IIBR:Informatics:Toward an Automated RNA-seq Bioinformatician

IIBR：信息学：走向自动化 RNA-seq 生物信息学家

• 批准号: 1937540
• 负责人: Carleton Kingsford
• 金额: $ 54.61万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1937540&HistoricalAwards=false
• 关键词: IIBR; Informatics; Toward; Automated; RNA

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.

基因表达的测量-哪些基因在哪些条件下活跃-是理解生物系统不可或缺的工具。从现代基因组测序技术分析基因表达需要使用复杂的软件，如读取映射器、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经常必须手动或通过其他特别方法调整这些参数。这个项目的目标是通过设计和实现一个自动学习基因表达分析软件的高性能参数的框架来自动化这一过程。该项目还旨在开发算法、软件和方法论，使该框架实用和有用。这将使更多的研究人员能够以显著较少的工作量获得高质量的基因表达分析，并将能够改进对大数据集的分析，在这些数据集中，手动调整每个样本的参数是不现实的。生物结果的重现性也将得到提高，因为参数的选择明确地让给了一个自动化的、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准确，成本更低。计划为不同层次的学生(小学到本科)开展一些教育和外展活动，以加强社区对基因表达及其分析的了解。开发的过程将在几个包装工具中实施，用于参数优化，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为几个更容易处理的步骤。第一步将是通过分析现有RNA-SEQ样本的大量集合，为每个工具学习具有代表性的参数向量的集合。在第二步中，将使用基于贝叶斯优化、遗传算法和分类方法等技术的组合的机器学习方法来设计从这些集合中选择预测提供高性能的参数向量的技术。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理由的技术。该系统的设计还将增强我们在生物学其他应用领域中此类参数优化技术的实用知识。这个奖项反映了NSF的法定使命，通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Revisiting the complexity of and algorithms for the graph traversal edit distance and its variants

• DOI: 10.1186/s13015-024-00262-6
• 发表时间: 2024-04-29
• 期刊: ALGORITHMS FOR MOLECULAR BIOLOGY
• 影响因子: 1
• 作者: Qiu，Yutong;Shen，Yihang;Kingsford，Carl
• 通讯作者: Kingsford，Carl

Reinforcement Learning for Robotic Liquid Handler Planning

机器人液体处理机规划的强化学习

• 发表时间: 2023
• 期刊: WABI 2023
• 作者: Ferdosi, Mohsen;Ge, Yuejun;Kingsford, Carl
• 通讯作者: Kingsford, Carl

How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design

• DOI: 10.1145/3406325.3451036
• 发表时间: 2021-06
• 期刊: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
• 作者: Maria-Florina Balcan;Dan F. DeBlasio;Travis Dick;Carl Kingsford;T. Sandholm;Ellen Vitercik

Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection

通过变量选择进行计算高效的高维贝叶斯优化

• 发表时间: 2023
• 期刊: AutoML Conference 2023
• 作者: Shen, Yihang;Kingsford, Carl
• 通讯作者: Kingsford, Carl

Optimizing Dynamic Structures with Bayesian Generative Search

• 发表时间: 2020-07
• 作者: Minh Hoang;Carleton Kingsford