GTB - 一个在线基因组耐受性浏览器。
GTB - an online genome tolerance browser.
作者信息
Shihab Hashem A, Rogers Mark F, Ferlaino Michael, Campbell Colin, Gaunt Tom R
机构信息
MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, BS8 2BN, UK.
Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, UK.
出版信息
BMC Bioinformatics. 2017 Jan 6;18(1):20. doi: 10.1186/s12859-016-1436-4.
BACKGROUND
Accurate methods capable of predicting the impact of single nucleotide variants (SNVs) are assuming ever increasing importance. There exists a plethora of in silico algorithms designed to help identify and prioritize SNVs across the human genome for further investigation. However, no tool exists to visualize the predicted tolerance of the genome to mutation, or the similarities between these methods.
RESULTS
We present the Genome Tolerance Browser (GTB, http://gtb.biocompute.org.uk ): an online genome browser for visualizing the predicted tolerance of the genome to mutation. The server summarizes several in silico prediction algorithms and conservation scores: including 13 genome-wide prediction algorithms and conservation scores, 12 non-synonymous prediction algorithms and four cancer-specific algorithms.
CONCLUSION
The GTB enables users to visualize the similarities and differences between several prediction algorithms and to upload their own data as additional tracks; thereby facilitating the rapid identification of potential regions of interest.
背景
能够预测单核苷酸变异（SNV）影响的准确方法正变得越来越重要。存在大量旨在帮助识别和优先排序人类基因组中SNV以便进一步研究的计算机算法。然而，目前还没有工具能够可视化基因组对突变的预测耐受性，或者这些方法之间的相似性。
结果
我们展示了基因组耐受性浏览器（GTB，http://gtb.biocompute.org.uk ）：一个用于可视化基因组对突变的预测耐受性的在线基因组浏览器。该服务器总结了几种计算机预测算法和保守性得分：包括13种全基因组预测算法和保守性得分、12种非同义预测算法以及4种癌症特异性算法。
结论
GTB使用户能够可视化几种预测算法之间的异同，并上传自己的数据作为附加轨迹；从而便于快速识别潜在的感兴趣区域。
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