Flannick Jason, Novak Antal, Do Chuong B, Srinivasan Balaji S, Batzoglou Serafim
Department of Computer Science, Stanford University , Stanford, CA 94305, USA.
J Comput Biol. 2009 Aug;16(8):1001-22. doi: 10.1089/cmb.2009.0099.
We developed Graemlin 2.0, a new multiple network aligner with (1) a new multi-stage approach to local network alignment; (2) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (3) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adapt it to any set of networks; and (4) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time. We tested Graemlin 2.0's accuracy on protein interaction networks from IntAct, DIP, and the Stanford Network Database. We show that, on each of these datasets, Graemlin 2.0 has higher sensitivity and specificity than existing network aligners. Graemlin 2.0 is available under the GNU public license at http://graemlin.stanford.edu .
我们开发了Graemlin 2.0，这是一种新型的多网络比对工具，具有以下特点：（1）一种用于局部网络比对的全新多阶段方法；（2）一种新颖的评分函数，该函数可以使用多网络比对的任意特征，如蛋白质缺失、蛋白质重复、蛋白质突变和相互作用损失；（3）一种参数学习算法，该算法使用已知网络比对的训练集来学习评分函数的参数，从而使其适用于任何网络集；（4）一种算法，该算法使用我们的评分函数在线性时间内找到近似的多网络比对。我们在来自IntAct、DIP和斯坦福网络数据库的蛋白质相互作用网络上测试了Graemlin 2.0的准确性。我们表明，在这些数据集中的每一个上，Graemlin 2.0都比现有的网络比对工具具有更高的灵敏度和特异性。Graemlin 2.0可在http://graemlin.stanford.edu上根据GNU公共许可证获取。
