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.

细胞是提供维持生物体生命所需功能的基本单位。细胞功能由蛋白质、基因产物和从基因产生蛋白质的过程（即，基因表达）由复杂的调节系统介导。关于基因调控的机制仍有许多未知之处。给定细胞中的所有基因，基因之间的调控关系可以用网络来表示，称为基因调控网络。从实验和计算上重建这些网络一直是一个长期的挑战。一个基因可以表达多种亚型（mRNA分子），因此产生多种不同的蛋白质，这使得潜在的基因调控网络更加复杂。单细胞RNA测序（scRNA-Seq）技术的最新进展为解决高质量的调控网络带来了新的机遇，但也带来了新的计算挑战。该项目旨在从大规模测序数据中计算重建异构体水平的精确调控网络。 将开展教育和外联活动，例如关于计算生物学和少数民族学生融入问题的课程。该项目将开发有效的方法来识别表达的异构体，并确定表达丰度，然后开发一个网络重建方法，提高目前的最先进的新的计算方法将被验证和应用到免疫学领域-研究类固醇产生细胞的细胞机制。该项目将有助于改进现有的方法。首先，所提出的用于开发可扩展的转录物组装器的方法将使得能够准确地确定和定量所表达的同种型，并且使得能够在同种型水平上构建调控网络以反映不同同种型的调控机制中的可能差异。其次，许多最近开发的网络推理方法需要使用轨迹推理或RNA速度对细胞进行预先排序，以模拟时间序列数据。细胞排序中的错误可能会误导网络推理并导致错误的预测。该项目建议同时执行单元排序和网络推理，这有望为单元排序和网络推理提供更好的结果。该项目将从已发表和新生成的单细胞数据中重建不同类型类固醇产生细胞的转录水平调控网络。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。

项目成果

Locality-sensitive bucketing functions for the edit distance.

• DOI: 10.1186/s13015-023-00234-2
• 发表时间: 2023-07-24
• 期刊: Algorithms for molecular biology : AMB

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