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      s CIRCLE—An interactive visual exploration tool for single cell RNA-Seq data

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      NAR Genomics and Bioinformatics
      Oxford University Press

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          Abstract

          sCIRCLE ( single- Cell Interactive Real-time Computer visualization for Low-dimensional Exploration) is a tool for exploratory analysis of single cell RNA-seq (scRNA-seq) data sets, with a focus on bacterial scRNA-seq. The software takes an information design perspective to re-envision visually and interactively exploring low dimensional representations of scRNA-Seq data. Users can project cells in various 3D and 2D spaces and interactively query and paint cells using rich metadata sets reporting on cell cluster, gene function, and gene expression. As a standalone application it contains, among other features, options for dimensionality reduction, navigation and interaction with data in 3d and 2d space, gene filtering, fold change and metacell computation as well as various capabilities for visualization, data import and export.

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          eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses

          Abstract eggNOG is a public database of orthology relationships, gene evolutionary histories and functional annotations. Here, we present version 5.0, featuring a major update of the underlying genome sets, which have been expanded to 4445 representative bacteria and 168 archaea derived from 25 038 genomes, as well as 477 eukaryotic organisms and 2502 viral proteomes that were selected for diversity and filtered by genome quality. In total, 4.4M orthologous groups (OGs) distributed across 379 taxonomic levels were computed together with their associated sequence alignments, phylogenies, HMM models and functional descriptors. Precomputed evolutionary analysis provides fine-grained resolution of duplication/speciation events within each OG. Our benchmarks show that, despite doubling the amount of genomes, the quality of orthology assignments and functional annotations (80% coverage) has persisted without significant changes across this update. Finally, we improved eggNOG online services for fast functional annotation and orthology prediction of custom genomics or metagenomics datasets. All precomputed data are publicly available for downloading or via API queries at http://eggnog.embl.de
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            Single-cell RNA-seq: advances and future challenges

            Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here—each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.
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              Mapping single-cell data to reference atlases by transfer learning

              Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.
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                Author and article information

                Contributors
                Journal
                NAR Genom Bioinform
                NAR Genom Bioinform
                nargab
                NAR Genomics and Bioinformatics
                Oxford University Press
                2631-9268
                September 2024
                17 July 2024
                17 July 2024
                : 6
                : 3
                : lqae084
                Affiliations
                Technical University of Applied Sciences Würzburg-Schweinfurt, Faculty of Design , 97070 Würzburg, Germany
                Helmholtz Institute for RNA-based Infection Research, Helmholtz Centre for Infection Research , 97080 Würzburg, Germany
                Technical University of Applied Sciences Würzburg-Schweinfurt, Faculty of Design , 97070 Würzburg, Germany
                Helmholtz Institute for RNA-based Infection Research, Helmholtz Centre for Infection Research , 97080 Würzburg, Germany
                University of Würzburg, Faculty of Medicine , 97080 Würzburg, Germany
                Department of Biology, University of Toronto Mississauga , Mississauga, ON, L5L 1C6, Canada
                Author notes
                To whom correspondence should be addressed. Tel: +1 905 828 5471; Email: lars.barquist@ 123456helmholtz-hiri.de
                Author information
                https://orcid.org/0000-0003-4732-2667
                Article
                lqae084
                10.1093/nargab/lqae084
                11252841
                39022325
                de682c14-266c-4f51-901a-0b3c4f613a0c
                © The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 09 January 2024
                : 17 June 2024
                : 04 July 2024
                Page count
                Pages: 6
                Funding
                Funded by: Bavarian Ministry of Science and Art, DOI 10.13039/501100021711;
                Funded by: NSERC, DOI 10.13039/501100000038;
                Award ID: RGPIN-2024-04305
                Categories
                AcademicSubjects/SCI00030
                AcademicSubjects/SCI00980
                AcademicSubjects/SCI01060
                AcademicSubjects/SCI01140
                AcademicSubjects/SCI01180
                Computational Methods for Transcriptome Analysis
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