Summary: bollito is an automated, flexible and parallelizable computational pipeline for the comprehensive analysis of single-cell RNA-seq data. Starting from FASTQ files or preprocessed expression matrices, bollito performs both basic and advanced tasks in single-cell analysis integrating >30 state-of-the-art tools. This includes quality control, read alignment, dimensionality reduction, clustering, cell-marker detection, differential expression, functional analysis, trajectory inference and RNA velocity. bollito is built using the Snakemake workflow management system, which easily connects each execution step and facilitates the reproducibility of results. bollito's modular design makes it easy to incorporate other packages into the pipeline enabling its expansion with new functionalities.
Availability And Implementation: Source code is freely available at https://gitlab.com/bu_cnio/bollito under the MIT license.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab758 | DOI Listing |
Environ Microbiome
January 2025
Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
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January 2025
Department of Software Engineering, Faculty of Engineering, Muğla Sıtkı Koçman University, Muğla, 4800, Turkey.
Objectives: Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.
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Microb Genom
January 2025
GMT Science 75 route de Lyons-La-Foret, Rouen F-76000, France.
Microbiome profiling tools rely on reference catalogues, which significantly affect their performance. Comparing them is, however, challenging, mainly due to differences in their native catalogues. In this study, we present a novel standardized benchmarking framework that makes such comparisons more accurate.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Artificial Intelligence, Jilin University, Jilin, China.
Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.
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Pharmacoeconomics
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Belgian Health Care Knowledge Centre, Brussels, Belgium.
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