Related Experiment Video
Updated: Jun 10, 2025
Genome-wide Snapshot of Chromatin Regulators and States in Xenopus Embryos by ChIP-Seq
Published on: February 26, 2015
Xenomake: a pipeline for processing and sorting xenograft reads from spatial transcriptomic experiments
Benjamin S Strope1,2, Katherine E Pendleton1,2,3, William Z Bowie1,2
1Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, United States.
Summary:
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.
Availability And Implementation:
Xenomake is an open-source program that is available on Github (https://github.com/qianzhulab/Xenomake). Complete documentation can be found at the link.
Insights
Xenomake is a new bioinformatics pipeline that automates spatial xenograft data processing. This tool enhances gene counts and maintains biological relevance for improved tumor microenvironment studies.
Area of Science:
- Bioinformatics
- Genomics
- Cancer Research
Background:
- Xenograft models are crucial for studying human tumor biology and drug responses.
- Spatially resolved transcriptomics (SRT) offers insights into xenograft model organization.
- A specialized pipeline for SRT xenograft data processing is currently lacking.
Purpose of the Study:
- To develop Xenomake, a standalone pipeline for automated processing of spatial xenograft reads.
- To facilitate downstream spatial analysis of xenograft data.
Main Methods:
- Xenomake automates read processing, alignment, and xenograft read sorting.
- The pipeline is designed for seamless integration with existing spatial analysis packages.
Main Results:
- Xenomake correctly assigns organism-specific reads.
- The pipeline reduces data sparsity by increasing gene counts.
- Biological relevance is maintained throughout the data processing.
Conclusions:
- Xenomake addresses the need for specialized xenograft SRT data processing.
- The pipeline enhances data quality and supports critical tumor microenvironment research.
