially expressed genes (uncorrected p<0.01) were subjected to Ingenuity Pathway Analysis (IPA) to determine significant gene clustering (p<0.05) within signaling pathways or other disease annotations (Ingenuity Systems, http://www.ingneuity.com). Volcano plots were generated using the Shiny Transcriptome Analysis Resource Tool (START) 54 . Heat maps were generated using R software (version 3.4.2)…”

Section: Sequence Alignment and Gene Countsmentioning

confidence: 99%

A precision medicine approach uncovers a unique signature of neutrophils in patients with brushite kidney stones

Makki

Winfree

Lingeman

et al. 2019

Preprint

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Kidney stone disease is very common and causes significant morbidity. The main goal of this study was to uncover stone-type specific molecular signatures using a precision medicine approach that combines unbiased transcriptomics, proteomics and innovative 3D imaging strategies. We found that increased neutrophil infiltration and NETosis are unrecognized new factors specifically associated with brushite stone formation. These discoveries underscore the unequivocal presence of inflammation in human stone disease, and the divergence of cellular and molecular pathways in distinct forms of nephrolithiasis. Abstract Background:We have previously found that papillary histopathology differs greatly between calcium oxalate and brushite stone formers (SF); the latter have much more papillary mineral deposition, tubular cell injury and tissue fibrosis. Methods:In this study, we applied unbiased orthogonal "omics" approaches on biopsied renal papillae and extracted stones from patients with brushite or calcium oxalate (CaOx) stones. Our goal was to discover stone type-specific molecular signatures to advance our understanding of the underlying pathogenesis.Results: Brushite SF did not differ from CaOx SF with respect to metabolic risk factors for stones, but did exhibit increased tubule plugging in their papillae. Brushite SF had upregulation of inflammatory pathways in papillary tissue, and increased neutrophil markers in stone matrix compared to those with CaOx stones. Large-scale 3D tissue cytometry on renal papillary biopsies showed an increase in the number and density of neutrophils in the papillae of brushite vs. CaOx patients, thereby linking the observed inflammatory signatures to the neutrophils in the tissue. To explain how neutrophil proteins appear in the stone matrix, we measured neutrophil extracellular trap (NET) formation, NETosis, and found it significantly increased in the papillae of brushite compared to CaOx patients. Conclusions:We show that increased neutrophil infiltration and NETosis is an unrecognized factor that differentiates brushite and CaOx SF, and may explain the markedly increased scarring and inflammation seen in the papillae of brushite patients. Given the increasing prevalence of brushite stones, the role of neutrophil activation in brushite stone formation requires further study.

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Section: Sequence Alignment and Gene Countsmentioning

confidence: 99%

A precision medicine approach uncovers a unique signature of neutrophils in patients with brushite kidney stones

Makki

Winfree

Lingeman

et al. 2019

Preprint

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“…A growing number of software packages (Younesy et al, 2015;Nelson et al, 2016;Su et al, 2017;Harshbarger et al, 2017;Gardeux et al, 2017;Lim et al, 2017;Li and Andrade, 2017;Zhu et al, 2018;Ge et al, 2018;Monier et al, 2018;McDermaid et al, 2018;Schultheis et al, 2018;Kucukural et al, 2019;Choi and Ratner, 2019;Price et al, 2019;Tintori et al, 2020;Su et al, 2019) have been developed to operate on tabular-like summarized expression data, or on formats which might derive from their results (see Additional file 1: Table S1 for a comprehensive list of details on their functionality and characteristics).…”

Section: Introductionmentioning

confidence: 99%

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

Marini

Linke

Binder

2020

Preprint

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Background: RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.Results: We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.Conclusion: ideal is distributed as an R package in the Bioconductor project (http: //bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

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“…Implementations where PCA results can be explored are available, mostly focused on small sample datasets, such as Fisher's iris (Fisher, 1984) (https://gist.github.com/dgrapov/5846650 or https://github.com/dgrapov/DeviumWeb, https://github.com/benmarwick/Interactive_ PCA_Explorer) and have been developed rather for generic data, without considering the aspects typical of transcriptomic data (http://langtest.jp/shiny/pca/, (Vaissie et al, 2017)). In the field of genomics, some tools are already available for performing such operations (Sharov et al, 2005;la Grange et al, 2009;Metsalu and Vilo, 2015;Khomtchouk et al, 2016;Manimaran et al, 2016;Nelson et al, 2016;Schultheis et al, 2018), yet none of them feature an interactive analysis, fully integrated in Bioconductor, while also providing the basis for generating a reproducible analysis (Peng, 2011;McNutt, 2014). Alternatively, more general software suites are also available (e.g.…”

Section: Introductionmentioning

confidence: 99%

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

Marini

Binder

2018

Preprint

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Background: Principal component analysis (PCA) is frequently used in genomics applications for quality assessment and exploratory analysis in high-dimensional data, such as RNA sequencing (RNA-seq) gene expression assays. Despite the availability of many software packages developed for this purpose, an interactive and comprehensive interface for performing these operations is lacking.Results: We developed the pcaExplorer software package to enhance commonly performed analysis steps with an interactive and user-friendly application, which provides state saving as well as the automated creation of reproducible reports. pcaExplorer is implemented in R using the Shiny framework and exploits data structures from the opensource Bioconductor project. Users can easily generate a wide variety of publication-ready graphs, while assessing the expression data in the different modules available, including a general overview, dimension reduction on samples and genes, as well as functional interpretation of the principal components.Conclusion: pcaExplorer is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/pcaExplorer/), and is designed to assist a broad range of researchers in the critical step of interactive data exploration.

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The START App: a web-based RNAseq analysis and visualization resource

Abstract: minnier@ohsu.edu

Cited by 92 publications

References 9 publications

A precision medicine approach uncovers a unique signature of neutrophils in patients with brushite kidney stones

A precision medicine approach uncovers a unique signature of neutrophils in patients with brushite kidney stones

ideal: an R/Bioconductor package for Interactive Differential Expression Analysis

pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components

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