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Updated: May 21, 2026
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Depletion of Mouse Cells from Human Tumor Xenografts Significantly Improves Downstream Analysis of Target Cells
Published on: July 29, 2016
Xenome--a tool for classifying reads from xenograft samples
Thomas Conway1, Jeremy Wazny, Andrew Bromage
1NICTA Victoria Research Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Parkville and Monash Institute of Medical Research, Monash University, Clayton, Australia. tom.conway@nicta.com.au
Bioinformatics (Oxford, England)
|June 13, 2012
Summary
Shotgun sequencing of xenograft samples mixes host and graft DNA. Xenome accurately classifies these mixed reads, enabling precise downstream analysis for improved research outcomes.
Area of Science:
- Bioinformatics
- Genomics
- Computational Biology
Background:
- Shotgun sequence data from xenografts contain mixed host and graft reads.
- Accurate classification of these mixed reads is crucial for precise downstream analysis.
Purpose of the Study:
- To develop a fast, accurate, and specific technique for classifying xenograft-derived sequence read data.
- To introduce the Xenome tool for this classification task.
Main Methods:
- Development of a novel classification technique.
- Implementation of the technique into the Xenome software tool.
- Evaluation using RNA-Seq data from human, mouse, and human-in-mouse xenografts.
Main Results:
- Xenome demonstrates fast, accurate, and specific classification of xenograft sequence reads.
- Successful evaluation across diverse xenograft datasets (human, mouse, human-in-mouse).
Conclusions:
- Xenome provides an effective solution for separating host and graft reads in xenograft sequencing data.
- This facilitates more precise analysis in xenograft research.
