CAGEE: Computational analysis of gene expression evolution

生物学的进步表明，许多性状是由每个细胞中产生的基因数量控制的，而不仅仅是每个基因和蛋白质的序列。基因在相同生物体内的不同组织之间或相同组织中的物种之间的这些表达水平可以不同。了解这些水平如何变化以及它们如何控制生物体的外观和功能是当前研究的关键挑战。该项目将开发新的方法来了解组织和物种之间的基因表达变化，以及这些变化是如何协调的。该研究采用新的统计方法来制作用于进行分析的开源软件。该软件将使所有科学家能够在多个平台上使用这些新工具，为国家网络基础设施做出贡献。这项工作还将改善和加速具有多种社会效益的研究，包括实现新的生物学发现。该项目将支持培养一名博士后研究员、研究生和本科生，他们来自科学和技术职业中代表性不足的群体。将开发关于理解进化树的课堂教案和计算机实验室并分发给合作机构。基因表达的变化已被证明是物种之间许多差异的原因，最近的技术进步意味着此类数据很容易收集，即使在非模式生物中也是如此。通过测量同一基因在多个物种中的表达水平，我们可以开始了解生物体间基因表达变化的历史。通过测量数千个基因的表达，我们可以进一步了解基因表达进化的机制和模式。本研究开发了一个新的软件包（CAGEE），用于研究基因表达进化的系统发育树。这里开发的统计方法也使得在一个框架中研究多个组织或性别成为可能，使我们能够对样本之间进化速率的差异进行统计学上的严格推断。将这些工具集成到一个可在多个平台上使用的软件包中，使这些方法能够被广泛的用户群应用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Biological advances have revealed that many traits are controlled by the amount of a gene produced in each cell, not just the sequence of each gene and protein. Genes can differ in these expression levels between different tissues within the same organism or between species in the same tissue. Understanding how these levels change—and how they control the appearance and function of organisms—is a key challenge for current research. This project will develop new methods for understanding gene expression changes between tissues and species, as well as how these changes are coordinated. The research applies new statistical approaches to produce open-source software for carrying out the analyses. The software will allow all scientists to be able to use these new tools on multiple platforms, contributing to the national cyberinfrastructure. This work also will improve and accelerate research with multiple societal benefits, including enabling new biological discoveries. This project will support the training of a postdoctoral researcher, graduate students, and undergraduates from groups that are underrepresented in science and technology careers. A classroom lesson plan and computer lab on understanding evolutionary trees will be developed and distributed to partner institutions.Changes in gene expression have been shown to be responsible for many differences between species, and recent technological advances mean that such data are easy to collect, even in non-model organisms. By measuring expression levels of the same gene in multiple species, we can begin to understand the history of changes in gene expression across organisms. By measuring expression in thousands of genes, we can further understand the mechanisms and modes by which gene expression evolves. This research develops a new software package (CAGEE) for studying gene expression evolution across a phylogenetic tree. The statistical approach developed here also makes it possible to study multiple tissues or sexes in a single framework, allowing us to make statistically rigorous inferences about differences in the rates of evolution among samples. Integrating these tools into a single software package available on multiple platforms enables these methods to be applied by a wide group of users.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.