Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, United States.
Dan L Duncan Cancer Center, Baylor College of Medicine, Houston, TX, 77030, United States.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae608.
Xenograft models are attractive models that mimic human tumor biology and permit one to perturb the tumor microenvironment and study its drug response. Spatially resolved transcriptomics (SRT) provides a powerful way to study the organization of xenograft models, but currently there is a lack of specialized pipeline for processing xenograft reads originated from SRT experiments. Xenomake is a standalone pipeline for the automated handling of spatial xenograft reads. Xenomake handles read processing, alignment, xenograft read sorting, and connects well with downstream spatial analysis packages. We additionally show that Xenomake can correctly assign organism-specific reads, reduce sparsity of data by increasing gene counts, while maintaining biological relevance for studies.
Xenomake is an open-source program that is available on Github (https://github.com/qianzhulab/Xenomake). Complete documentation can be found at the link.
异种移植模型是一种有吸引力的模型，可模拟人类肿瘤生物学，允许人们扰乱肿瘤微环境并研究其药物反应。空间分辨转录组学（SRT）为研究异种移植模型的组织提供了一种强大的方法，但目前缺乏专门用于处理源自 SRT 实验的异种移植读取的管道。Xenomake 是一个用于自动处理空间异种移植读取的独立管道。Xenomake 处理读取处理、比对、异种移植读取排序，并与下游空间分析包很好地连接。我们还表明，Xenomake 可以正确分配特定于生物体的读取，通过增加基因计数来减少数据的稀疏性，同时保持对研究的生物学相关性。
Xenomake 是一个开源程序，可在 Github 上获得（https://github.com/qianzhulab/Xenomake）。完整的文档可以在该链接中找到。
