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.

高通量测序的发展已引入了分子生物学的新时代，从而可以对基因组进行廉价研究。此外，近年来，RNA测序增强了我们使用转录水平精度和单细胞分辨率量化基因表达动力学的能力。这在各种领域都有应用，例如进化生物学，发育生物学，医学转录组学以及合成生物学。这些生物技术方面的进步需要在对这些新数据集进行推断的计算算法的开发中相应的进步。信息理论提供了一种自然镜头来研究此类问题，因为它可以量化进行准确推断所需的数据量以及导致最佳性。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序列分析中引起的推理问题。该项目还将有一个重要的教育组成部分，将这些新发现集成到研究生和本科课程中，除了将高中生和本科学生暴露于该研究领域外，还可以通过外展和指导将高中生和本科生进行测序。 RNA测序的典型方法涉及将RNA分解为短片段，然后将其测序。该项目的第一个主旨是研究该组件的信息限制和算法？问题，特别是在研究错误和重复区域在基因组中的作用时。第二个推力是研究使用单细胞RNA序列数据的下游任务的信息限制和算法来了解基因调节和细胞分化。

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

Prism: Deconstructing the Blockchain to Approach Physical Limits

• DOI: 10.1145/3319535.3363213
• 发表时间: 2019-01-01
• 期刊: PROCEEDINGS OF THE 2019 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'19)
• 作者: Bagaria, Vivek;Kannan, Sreeram;Viswanath, Pramod
• 通讯作者: Viswanath, Pramod

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