Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, United States of America.
Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.
PLoS Comput Biol. 2020 Mar 9;16(3):e1007741. doi: 10.1371/journal.pcbi.1007741. eCollection 2020 Mar.
We present ProteoClade, a Python toolkit that performs taxa-specific peptide assignment, protein inference, and quantitation for multi-species proteomics experiments. ProteoClade scales to hundreds of millions of protein sequences, requires minimal computational resources, and is open source, multi-platform, and accessible to non-programmers. We demonstrate its utility for processing quantitative proteomic data derived from patient-derived xenografts and its speed and scalability enable a novel de novo proteomic workflow for complex microbiota samples.
我们介绍了 ProteoClade，这是一个 Python 工具包，可针对多物种蛋白质组学实验执行特定于分类群的肽分配、蛋白质推断和定量。ProteoClade 可扩展到数亿个蛋白质序列，所需的计算资源很少，并且是开源的、跨平台的，非程序员也可以使用。我们展示了它在处理源自患者来源的异种移植物的定量蛋白质组学数据方面的实用性，其速度和可扩展性使其能够为复杂的微生物组样本提供新的从头蛋白质组学工作流程。
