DASAF: An R Package for Deep Sequencing-Based Detection of Fetal Autosomal Abnormalities from Maternal Cell-Free DNA

文献类型： 外文期刊

第一作者： Liu, Baohong

作者： Liu, Baohong;Cai, JianPing;Tang, Xiaoyan;Li, Yixue;Ding, Guohui;Qiu, Feng;Gao, Junhui;Li, Yixue;Ding, Guohui;Qiu, Feng;Tao, Chunmei;Gao, Junhui;Ma, Mengmeng;Zhong, Tingyan;Ding, Guohui

作者机构：

期刊名称：BIOMED RESEARCH INTERNATIONAL （ 影响因子：3.411； 五年影响因子：3.62 ）

ISSN： 2314-6133

年卷期： 2016 年

页码：

收录情况： SCI

摘要： Background. With the development of massively parallel sequencing (MPS), noninvasive prenatal diagnosis using maternal cell-free DNA is fast becoming the preferred method of fetal chromosomal abnormality detection, due to its inherent high accuracy and low risk. Typically, MPS data is parsed to calculate a risk score, which is used to predict whether a fetal chromosome is normal or not. Although there are several highly sensitive and specific MPS data-parsing algorithms, there are currently no tools that implement these methods. Results. We developed an R package, detection of autosomal abnormalities for fetus (DASAF), that implements the three most popular trisomy detection methods-the standard Z-score (STDZ) method, the GC correction Z-score (GCCZ) method, and the internal reference Z-score (IRZ) method-together with one subchromosome abnormality identification method (SCAZ). Conclusions. With the cost of DNA sequencing declining and with advances in personalized medicine, the demand for noninvasive prenatal testing will undoubtedly increase, which will in turn trigger an increase in the tools available for subsequent analysis. DASAF is a user-friendly tool, implemented in R, that supports identification of whole-chromosome as well as subchromosome abnormalities, based on maternal cell-free DNA sequencing data after genome mapping.

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