IIBR:Informatics:Toward an Automated RNA-seq Bioinformatician

测量基因表达——哪些基因在哪些条件下是活跃的——是理解生物系统不可或缺的工具。现代基因组测序技术的基因表达分析需要使用复杂的软件，如读图绘制器、转录本组装器和表达丰度估计器。实现这些步骤之一的软件程序通常具有大量用户可设置的参数，这些参数影响分析算法的执行方式。科学家、生物学家和临床研究人员必须经常手动或通过其他特殊方法调整这些参数。该项目的目标是通过设计和实现一个框架来自动学习基因表达分析软件的高性能参数，从而使这一过程自动化。该项目还旨在开发算法、软件和方法，使该框架具有实用性和实用性。这将使更多的研究人员能够以更少的努力获得高质量的基因表达分析，并且还可以改进对大型数据集的分析，其中手动调整每个样本的参数是不切实际的。生物结果的可重复性也将得到加强，因为参数的选择明确地让与一个自动化的，可重复的过程。这项研究将使涉及基因表达的生物学研究更加准确，成本更低。计划为不同层次的学生（从小学到本科）举办一些教育和推广活动，以加强社区对基因表达及其分析的理解。开发的过程将在几个包装工具中实施，用于参数优化，这些工具可以放入现有的RNA-seq分析管道中，以提高每一步的准确性。设计这些工具的研究将被分解成几个更容易处理的步骤。第一步将是通过分析大量现有RNA-seq样本来学习每个工具的代表性参数向量集合。在第二步中，基于贝叶斯优化、遗传算法和分类方法等技术组合的机器学习方法将用于设计技术，以从这些预测提供高性能的集合中选择参数向量。在第三步中，将设计和实现为自动参数选择提供人类可解释的基本原理的技术。该系统的设计也将增强我们在生物学其他应用领域中对此类参数优化技术的实践知识。该奖项反映了美国国家科学基金会的法定使命，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，认为值得支持。

Measurement of gene expression --- which genes are active in which conditions --- is an indispensable tool for understanding biological systems. Analysis of gene expression from modern genomic sequencing technologies requires the use of sophisticated software such as read mappers, transcript assemblers, and expression abundance estimators. A software program implementing one of these steps typically has a large number of user-settable parameters that influence how the analysis algorithm performs. Scientists,biologists, and clinical researchers must often tune these parameters by hand or through other ad hoc means. The goal of this project is to automate this process by designing and implementing a framework for automatically learning high-performing parameters for gene expression analysis software. This project also aims to develop algorithms, software, and methodology to make this framework practical and useful. This will allow more researchers to obtain high-quality gene expression analyses with significantly less effort and will also enable improved analysis of large data sets where per-sample parameter tuning by hand is impractical. Reproducibility of biological results will also be enhanced since the choice of parameters is explicitly ceded to an automated, repeatable process. This research will make biological studies involving gene expression more accurate and less costly. A number of educational and outreach activities for various levels of students (elementary through undergraduate) are planned to enhance community understanding of gene expression and its analysis.The developed processes will be implemented in several wrapper tools for parameter optimization that can be dropped into existing RNA-seq analysis pipelines to improve accuracy at each step. The research to design these tools will be broken down into several more tractable steps. The first step will be learning, for each tool, a collection of representative parameter vectors by analyzing large collections of existing RNA-seq samples. In the second step, machine learning methods, based on a combination of techniques such as Bayesian Optimization, genetic algorithms, and classification approaches, will be used to design techniques to select parameter vectors from these sets that are predicted to offer high performance. In the third step, techniques for providing human-interpretable rationales for the automatic parameter choices will be designed and implemented. The design of this system will also enhance our practical knowledge of techniques for such parameter optimization in other application domains within biology. Results from the project can be founThis 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.