Proks Martin, Alejandro Romero Herrera Jose, Sedzinski Jakub, Brickman Joshua M
Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Department of Biomedical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.
Center for Health Data Science, University of Copenhagen, Copenhagen, 2200, Denmark.
Bioinform Adv. 2025 May 23;5(1):vbaf089. doi: 10.1093/bioadv/vbaf089. eCollection 2025.
Single sequencing technology (scRNA-seq) enables the study of gene regulation at a single cell level. Although many sc-RNA-seq protocols have been established, they have varied in technical complexity, sequencing depth and multimodal capabilities leading to shared limitations in data interpretation due to a lack of standardized preprocessing and consistent data reproducibility. While plate based techniques such as Massively Parallel RNA Single cell Sequencing (MARS-seq2.0) provide reference data on the cells that will be sequenced, the data format limits the possible analysis. Here, we focus on the standardization of MARS-seq analysis and its applicability to RNA velocity.
We have taken the original MARS-seq2.0 pipeline and revised it to enable implementation using the nf-core framework. By doing so, we have simplified pipeline execution, enabling a streamlined application with increased transparency and scalability. We have incorporated additional checkpoints to verify experimental metadata and improved the pipeline by implementing a custom workflow for RNA velocity estimation. The pipeline is part of the nf-core bioinformatics community and is freely available at https://github.com/nfcore/marsseq with data analysis at https://github.com/brickmanlab/proks-et-al-2023.
We introduce an updated preprocessing pipeline for MARS-seq experiments following state-of-the-art guidelines for scientific software development with the added ability to infer RNA velocity.
单测序技术（scRNA-seq）能够在单细胞水平上研究基因调控。尽管已经建立了许多sc-RNA-seq方案，但它们在技术复杂性、测序深度和多模态能力方面存在差异，由于缺乏标准化的预处理和一致的数据可重复性，导致在数据解释方面存在共同的局限性。虽然基于平板的技术，如大规模平行RNA单细胞测序（MARS-seq2.0）提供了关于将要测序细胞的参考数据，但数据格式限制了可能的分析。在这里，我们专注于MARS-seq分析的标准化及其在RNA速度分析中的适用性。
我们采用了原始的MARS-seq2.0流程，并对其进行了修订，以使其能够使用nf-core框架实施。通过这样做，我们简化了流程执行，实现了一个更简化的应用程序，提高了透明度和可扩展性。我们纳入了额外的检查点以验证实验元数据，并通过实施用于RNA速度估计的自定义工作流程改进了该流程。该流程是nf-core生物信息学社区的一部分，可在https://github.com/nfcore/marsseq免费获取，数据分析可在https://github.com/brickmanlab/proks-et-al-2023获取。
我们根据科学软件开发的最新指南，为MARS-seq实验引入了一个更新的预处理流程，并增加了推断RNA速度的能力。
