Phenonaut：表型空间探索的多组学数据集成。
Phenonaut: multiomics data integration for phenotypic space exploration.
机构信息
Edinburgh Cancer Research, Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, United Kingdom.
GlaxoSmithKline Medicines Research Centre, Stevenage SG1 2NY, United Kingdom.
出版信息
Bioinformatics. 2023 Apr 3;39(4). doi: 10.1093/bioinformatics/btad143.
SUMMARY
Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high-content imaging, proteomics, metabolomics, and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation.
AVAILABILITY AND IMPLEMENTATION
Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/.
摘要
多组学数据的数据集成工作流程在学术界和工业界有多种形式。学术界资源有限的情况下，往往难以达到处理和结合高内涵成像、蛋白质组学、代谢组学和其他组学数据的最佳数据集成实践标准。我们提出了 Phenonaut，这是一个 Python 软件包，旨在满足在应用文献和专有的技术来创建与数据源和结构无关的工作流程时，在迁移、控制、集成和可审计性方面的需求。
可用性和实现
源代码：https://github.com/CarragherLab/phenonaut，文档：https://carragherlab.github.io/phenonaut，PyPI 包：https://pypi.org/project/phenonaut/。
Zotero 插件