FATHMM-XF：通过扩展特征准确预测致病性点突变。
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features.
机构信息
Intelligent Systems Laboratory, University of Bristol, Bristol BS8?1UB, UK.
MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol BS8?2BN, UK.
出版信息
Bioinformatics. 2018 Feb 1;34(3):511-513. doi: 10.1093/bioinformatics/btx536.
Abstract
SUMMARY
We present FATHMM-XF, a method for predicting pathogenic point mutations in the human genome. Drawing on an extensive feature set, FATHMM-XF outperforms competitors on benchmark tests, particularly in non-coding regions where the majority of pathogenic mutations are likely to be found.
AVAILABILITY AND IMPLEMENTATION
The FATHMM-XF web server is available at http://fathmm.biocompute.org.uk/fathmm-xf/, and as tracks on the Genome Tolerance Browser: http://gtb.biocompute.org.uk. Predictions are provided for human genome version GRCh37/hg19. The data used for this project can be downloaded from: http://fathmm.biocompute.org.uk/fathmm-xf/.
CONTACT
mark.rogers@bristol.ac.uk or c.campbell@bristol.ac.uk.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
摘要
我们提出了 FATHMM-XF，这是一种预测人类基因组中致病点突变的方法。FATHMM-XF 利用广泛的特征集，在基准测试中优于竞争对手，特别是在大多数致病突变可能被发现的非编码区域。
可用性和实现
FATHMM-XF 网络服务器可在 http://fathmm.biocompute.org.uk/fathmm-xf/ 获得，也可在基因组耐受浏览器上作为轨道使用：http://gtb.biocompute.org.uk。预测结果适用于人类基因组版本 GRCh37/hg19。该项目使用的数据可从以下网址下载：http://fathmm.biocompute.org.uk/fathmm-xf/。
联系方式
mark.rogers@bristol.ac.uk 或 c.campbell@bristol.ac.uk。
补充信息
补充数据可在生物信息学在线获得。
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