Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark.
PLoS One. 2013 Jul 25;8(7):e68370. doi: 10.1371/journal.pone.0068370. Print 2013.
We have developed a sequence conservation-based artificial neural network predictor called NetDiseaseSNP which classifies nsSNPs as disease-causing or neutral. Our method uses the excellent alignment generation algorithm of SIFT to identify related sequences and a combination of 31 features assessing sequence conservation and the predicted surface accessibility to produce a single score which can be used to rank nsSNPs based on their potential to cause disease. NetDiseaseSNP classifies successfully disease-causing and neutral mutations. In addition, we show that NetDiseaseSNP discriminates cancer driver and passenger mutations satisfactorily. Our method outperforms other state-of-the-art methods on several disease/neutral datasets as well as on cancer driver/passenger mutation datasets and can thus be used to pinpoint and prioritize plausible disease candidates among nsSNPs for further investigation. NetDiseaseSNP is publicly available as an online tool as well as a web service: http://www.cbs.dtu.dk/services/NetDiseaseSNP.
我们开发了一种基于序列保守性的人工神经网络预测器，称为 NetDiseaseSNP，它可以将 nsSNP 分类为致病或中性。我们的方法使用 SIFT 的优秀对齐生成算法来识别相关序列，并结合 31 种评估序列保守性和预测表面可及性的特征，生成一个单一的分数，可用于根据潜在的致病可能性对 nsSNP 进行排名。NetDiseaseSNP 成功地对致病和中性突变进行了分类。此外，我们还表明，NetDiseaseSNP 可以令人满意地区分癌症驱动和乘客突变。我们的方法在几个疾病/中性数据集以及癌症驱动/乘客突变数据集中的表现优于其他最先进的方法，因此可以用于在 nsSNP 中找出并优先考虑可能的疾病候选物，以便进一步研究。NetDiseaseSNP 作为在线工具和网络服务提供：http://www.cbs.dtu.dk/services/NetDiseaseSNP。
