Population genetics-based codon models

哺乳动物、病毒、细菌，甚至癌症肿瘤都会进化。了解它们的进化历史对于了解它们的生物学和未来变化的潜力至关重要。科学家们目前使用模型来帮助推断这段历史，但大多数模型都忽略了自然选择的现实。这项研究计划将开发模型，使科学家能够估计这些历史，同时将某些氨基酸的选择纳入模型。这些新模型包括反映群体遗传学过程的参数，并将用于推断密码子使用和氨基酸序列的选择强度，蛋白质功能对不同氨基酸性质（如大小和极性）的敏感性以及突变率。这些模型比传统模型更适合经验数据，并且将更准确地推断进化历史和过程。虽然初步工作使用氨基酸序列，但最终项目将使用DNA序列，从而更好地解决进化事件。鉴于理解从医学到法医学，从农业到基础分类学等领域的进化历史和过程的重要性，本文开发的方法的改进将对科学和社会都有很大的用处。这项研究将创建连接突变，漂移，密码子选择和氨基酸选择的模型，用于系统发育推断。与过去使用的从经验估计得到的对称转移矩阵不同，这里开发的模型将允许20个不同的转移矩阵（每个可能的最佳氨基酸一个），其允许氨基酸对之间的不同增益和损失率，但仅从几个现实参数生成。这里开发的模型将进行评估，既适合，使用AIC等措施，和充分性，相互比较，并与标准模型。性能将使用来自生物体组的多个基因数据集进行评估，从少数基因到整个基因组，以及模拟遗传数据集。初步分析表明，使用简化的模型拟合模型相比，传统的模型显着改善。它们还能更好地匹配和预测经验数据，这是模型充分性的标准测试。模型将通过包括软件开发人员在内的黑客团队被纳入现有的遗传学软件。外联活动将包括与当地教师合作，为他们的高中班级开发一个学习模块。

Mammals, viruses, bacteria, and even cancer tumors evolve. Understanding their evolutionary history can be critical to understanding their biology and potential for future change. Scientists currently use models to help infer this history, but most of these models ignore the reality of natural selection. This research proposal will develop models that allow scientists to estimate these histories while incorporating selection for certain amino acids into the models. These new models include parameters reflecting processes from population genetics, and will be used to infer the strength of selection on codon usage and amino acid sequence, sensitivity of protein function to different amino acid properties such as size and polarity, and mutation rates. These models fit empirical data far better than traditional models, and will more accurately infer evolutionary histories and processes. While the preliminary work uses amino acid sequences, the final project will use DNA sequences, allowing even greater resolution of evolutionary events. Given the importance of understanding evolutionary history and processes in fields ranging from medicine to forensics and from agriculture to basic taxonomy, the improvements in methods developed here will have great utility to both science and society.This research will create models linking mutation, drift, codon selection, and amino acid selection for use in phylogenetic inference. Rather than symmetric transition matrices derived from empirical estimates as used in the past, the models developed here will allow for twenty different transition matrices (one for each possible optimal amino acid) which allow for different rates of gain and loss between pairs of amino acids but which are generated from just a few, realistic parameters. The models developed here will be evaluated both for fit, using measures such as AIC, and adequacy, comparing them with each other and with standard models. Performance will be evaluated using multiple gene datasets from groups of organisms, ranging from a handful of genes to entire genomes, as well as from simulated genetic datasets. Preliminary analyses using a simplification of the proposed models show a dramatic improvement in model fit compared to traditional models. They also better match and predict empirical data, which is a standard test of model adequacy. Models will be incorporated into existing phylogenetics software via a hackathon including software developers. Outreach will include work with local teachers and development of a learning module for their high school classes.