Are special read alignment strategies necessary and cost-effective when handling sequencing reads from patient-derived tumor xenografts?

BMC Genomics

Kevin Y Yip¹  Kwok-Wai Lo²  Sau Dan Lee³  Kai-Yuen Tso³ 

[1] CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong;Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong;Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong

关键词: High-throughput sequencing;    Contamination;    Nasopharyngeal carcinoma;    Xenografts;   

Others  :  1121505
DOI  :  10.1186/1471-2164-15-1172

 received in 2014-04-08, accepted in 2014-12-11,  发布年份 2014

【 摘 要 】

Background

Patient-derived tumor xenografts in mice are widely used in cancer research and have become important in developing personalized therapies. When these xenografts are subject to DNA sequencing, the samples could contain various amounts of mouse DNA. It has been unclear how the mouse reads would affect data analyses. We conducted comprehensive simulations to compare three alignment strategies at different mutation rates, read lengths, sequencing error rates, human-mouse mixing ratios and sequenced regions. We also sequenced a nasopharyngeal carcinoma xenograft and a cell line to test how the strategies work on real data.

Results

We found the "filtering" and "combined reference" strategies performed better than aligning reads directly to human reference in terms of alignment and variant calling accuracies. The combined reference strategy was particularly good at reducing false negative variants calls without significantly increasing the false positive rate. In some scenarios the performance gain of these two special handling strategies was too small for special handling to be cost-effective, but it was found crucial when false non-synonymous SNVs should be minimized, especially in exome sequencing.

Conclusions

Our study systematically analyzes the effects of mouse contamination in the sequencing data of human-in-mouse xenografts. Our findings provide information for designing data analysis pipelines for these data.

【 授权许可】

   
2014 Tso et al.; licensee BioMed Central.

【 图 表 】

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