ArticleBioinformatics (Oxford, England)2024
Xenomake: a pipeline for processing and sorting xenograft reads from spatial transcriptomic experiments.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- KSHV-infected endothelial cells expand and up-regulate angiogenic pathways and CXCR4 in patient-derived Kaposi sarcoma models.Science translational medicine · 2026Article
- Mapping herpesvirus-driven impacts on the cellular milieu and transcriptional profile of Kaposi sarcoma in patient-derived mouse models.bioRxiv : the preprint server for biology · 2024Article
- Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples.Cell reports methods · 2024Article
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5 authors.
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Abstract
summaryXenograft 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.