XenofilteR: computational deconvolution of mouse and human reads in tumor xenograft sequence data

Background Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human ca...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 19; no. 1; pp. 366 - 15
Main Authors: Kluin, Roelof J. C., Kemper, Kristel, Kuilman, Thomas, de Ruiter, Julian R., Iyer, Vivek, Forment, Josep V., Cornelissen-Steijger, Paulien, de Rink, Iris, ter Brugge, Petra, Song, Ji-Ying, Klarenbeek, Sjoerd, McDermott, Ultan, Jonkers, Jos, Velds, Arno, Adams, David J., Peeper, Daniel S., Krijgsman, Oscar
Format: Journal Article
Language:English
Published: London BioMed Central 04.10.2018
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Accuracy
Algorithms
Animal models
Animals
Bioinformatics
Biomedical and Life Sciences
Breast cancer
Cancer
Cancer research
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer applications
Computers
Data processing
Databases, Genetic
Deoxyribonucleic acid
DNA
DNA sequencing
Gene expression
Gene frequency
Gene mutation
Genetic testing
Genomes
Health aspects
High-Throughput Nucleotide Sequencing - methods
Humans
Kinases
Laboratory rats
Life Sciences
Melanoma
Mice
Microarrays
Mouse devices
Mutation
Next-generation sequencing (NGS)
Nucleotide sequence
Ribonucleic acid
RNA
Sequence analysis (methods)
Sequencing
Software
Tumor cell lines
Tumors
Xenograft
Xenografts
Xenotransplantation
Xenograft
Melanoma
Breast cancer
Patient-derived xenografts (PDX)
Sequencing
Next-generation sequencing (NGS)
Cancer
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Mouse xenografts from (patient-derived) tumors (PDX) or tumor cell lines are widely used as models to study various biological and preclinical aspects of cancer. However, analyses of their RNA and DNA profiles are challenging, because they comprise reads not only from the grafted human cancer but also from the murine host. The reads of murine origin result in false positives in mutation analysis of DNA samples and obscure gene expression levels when sequencing RNA. However, currently available algorithms are limited and improvements in accuracy and ease of use are necessary. Results We developed the R-package XenofilteR, which separates mouse from human sequence reads based on the edit-distance between a sequence read and reference genome. To assess the accuracy of XenofilteR, we generated sequence data by in silico mixing of mouse and human DNA sequence data. These analyses revealed that XenofilteR removes > 99.9% of sequence reads of mouse origin while retaining human sequences. This allowed for mutation analysis of xenograft samples with accurate variant allele frequencies, and retrieved all non-synonymous somatic tumor mutations. Conclusions XenofilteR accurately dissects RNA and DNA sequences from mouse and human origin, thereby outperforming currently available tools. XenofilteR is open source and available at https://github.com/PeeperLab/XenofilteR .
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2353-5