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@article{Delacher2020,
abstract = {Specialized regulatory T (Treg) cells accumulate and perform homeostatic and regenerative functions in nonlymphoid tissues. Whether common precursors for nonlymphoid-tissue Treg cells exist and how they differentiate remain elusive. Using transcription factor nuclear factor, interleukin 3 regulated (Nfil3) reporter mice and single-cell RNA-sequencing (scRNA-seq), we identified two precursor stages of interleukin 33 (IL-33) receptor ST2-expressing nonlymphoid tissue Treg cells, which resided in the spleen and lymph nodes. Global chromatin profiling of nonlymphoid tissue Treg cells and the two precursor stages revealed a stepwise acquisition of chromatin accessibility and reprogramming toward the nonlymphoid-tissue Treg cell phenotype. Mechanistically, we identified and validated the transcription factor Batf as the driver of the molecular tissue program in the precursors. Understanding this tissue development program will help to harness regenerative properties of tissue Treg cells for therapy.},
author = {Delacher, Michael and Imbusch, Charles D. and Hotz-Wagenblatt, Agnes and Mallm, Jan Philipp and Bauer, Katharina and Simon, Malte and Riegel, Dania and Rendeiro, Andr{\'{e}} F. and Bittner, Sebastian and Sanderink, Lieke and Pant, Asmita and Schmidleithner, Lisa and Braband, Kathrin L. and Echtenachter, Bernd and Fischer, Alexander and Giunchiglia, Valentina and Hoffmann, Petra and Edinger, Matthias and Bock, Christoph and Rehli, Michael and Brors, Benedikt and Schmidl, Christian and Feuerer, Markus},
doi = {10.1016/j.immuni.2019.12.002},
issn = {10974180},
journal = {Immunity},
keywords = {ATAC-seq,Areg,Batf,Foxp3,Gata3,Nfil3,precursor,scRNA-seq,scTCR-seq,tissue Treg},
number = {2},
pages = {295--312.e11},
pmid = {31924477},
title = {{Precursors for Nonlymphoid-Tissue Treg Cells Reside in Secondary Lymphoid Organs and Are Programmed by the Transcription Factor BATF}},
volume = {52},
year = {2020}
}
@article{Ludt2022,
abstract = {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. {\textcopyright} 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer. Basic Protocol 2: Differential Expression Analysis with ideal. Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic. Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic. Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.},
author = {Ludt, Annekathrin and Ustjanzew, Arsenij and Binder, Harald and Strauch, Konstantin and Marini, Federico},
doi = {10.1002/cpz1.411},
issn = {26911299},
journal = {Current Protocols},
keywords = {RNA-seq,data visualization,functional interpretation,interactive data analysis,reproducible research},
number = {4},
pages = {1--55},
pmid = {35467799},
title = {{Interactive and Reproducible Workflows for Exploring and Modeling RNA-seq Data with pcaExplorer, Ideal, and GeneTonic}},
volume = {2},
year = {2022}
}
@article{Marini2019,
abstract = {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 open-source 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.},
author = {Marini, Federico and Binder, Harald},
doi = {10.1186/s12859-019-2879-1},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Bioconductor,Exploratory data analysis,Principal component analysis,R,RNA-Seq,Reproducible research,Shiny,User-friendly},
month = {dec},
number = {1},
pages = {331},
publisher = {BMC Bioinformatics},
title = {{pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components}},
url = {https://www.biorxiv.org/content/early/2018/12/12/493551 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2879-1},
volume = {20},
year = {2019}
}
@Article{Love2014,
title = {Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
author = {Michael I. Love and Wolfgang Huber and Simon Anders},
year = {2014},
journal = {Genome Biology},
doi = {10.1186/s13059-014-0550-8},
volume = {15},
issue = {12},
pages = {550},
}
@article{Marini2020,
abstract = {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 work-flow 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.},
author = {Marini, Federico and Linke, Jan and Binder, Harald},
doi = {10.1186/s12859-020-03819-5},
isbn = {1285902003819},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Bioconductor,Data visualization,Differential expression,Interactive data analysis,R,RNA-Seq,Reproducible research,Shiny,Transcriptomics,Web application},
month = {dec},
number = {1},
pages = {565},
publisher = {BioMed Central},
title = {{ideal: an R/Bioconductor package for interactive differential expression analysis}},
url = {https://doi.org/10.1186/s12859-020-03819-5 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03819-5},
volume = {21},
year = {2020}
}
@Manual{topGO,
title = {topGO: Enrichment Analysis for Gene Ontology},
author = {Adrian Alexa and Jorg Rahnenfuhrer},
year = {2024},
note = {R package version 2.56.0},
}
@article{Sakaguchi2008,
abstract = {Regulatory T cells (Tregs) play an indispensable role in maintaining immunological unresponsiveness to self-antigens and in suppressing excessive immune responses deleterious to the host. Tregs are produced in the thymus as a functionally mature subpopulation of T cells and can also be induced from naive T cells in the periphery. Recent research reveals the cellular and molecular basis of Treg development and function and implicates dysregulation of Tregs in immunological disease. {\textcopyright} 2008 Elsevier Inc. All rights reserved.},
author = {Sakaguchi, Shimon and Yamaguchi, Tomoyuki and Nomura, Takashi and Ono, Masahiro},
doi = {10.1016/j.cell.2008.05.009},
issn = {00928674},
journal = {Cell},
number = {5},
pages = {775--787},
pmid = {18510923},
title = {{Regulatory T Cells and Immune Tolerance}},
volume = {133},
year = {2008}
}