Publication Date
11-1-2024
Journal
Bioinformatics
DOI
10.1093/bioinformatics/btae608
PMID
39400332
PMCID
PMC11583937
PubMedCentral® Posted Date
10-14-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Humans, Software, Animals, Transcriptome, Mice, Gene Expression Profiling, Computational Biology, Neoplasms
Abstract
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.
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