Previous studies have reported that some important loci are missed in single-locus genome-wide association studies (GWAS), especially because of the large phenotypic error in field experiments. To solve this issue, multi-locus GWAS methods have been recommended. However, only a few software packages for multi-locus GWAS are available. Therefore, we developed an R software named mrMLM v4.0.2. This software integrates mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO methods developed by our lab. There are four components in mrMLM v4.0.2, including dataset input, parameter setting, software running, and result output. The fread function in data.table is used to quickly read datasets, especially big datasets, and the doParallel package is used to conduct parallel computation using multiple CPUs. In addition, the graphical user interface software mrMLM.GUI v4.0.2, built upon Shiny, is also available. To confirm the correctness of the aforementioned programs, all the methods in mrMLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets. The results confirm the superior performance of mrMLM v4.0.2 to other methods currently available. False positive rates are effectively controlled, albeit with a less stringent significance threshold. mrMLM v4.0.2 is publicly available at BioCode (https://bigd.big.ac.cn/biocode/tools/BT007077) or R (https://cran.r-project.org/web/packages/mrMLM.GUI/index.html) as an open-source software.
蛋白质琥珀酰化是一种生物化学反应，其中琥珀基(-CO-CH2-CH2-CO-)连接到蛋白质分子的赖氨酸残基上。赖氨酸琥珀酰化在活细胞中起着重要的调节作用。然而，赖氨酸琥珀酰化的底物位点特异性难以在实验中确定，导致这一领域的研究受到了限制。为了促进这对这种蛋白翻译后修饰的理解，一些工具已经被开发用于计算、鉴定琥珀酰赖氨酸位点。在本研究中，我们开发了一种基于氨基酸组成的赖氨酸琥珀酰化位点底物特异性研究方法。利用从公共资源收集的、经实验验证的赖氨酸琥珀酰化位点信息，使用两个样品标识程序发现了琥珀酰化和非琥珀酰化位点之间的位置特异性氨基酸组成的显着差异。这些发现使得能够采用有效的机器学习方法，即支持向量机，以训练不仅具有氨基酸组成，而且具有k间隔的氨基酸对组成的预测模型。使用十倍交叉验证方法选择最佳模型后，利用从已发表的研究文章中手动提取的独立数据集进行验证，结果显示所选模型的性能明显优于现有工具。最后，选择的模型用于开发基于Web的工具SuccSite，以辅助蛋白质琥珀酰化的研究。该网站使用两种蛋白质作为案例研究，以证明琥珀酰化位点的有效预测。我们将通过集成更多实验数据集来定期更新SuccSite。SuccSite可从http://csb.cse.yzu.edu.tw/SuccSite/免费访问。





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