BBSRC-NSF/BIO: IIBR Informatics: Collaborative Research: Inference of isoform-level regulatory infrastructures with studies in steroid-producing cells

BBSRC-NSF/BIO：IIBR 信息学：合作研究：通过对类固醇生成细胞的研究推断异构体水平的监管基础设施

• 批准号: 2019797
• 负责人: Mingfu Shao
• 金额: $ 40万
• 依托单位: Pennsylvania State Univ University Park
• 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: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019797&HistoricalAwards=false
• 关键词: BBSRC; NSF; BIO; IIBR; Informatics

Cells are the fundamental units that provide functions needed to sustain life in living organisms. Cellular functions are carried out by proteins, products of genes, and the process of producing proteins from genes (i.e., gene expression) is mediated by complex regulation systems. Much remains unknown about the mechanisms of gene regulations. Given all genes in a cell, the regulatory relationships among genes can be represented by networks, called gene regulatory networks. It has been a long-standing challenge to reconstruct these networks experimentally and computationally. A gene can express multiple isoforms (mRNA molecules), and hence produces multiple different proteins, which makes the underlying gene regulatory networks more complicated. Recent advances in single cell RNA-Sequencing (scRNA-Seq) technology has brought new opportunities in resolving high-quality regulatory networks, but also posed new computational challenges. The project aims to computationally reconstruct accurate regulatory networks at the isoform-level from large-scale sequencing data. Educational and outreach activities, such as courses on topics in computational biology and inclusion of minority students, will be carried out. The project will develop efficient approaches to identify expressed isoforms and to determine expression abundances, and then develop a network-reconstruction method which improves current state-of-art. The new computational methods will be validated and applied to the field of immunology--to study cellular mechanisms in steroid-producing cells. The project will make contribution in improvements over existing methods. First, the proposed methods for developing a scalable transcript assembler will enable accurate determination and quantification of the expressed isoforms, and make it possible to build regulatory networks at the level of isoforms to reflect the possible difference in regulatory mechanisms for different isoforms. Second, many recently developed methods for network inference require cells to be pre-ordered with trajectory inference or RNA-velocity to mimic time-series data. Errors in the cell ordering can mislead network inference and lead to false predictions. The project proposes to perform cell ordering and network inference simultaneously, which is expected to provide better results for both cell ordering and network inference. The project will reconstruct transcript-level regulatory networks for different types of steroid-producing cells from both published and newly generated single-cell data. The results of the project can be found at the PI’s website: https://www.cc.gatech.edu/~xzhang954/.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.

细胞是提供维持生活组织生活所需功能的基本单位。细胞功能是通过蛋白质，基因产物进行的，从基因产生蛋白质（即基因表达）的过程是由复杂调节系统介导的。关于基因的机制，仍然未知。鉴于细胞中的所有基因，基因之间的调节关系可以由网络表示，称为基因调节网络。在实验和计算上重建这些网络一直是一个长期的挑战。基因可以表达多种同工型（mRNA分子），因此产生多种不同的蛋白质，这使得基因调节网络更加复杂。单细胞RNA-Sequering（SCRNA-SEQ）技术的最新进展为解决高质量的监管网络带来了新的机会，但也带来了新的计算挑战。该项目旨在通过大规模测序数据从同工型级上重建准确的调节网络。将开展教育和宣传活动，例如有关计算生物学主题和少数族裔学生的主题课程。该项目将开发有效的方法来识别表达的同工型并确定表达抽象，然后开发一种网络重建方法，以改善当前的最新前部。新的计算方法将得到验证并应用于免疫学领域 - 研究产生类固醇细胞的细胞机制。该项目将在改进现有方法方面做出贡献。首先，开发可伸缩转录物组件的建议方法将能够准确确定和量化表达的同工型，并可以在同工型水平上构建调节网络，以反映不同同工型的调节机制可能差异。其次，许多最近开发的网络推理方法要求细胞通过轨迹推断或RNA速度预先排序以模拟时间序列数据。单元格排序中的错误可能会误导网络推断并导致错误的预测。简单地执行单元格排序和网络推理的项目建议，预计将为单元格排序和网络推理提供更好的结果。该项目将重建来自已发表和新生成的单细胞数据的不同类型产生类固醇的细胞的成绩单级调节网络。该项目的结果可以在PI的网站上找到：https：//www.cc.gatech.edu/~xzhang954/.this奖反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来诚实地支持。

项目成果

Accurate assembly of multi-end RNA-seq data with Scallop2

• DOI: 10.1038/s43588-022-00216-1
• 发表时间: 2022-03
• 期刊: Nature Computational Science
• 作者: Qimin Zhang;Qian Shi;Mingfu Shao