Robust and efficient statistical inference methods for genomics

稳健且高效的基因组学统计推断方法

• 批准号: 10308395
• 负责人: Yun S Song
• 金额: $ 36.79万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10308395
• 关键词: Attention; Basic Science; Big Data; Biological; Biological Assay; Biological Process; Biology; Biomedical Research; Catalogs; Cells; Communities; Complex; Computers; Computing Methodologies; Data; Data Analyses; Development; Fruit; Gene Expression Profiling; Genetic; Genetic Models; Genetic Transcription; Genetic Translation; Genetic Variation; Genomics; Goals; Human Biology; Immunology; Metagenomics; Methods; Modeling; Population; Population Genetics; Population Heterogeneity; Research; Research Personnel; Scientist; Statistical Methods; Transcription Process; Translation Process; Translations; analytical tool; base; computerized tools; disorder risk; epigenetic variation; genomic data; human disease; mathematical analysis; mathematical model; novel; programs; sound; structural biology; tool

PROJECT SUMMARY The Song Lab consists of computer scientists, statisticians, and mathematicians who are fully committed to ad- vancing biology. We develop efficient computational tools and robust statistical methods to facilitate the research of the broad biomedical community, while also getting deeply involved in data analysis to make new biological discoveries. In particular, we have been making notable contributions to the field of population genomics, where we have obtained significant theoretical results and developed useful inference tools that are generalizable to complex models and scalable to big data. In the past five years, our research has branched out to other ar- eas of genomics, including bulk and single-cell gene expression analysis; mRNA translation dynamics; structural biology; immunology; and metagenomics. Technological advances in sequencing and experimental assays have greatly increased the availability of various kinds of genomic data, enabling us to catalog genetic and epigenetic variation in diverse populations, and to probe fundamental biological processes (e.g., transcription and translation) in unprecedented detail. This development is providing a number of new opportunities for basic and biomedical research, but often the data are noisy and multifaceted, while the underlying biology is very complex, thus presenting both theoretical and computational challenges for analysis and interpretation. New efficient and robust statistical inference tools, as well as theoretical analysis of mathematical models, are much in need of development to bring the promise of the big data era in biology to full fruition. The central goal of our research program is to meet these important challenges. Over the next five years, we will continue to carry out basic research in both population genomics and computa- tional genomics, and develop a suite of useful analytical tools, paying attention to sound mathematical modeling, rigorous statistical estimation, and computational scalability. In particular, we will tackle several key technical challenges in population genomics, and develop both likelihood-based and likelihood-free methods to enable in- ference under more complicated, realistic models than previously possible. We will also develop novel inference methods to analyze, integrate, and interpret various types of genomic data, and carry out theoretical analysis of mathematical models to elucidate the intricate details of both transcription and translation processes. In addition, we will continue to collaborate with empirical and experimental biologists to pursue basic research questions in biology, as we have done fruitfully in the past.

项目总结 宋实验室由计算机科学家、统计学家和数学家组成，他们完全致力于ad. 令人兴奋的生物学。我们开发了有效的计算工具和稳健的统计方法来促进研究 广泛的生物医学界，同时也深入参与数据分析，以做出新的生物学 发现。特别是，我们在种群基因组学的fi领域做出了显著贡献，在那里 我们已经得到了有意义的理论结果，并开发了有用的推理工具，这些工具可以推广到 模型复杂，可扩展到大数据。在过去的几年里，我们的研究已经扩展到其他领域--fi 基因组学的EAS，包括整体和单细胞基因表达分析；信使核糖核酸翻译动态；结构 生物学、免疫学和元基因组学。 测序和实验分析方面的技术进步大大提高了可获得性 各种基因组数据，使我们能够对不同种群的遗传和表观遗传变异进行分类， 并以前所未有的细节探索基本的生物过程(例如转录和翻译)。这 发展为基础和生物医学研究提供了许多新的机会，但数据往往 是嘈杂的和多方面的，而潜在的生物学是非常复杂的，因此呈现出理论和 分析和解释的计算挑战。新的高效和健壮的统计推断工具，如 以及理论分析的数学模型，都是亟待发展的前景所在 生物学的大数据时代全面开花结果。我们研究计划的中心目标是满足这些重要的 挑战。 在接下来的几年里，我们将继续在种群基因组学和计算方面进行基础研究。 传统基因组学，并开发了一套有用的分析工具，注重合理的数学建模， 严格的统计估计和计算可伸缩性。特别是，我们将解决几个关键技术问题 在种群基因组学方面的挑战，并开发基于可能性和无可能性的方法，以使在 费伦斯在比以前更复杂、更现实的模型下。我们还将开发新的推理 方法分析、整合和解释各种类型的基因组数据，并进行理论分析 数学模型，以阐明转录和翻译过程的复杂细节。此外, 我们将继续与实验生物学家和实验生物学家合作，在 生物学，就像我们在过去卓有成效地做的那样。