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Unlabelled: Chevreul is an open-source R Bioconductor package and interactive R Shiny app for processing and visualising single-cell RNA sequencing (scRNA-seq) data. Chevreul differs from other scRNA-seq analysis packages in its ease of use, capacity to analyze full-length RNA sequencing data for exon coverage and transcript isoform inference, and support for batch correction. Chevreul enables exploratory analyses of scRNA-seq data using Bioconductor SingleCellExperiment objects (or converted Seurat objects), including batch integration, quality control filtering, read count normalization and transformation, dimensionality reduction, clustering at a range of resolutions, and cluster marker gene identification. Processed data can be visualized in the R Shiny app. Gene or transcript expression can be visualized using PCA, tSNE, UMAP, heatmaps, or violin plots; differential expression can be evaluated with several statistical tests. Chevreul also provides accessible tools for isoform-level analyses and alternative splicing detection. Chevreul empowers researchers without programming experience to analyze full-length scRNA-seq data.
Availability & Implementation: Chevreul is implemented in R, and the R package and integrated Shiny application are freely available at https://github.com/cobriniklab/chevreul with constituent packages hosted on Bioconductor at https://bioconductor.org/packages/chevreulProcess, https://bioconductor.org/packages/chevreulPlot, and https://bioconductor.org/packages/chevreulShiny.
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http://dx.doi.org/10.46471/gigabyte.158 | DOI Listing |
Patterns (N Y)
July 2025
Channing Division of Network Medicine, Mass General Brigham, 181 Longwood Avenue, Boston, MA 02115, USA.
This opinion piece discusses the Bioconductor project for open-source bioinformatics and the engineering concepts underlying its effectiveness to date. Since the inception of Bioconductor in 2002 with 15 software packages devoted to analysis of DNA microarrays, it has grown into an ecosystem of ∼3,000 packages contributed by more than 1,000 developers. Aspects of the history and commitments are reviewed here to contribute to thinking about the design and orchestration of future open-source software projects.
View Article and Find Full Text PDFNucleic Acids Res
September 2025
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, United States.
Tandem repetition is one of the major processes underlying genome evolution and phenotypic diversification. While newly formed tandem repeats are often easy to identify, it is more challenging to detect repeat copies as they diverge over evolutionary timescales. Existing programs for finding tandem repeats return markedly different results, and it is unclear which predictions are more correct and how much room remains for improvement.
View Article and Find Full Text PDFBioinformatics
September 2025
Institute of Medical Data Science, Otto-von-Guericke University Magdeburg, Magdeburg, 39120, Germany.
Summary: Recent advances in single-cell sequencing made it possible to not just analyze a cell's individual expression pattern, but to gain insights into a single cell's genome using the cutting-edge technology single-cell DNA sequencing. Mission Bio is, with the Tapestri platform, one of the few providers of this technology. So far, however, there is only little open-source software available for user-friendly processing and quality analysis of this data type.
View Article and Find Full Text PDFPLoS Comput Biol
August 2025
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.
G-quadruplexes (G4s) are nucleic acid secondary structures with important regulatory functions. Single-nucleotide variants (SNVs), one of the most common forms of genetic variation, can potentially impact the formation of G4 structures if they occur within G4 regions. However, there is currently a lack of software tools specifically designed to assess such effects.
View Article and Find Full Text PDFBioinformatics
August 2025
Department of Statistical Sciences, University of Bologna, Bologna 40126, Italy.
Motivation: Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms.
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