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: 10063943
• 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/10063943
• 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.

项目摘要 宋实验室由计算机科学家、统计学家和数学家组成，他们完全致力于广告， 先锋生物学我们开发有效的计算工具和强大的统计方法，以促进研究 广泛的生物医学界，同时也深入参与数据分析，使新的生物 发现。特别是，我们一直在人口基因组学领域做出显着贡献， 我们已经获得了重要的理论结果，并开发了有用的推理工具，这些工具可推广到 复杂的模型和可扩展到大数据。在过去的五年里，我们的研究已经扩展到其他领域， 基因组学的eas，包括批量和单细胞基因表达分析; mRNA翻译动力学;结构 生物学、免疫学和宏基因组学。 测序和实验测定中的技术进步极大地增加了生物测定的可用性。 各种各样的基因组数据，使我们能够在不同的人群中对遗传和表观遗传变异进行分类， 并探测基本的生物过程（例如，翻译和翻译，以前所未有的细节。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 是嘈杂和多方面的，而基础生物学是非常复杂的，因此提出了理论和 分析和解释的计算挑战。新的高效和强大的统计推断工具，如 以及数学模型的理论分析，都非常需要发展， 生物学的大数据时代取得丰硕成果。我们的研究计划的中心目标是满足这些重要的 挑战 未来五年，我们将继续开展人口基因组学和计算遗传学的基础研究， 传统基因组学，并开发一套有用的分析工具，注意健全的数学建模， 严格的统计估计和计算可扩展性。特别是，我们将解决几个关键技术 人口基因组学的挑战，并开发基于可能性和无可能性的方法，使在 比以前更复杂，更现实的模型。我们还将开发新的推理 方法来分析，整合和解释各种类型的基因组数据，并进行理论分析， 数学模型来阐明转录和翻译过程的复杂细节。此外，本发明还提供了一种方法， 我们将继续与经验和实验生物学家合作，在以下方面探索基础研究问题： 生物学，就像我们过去卓有成效地做的那样。