Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W232-7. doi: 10.1093/nar/gkt471. Epub 2013 Jun 8.
Evolutionary analysis of phyletic patterns (phylogenetic profiles) is widely used in biology, representing presence or absence of characters such as genes, restriction sites, introns, indels and methylation sites. The phyletic pattern observed in extant genomes is the result of ancestral gain and loss events along the phylogenetic tree. Here we present CoPAP (coevolution of presence-absence patterns), a user-friendly web server, which performs accurate inference of coevolving characters as manifested by co-occurring gains and losses. CoPAP uses state-of-the-art probabilistic methodologies to infer coevolution and allows for advanced network analysis and visualization. We developed a platform for comparing different algorithms that detect coevolution, which includes simulated data with pairs of coevolving sites and independent sites. Using these simulated data we demonstrate that CoPAP performance is higher than alternative methods. We exemplify CoPAP utility by analyzing coevolution among thousands of bacterial genes across 681 genomes. Clusters of coevolving genes that were detected using our method largely coincide with known biosynthesis pathways and cellular modules, thus exhibiting the capability of CoPAP to infer biologically meaningful interactions. CoPAP is freely available for use at http://copap.tau.ac.il/.
系统发育模式（系统发育谱）的进化分析在生物学中得到了广泛应用，代表了基因、限制位点、内含子、插入缺失和甲基化位点等特征的存在或缺失。在现存基因组中观察到的系统发育模式是沿着系统发育树发生的祖先增益和丢失事件的结果。在这里，我们介绍了 CoPAP（共现缺失模式的共进化），这是一个用户友好的网络服务器，它可以准确推断出由共现的增益和损失表现出来的共进化特征。CoPAP 使用最先进的概率方法来推断共进化，并允许进行高级网络分析和可视化。我们开发了一个用于比较检测共进化的不同算法的平台，其中包括具有共进化位点和独立位点对的模拟数据。使用这些模拟数据，我们证明了 CoPAP 的性能优于替代方法。我们通过分析 681 个基因组中数千个细菌基因之间的共进化来说明 CoPAP 的实用性。使用我们的方法检测到的共进化基因簇与已知的生物合成途径和细胞模块大致吻合，因此 CoPAP 能够推断出具有生物学意义的相互作用。CoPAP 可在 http://copap.tau.ac.il/ 免费使用。
