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Puente-Santamaría L, del Peso L. SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis software. PLoS One 2024; 19:e0300567. [PMID: 38889133 PMCID: PMC11185446 DOI: 10.1371/journal.pone.0300567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 02/29/2024] [Indexed: 06/20/2024] Open
Abstract
Single-cell transcriptomics techniques, such as scRNA-seq, attempt to characterize gene expression profiles in each cell of a heterogeneous sample individually. Due to growing amounts of data generated and the increasing complexity of the computational protocols needed to process the resulting datasets, the demand for dedicated training in mathematical and programming skills may preclude the use of these powerful techniques by many teams. In order to help close that gap between wet-lab and dry-lab capabilities we have developed SinglePointRNA, a shiny-based R application that provides a graphic interface for different publicly available tools to analyze single cell RNA-seq data. The aim of SinglePointRNA is to provide an accessible and transparent tool set to researchers that allows them to perform detailed and custom analysis of their data autonomously. SinglePointRNA is structured in a context-driven framework that prioritizes providing the user with solid qualitative guidance at each step of the analysis process and interpretation of the results. Additionally, the rich user guides accompanying the software are intended to serve as a point of entry for users to learn more about computational techniques applied to single cell data analysis. The SinglePointRNA app, as well as case datasets for the different tutorials are available at www.github.com/ScienceParkMadrid/SinglePointRNA.
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Affiliation(s)
- Laura Puente-Santamaría
- Departamento de Bioquímica, Facultad de Medicina, Universidad Autónoma de Madrid (UAM), Madrid, Spain
- Genomics Unit Cantoblanco, Fundación Parque Científico de Madrid, Madrid, Spain
| | - Luis del Peso
- Departamento de Bioquímica, Facultad de Medicina, Universidad Autónoma de Madrid (UAM), Madrid, Spain
- IdiPaz, Instituto de Investigación Sanitaria del Hospital Universitario La Paz, Madrid, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain
- Unidad Asociada de Biomedicina CSIC-UCLM, Albacete, Spain
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Sonawane AR, Pucéat M, Jo H. Editorial: Single-cell OMICs analyses in cardiovascular diseases. Front Cardiovasc Med 2024; 11:1413184. [PMID: 38770014 PMCID: PMC11102967 DOI: 10.3389/fcvm.2024.1413184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 04/23/2024] [Indexed: 05/22/2024] Open
Affiliation(s)
- Abhijeet Rajendra Sonawane
- Center for Interdisciplinary Cardiovascular Sciences and Center for Excellences in Vascular Biology, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Michel Pucéat
- INSERM, Cardiovascular and Nutrition Center (C2VN), Aix-Marseille University, Marseille, France
| | - Hanjoong Jo
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States
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Waichman TV, Vercesi ML, Berardino AA, Beckel MS, Giacomini D, Rasetto NB, Herrero M, Di Bella DJ, Arlotta P, Schinder AF, Chernomoretz A. scX: a user-friendly tool for scRNAseq exploration. BIOINFORMATICS ADVANCES 2024; 4:vbae062. [PMID: 38779177 PMCID: PMC11109472 DOI: 10.1093/bioadv/vbae062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/06/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Motivation Single-cell RNA sequencing (scRNAseq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. Results In this article, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and SingleCellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis. Availability and implementation Source code can be downloaded from https://github.com/chernolabs/scX. A docker image is available from dockerhub as chernolabs/scx.
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Affiliation(s)
- Tomás V Waichman
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - M L Vercesi
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Ariel A Berardino
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
| | - Maximiliano S Beckel
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
| | - Damiana Giacomini
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Natalí B Rasetto
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Magalí Herrero
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Daniela J Di Bella
- Department of Stem Cells and Regenerative Biology, Harvard University, Cambridge, MA 02138, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02138, United States
| | - Paola Arlotta
- Department of Stem Cells and Regenerative Biology, Harvard University, Cambridge, MA 02138, United States
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02138, United States
| | - Alejandro F Schinder
- Instituto de Investigaciones Bioquímicas de Buenos Aires, CONICET, Buenos Aires, CP1405, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, CP1405, Argentina
| | - Ariel Chernomoretz
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, CP1405, Argentina
- Departamento de Física, FCEN, Universidad de Buenos Aires, Buenos Aires, CP1428, Argentina
- INFINA, UBA-CONICET, Buenos Aires, CP 1428, Argentina
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Wang L, Dou X, Xie L, Zhou X, Liu Y, Liu J, Liu X. Metabolic Landscape of Osteosarcoma: Reprogramming of Lactic Acid Metabolism and Metabolic Communication. FRONT BIOSCI-LANDMRK 2024; 29:83. [PMID: 38420794 DOI: 10.31083/j.fbl2902083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/30/2023] [Accepted: 10/12/2023] [Indexed: 03/02/2024]
Abstract
BACKGROUND Lactic acid, previously regarded only as an endpoint of glycolysis, has emerged as a major regulator of tumor invasion, growth, and the tumor microenvironment. In this study, we aimed to explore the reprogramming of lactic acid metabolism relevant to osteosarcoma (OS) microenvironment by decoding the underlying lactic acid metabolic landscape of OS cells and intercellular signaling alterations. METHODS The landscape of OS metabolism was evaluated using single-cell gene expression data, lactic acid metabolism clustering, and screening of the hub genes in lactic acid metabolism of OS samples using transcriptome data. The role of the hub gene NADH:Ubiquinone Oxidoreductase Complex Assembly Factor 6 (NDUFAF6) was validated with in vitro studies and patient experiments. RESULTS Single-cell RNA sequencing data validated a lactic acid metabolismhigh subcluster in OS. Further investigation of intercellular communications revealed a unique metabolic communication pattern between the lactic acid metabolismhigh subcluster and other subclusters. Next, two lactic acid metabolic reprogramming phenotypes were defined, and six lactic acid metabolism-related genes (LRGs), including the biomarker NDUFAF6, were screened in OS. In vitro studies and patient experiments confirmed that NDUFAF6 is a crucial lactic acid metabolism-associated gene in OS. CONCLUSIONS The patterns of lactic acid metabolism in OS suggested metabolic reprogramming phenotypes relevant to the tumor microenvironment (TME) and identified NDUFAF6 as an LRG prognostic biomarker.
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Affiliation(s)
- Linbang Wang
- Department of Orthopedics, Peking University Third Hospital, 100191 Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, 100191 Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, 100191 Beijing, China
| | - Xinyu Dou
- Department of Orthopedics, Peking University Third Hospital, 100191 Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, 100191 Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, 100191 Beijing, China
| | - Linzhen Xie
- Peking University Fourth School of Clinical Medicine, 100035 Beijing, China
| | - Xuchang Zhou
- School of Sport Medicine and Rehabilitation, Beijing Sport University, 100084 Beijing, China
| | - Yu Liu
- Department of Orthopedics, Peking University Third Hospital, 100191 Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, 100191 Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, 100191 Beijing, China
| | - Jingkun Liu
- Department of Orthopedics, Honghui Hospital, Xi'an Jiaotong University, 710054 Xi'an, Shaanxi, China
| | - Xiaoguang Liu
- Department of Orthopedics, Peking University Third Hospital, 100191 Beijing, China
- Beijing Key Laboratory of Spinal Disease Research, 100191 Beijing, China
- Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, 100191 Beijing, China
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Farhat B, Bordeu I, Jagla B, Ibrahim S, Stefanovic S, Blanc H, Loulier K, Simons BD, Beaurepaire E, Livet J, Pucéat M. Understanding the cell fate and behavior of progenitors at the origin of the mouse cardiac mitral valve. Dev Cell 2024; 59:339-350.e4. [PMID: 38198889 DOI: 10.1016/j.devcel.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/08/2023] [Accepted: 12/08/2023] [Indexed: 01/12/2024]
Abstract
Congenital heart malformations include mitral valve defects, which remain largely unexplained. During embryogenesis, a restricted population of endocardial cells within the atrioventricular canal undergoes an endothelial-to-mesenchymal transition to give rise to mitral valvular cells. However, the identity and fate decisions of these progenitors as well as the behavior and distribution of their derivatives in valve leaflets remain unknown. We used single-cell RNA sequencing (scRNA-seq) of genetically labeled endocardial cells and microdissected mouse embryonic and postnatal mitral valves to characterize the developmental road. We defined the metabolic processes underlying the specification of the progenitors and their contributions to subtypes of valvular cells. Using retrospective multicolor clonal analysis, we describe specific modes of growth and behavior of endocardial cell-derived clones, which build up, in a proper manner, functional valve leaflets. Our data identify how both genetic and metabolic mechanisms specifically drive the fate of a subset of endocardial cells toward their distinct clonal contribution to the formation of the valve.
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Affiliation(s)
- Batoul Farhat
- INSERM U1251/Aix-Marseille Université, Marseille 13885, France
| | - Ignacio Bordeu
- Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK; Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, UK; Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago 9160000, Chile
| | - Bernd Jagla
- Pasteur Institute UtechS CB & Hub de Bioinformatique et Biostatistiques, C3BI, Paris, France
| | - Stéphanie Ibrahim
- C2VN Aix-Marseille Université, INSERM 1263, INRAE 1260, Marseille 13885, France
| | - Sonia Stefanovic
- C2VN Aix-Marseille Université, INSERM 1263, INRAE 1260, Marseille 13885, France
| | - Hugo Blanc
- Laboratory for Optics and Biosciences, Ecole Polytechnique, CNRS, INSERM, IP Paris, Palaiseau 91120, France
| | - Karine Loulier
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris 75012, France
| | - Benjamin D Simons
- Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK; Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, UK; Wellcome Trust-Medical Research Council Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge CB2 A0W, UK
| | - Emmanuel Beaurepaire
- Laboratory for Optics and Biosciences, Ecole Polytechnique, CNRS, INSERM, IP Paris, Palaiseau 91120, France
| | - Jean Livet
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris 75012, France
| | - Michel Pucéat
- INSERM U1251/Aix-Marseille Université, Marseille 13885, France.
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Nassiri I, Fairfax B, Lee A, Wu Y, Buck D, Piazza P. scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data. BMC Genomics 2023; 24:381. [PMID: 37415108 DOI: 10.1186/s12864-023-09447-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 06/13/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise. RESULTS We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function. CONCLUSIONS The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website.
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Affiliation(s)
- Isar Nassiri
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
| | - Benjamin Fairfax
- MRC-Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Department of Oncology, University of Oxford & Oxford Cancer Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Angela Lee
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Yanxia Wu
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - David Buck
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Paolo Piazza
- Oxford Genomics Centre, Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.
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Patil AR, Kumar G, Zhou H, Warren L. scViewer: An Interactive Single-Cell Gene Expression Visualization Tool. Cells 2023; 12:1489. [PMID: 37296611 PMCID: PMC10253102 DOI: 10.3390/cells12111489] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/09/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.
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Affiliation(s)
- Abhijeet R. Patil
- Global Statistical and Data Sciences, Teva Pharmaceuticals, West Chester, PA 19380, USA
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Dedden M, Wiendl M, Müller TM, Neurath MF, Zundler S. Manual cell selection in single cell transcriptomics using scSELpy supports the analysis of immune cell subsets. Front Immunol 2023; 14:1027346. [PMID: 37180117 PMCID: PMC10166880 DOI: 10.3389/fimmu.2023.1027346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 04/07/2023] [Indexed: 05/15/2023] Open
Abstract
Introduction Single cell RNA sequencing plays an increasing and indispensable role in immunological research such as in the field of inflammatory bowel diseases (IBD). Professional pipelines are complex, but tools for the manual selection and further downstream analysis of single cell populations are missing so far. Methods We developed a tool called scSELpy, which can easily be integrated into Scanpy-based pipelines, allowing the manual selection of cells on single cell transcriptomic datasets by drawing polygons on various data representations. The tool further supports the downstream analysis of the selected cells and the plotting of results. Results Taking advantage of two previously published single cell RNA sequencing datasets we show that this tool is useful for the positive and negative selection of T cell subsets implicated in IBD beyond standard clustering. We further demonstrate the feasibility for subphenotyping T cell subsets and use scSELpy to corroborate earlier conclusions drawn from the dataset. Moreover, we also show its usefulness in the context of T cell receptor sequencing. Discussion Collectively, scSELpy is a promising additive tool fulfilling a so far unmet need in the field of single cell transcriptomic analysis that might support future immunological research.
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Affiliation(s)
- Mark Dedden
- Department of Medicine 1, University Hospital Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Maximilian Wiendl
- Department of Medicine 1, University Hospital Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Tanja M. Müller
- Department of Medicine 1, University Hospital Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), University Hospital Erlangen, Erlangen, Germany
| | - Markus F. Neurath
- Department of Medicine 1, University Hospital Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), University Hospital Erlangen, Erlangen, Germany
| | - Sebastian Zundler
- Department of Medicine 1, University Hospital Erlangen and Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Deutsches Zentrum Immuntherapie (DZI), University Hospital Erlangen, Erlangen, Germany
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Weber C, Hirst MB, Ernest B, Schaub NJ, Wilson KM, Wang K, Baskir HM, Chu PH, Tristan CA, Singeç I. SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data. CELL REPORTS METHODS 2023; 3:100420. [PMID: 37056373 PMCID: PMC10088091 DOI: 10.1016/j.crmeth.2023.100420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/23/2022] [Accepted: 02/10/2023] [Indexed: 03/08/2023]
Abstract
SEQUIN is a web-based application (app) that allows fast and intuitive analysis of RNA sequencing data derived for model organisms, tissues, and single cells. Integrated app functions enable uploading datasets, quality control, gene set enrichment, data visualization, and differential gene expression analysis. We also developed the iPSC Profiler, a practical gene module scoring tool that helps measure and compare pluripotent and differentiated cell types. Benchmarking to other commercial and non-commercial products underscored several advantages of SEQUIN. Freely available to the public, SEQUIN empowers scientists using interdisciplinary methods to investigate and present transcriptome data firsthand with state-of-the-art statistical methods. Hence, SEQUIN helps democratize and increase the throughput of interrogating biological questions using next-generation sequencing data with single-cell resolution.
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Affiliation(s)
- Claire Weber
- National Center for Advancing Translational Sciences (NCATS), Division of Preclinical Innovation, Stem Cell Translation Laboratory (SCTL), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Marissa B. Hirst
- Rancho Biosciences, 16955 Via Del Campo, #200, San Diego, CA 92127, USA
| | - Ben Ernest
- Rancho Biosciences, 16955 Via Del Campo, #200, San Diego, CA 92127, USA
| | - Nicholas J. Schaub
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Kelli M. Wilson
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ke Wang
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Hannah M. Baskir
- National Center for Advancing Translational Sciences (NCATS), Division of Preclinical Innovation, Stem Cell Translation Laboratory (SCTL), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Pei-Hsuan Chu
- National Center for Advancing Translational Sciences (NCATS), Division of Preclinical Innovation, Stem Cell Translation Laboratory (SCTL), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Carlos A. Tristan
- National Center for Advancing Translational Sciences (NCATS), Division of Preclinical Innovation, Stem Cell Translation Laboratory (SCTL), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ilyas Singeç
- National Center for Advancing Translational Sciences (NCATS), Division of Preclinical Innovation, Stem Cell Translation Laboratory (SCTL), National Institutes of Health (NIH), 9800 Medical Center Drive, Rockville, MD 20850, USA
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