CAREER: Information-Theoretic Methods for RNA Analytics

职业：RNA 分析的信息理论方法

• 批准号: 1651236
• 负责人: Sreeram Kannan
• 金额: $ 50万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1651236&HistoricalAwards=false
• 关键词: CAREER; Information; Theoretic; Methods; RNA

The development of high-throughput sequencing has ushered in a new era in molecular biology, enabling inexpensive study of the genome. Furthermore, in recent years, RNA sequencing has enhanced our ability to quantify the dynamics of gene expression with transcript-level precision and single-cell resolution. This has applications in diverse areas like evolutionary biology, developmental biology, medical transcriptomics as well as synthetic biology. These advances in biotechnology necessitate corresponding advances in the development of computational algorithms that perform inference on these new datasets. Information theory offers a natural lens to study such problems as it can quantify the amount of data required to make accurate inferences, as well as leading to optimality. The main research objective of this project is to adapt, apply and create new information-theoretic and algorithmic methods to solve inference problems arising in RNA sequence analytics. The project will also have a significant educational component to integrate these new discoveries into graduate and undergraduate courses that can expose electrical engineering and computer science students to sequencing problems, in addition to exposing high-school and undergraduate students to this research area by outreach and mentoring.The project will study inference problems at two different levels of RNA-sequencing: assembly and downstream analytics. The typical method for RNA-sequencing involves fragmentation of RNA into short fragments that are then sequenced. The first thrust of this project will be in studying the informational limits and algorithms for this ?assembly? problem, particularly in studying the role of errors and repeated regions in the genome. The second thrust will be to study informational limits and algorithms for the downstream task of utilizing single-cell RNA-sequence data to understand gene-regulation and cell-differentiation.

高通量测序的发展开创了分子生物学的新时代，使基因组的廉价研究成为可能。此外，近年来，RNA测序增强了我们以转录水平的精度和单细胞分辨率量化基因表达动态的能力。这在进化生物学、发育生物学、医学转录组学以及合成生物学等不同领域都有应用。生物技术的这些进步需要在这些新数据集上执行推理的计算算法的开发方面取得相应的进展。信息论提供了一个自然的透镜来研究这些问题，因为它可以量化做出准确推断所需的数据量，以及导致最优性。该项目的主要研究目标是适应，应用和创建新的信息理论和算法方法来解决RNA序列分析中出现的推理问题。该项目还将有一个重要的教育组成部分，将这些新发现整合到研究生和本科生课程中，使电气工程和计算机科学专业的学生接触测序问题，此外还将通过推广和指导使高中和本科生接触这一研究领域。该项目将研究RNA测序两个不同层次的推理问题：组装和下游分析。RNA测序的典型方法包括将RNA片段化成短片段，然后进行测序。这个项目的第一个推力将是在研究信息的限制和算法？大会？这个问题，特别是在研究基因组中的错误和重复区域的作用。第二个重点将是研究利用单细胞RNA序列数据来理解基因调控和细胞分化的下游任务的信息限制和算法。

项目成果

C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation

• 发表时间: 2020-05
• 期刊: ArXiv
• 作者: A. Mondal;A. Bhattacharya;Sudipto Mukherjee;Sreeram Kannan;Himanshu Asnani;A. Prathosh

Fundamental Limits of Multi-Sample Flow Graph Decomposition

多样本流图分解的基本限制

• DOI: 10.1109/isit50566.2022.9834518
• 发表时间: 2022
• 期刊: IEEE International Symposium on Information Theory
• 作者: Mazooji, Kayvon;Kannan, Sreeram;Noble, William Stafford;Shomorony, Ilan
• 通讯作者: Shomorony, Ilan

CCMI : Classifier based Conditional Mutual Information Estimation

• 发表时间: 2019-06
• 作者: Sudipto Mukherjee;Himanshu Asnani;Sreeram Kannan

Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms

• 发表时间: 2018-02
• 作者: Ashok Vardhan Makkuva;P. Viswanath;Sreeram Kannan;Sewoong Oh

Deepcode: Feedback Codes via Deep Learning

• DOI: 10.1109/jsait.2020.2986752
• 发表时间: 2018-07
• 期刊: IEEE Journal on Selected Areas in Information Theory
• 作者: Hyeji Kim;Yihan Jiang;Sreeram Kannan;Sewoong Oh;P. Viswanath