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| Home > Publications database > MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. |
| Home > Publications database > MosaiCatcher v2: a single-cell structural variations detection and analysis reference framework based on Strand-seq. |
| Journal Article | DKFZ-2023-02111 |
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2023
Oxford Univ. Press
Oxford
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Please use a persistent id in citations: doi:10.1093/bioinformatics/btad633
Abstract: Single-cell DNA template strand sequencing (Strand-seq) allows a range of various genomic analysis including chromosome length haplotype phasing and structural variation (SV) calling in individual cells. Here, we present MosaiCatcher v2, a standardised workflow and reference framework for single-cell SV detection using Strand-seq. This framework introduces a range of functionalities, including: an automated upstream Quality Control (QC) and assembly sub-workflow that relies on multiple genome assemblies and incorporates a multistep normalisation module, integration of the scNOVA SV functional characterization and of the ArbiGent SV genotyping modules, platform portability, as well as a user-friendly and shareable web report. These new features of MosaiCatcher v2 enable reproducible computational processing of Strand-seq data, which are increasingly used in human genetics and single cell genomics, towards production environments. MosaiCatcher v2 is compatible with both container and conda environments, ensuring reproducibility and robustness and positioning the framework as a cornerstone in computational processing of Strand-seq data.MosaiCatcher v2 is a standardised workflow, implemented using the Snakemake workflow management system. The pipeline is available on GitHub: https://github.com/friendsofstrandseq/mosaicatcher-pipeline/ and on the snakemake-workflow-catalog: https://snakemake.github.io/snakemake-workflow-catalog/?usage=friendsofstrandseq/mosaicatcher-pipeline. Strand-seq example input data used in the publication can be found in the Data availability statement. Additionally, a lightweight dataset for test purposes can be found on the GitHub repository.Supplementary data are available at Bioinformatics online.
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