Next Generation Computational Tools for Functional Genomics

下一代功能基因组学计算工具

• 批准号: 10448436
• 负责人: Rafael Angel Irizarry
• 金额: $ 69.86万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-22 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10448436
• 关键词: ATAC-seq; Address; Affect; Algorithms; Area; Automobile Driving; Bar Codes; Base Sequence; Binding; Bioconductor; Biological; Cells; ChIP-seq; Chromatin; Communities; Complex; Computer software; Data; Data Analytics; Data Set; Deoxyribonucleases; Development; Disease; Elements; Gene Expression; Gene Expression Regulation; Genes; Genomics; Individual; Intuition; Joints; Knowledge; Maps; Measurement; Methodology; Methods; Modeling; Morphologic artifacts; Nature; Outcome; Paper; Performance; Process; Proteins; Protocols documentation; Pythons; RNA; Regulatory Element; Research; Research Personnel; Signal Transduction; Sorting - Cell Movement; Source; Speed; Statistical Models; Technology; Time; Tissues; Variant; Work; base; cell type; computerized tools; data integration; experience; functional genomics; genomic data; high throughput analysis; high throughput technology; improved; large datasets; new technology; next generation; next generation sequencing; open source; response; single cell technology; single-cell RNA sequencing; tool; transcription factor; user-friendly; whole genome

PROJECT SUMMARY During the last decade, Next Generation Sequencing (NGS) applications have expanded to include measurement of dynamic outcomes underlying genomic function in development and disease. Measurements related to functional elements that act at the protein and RNA levels, and regulatory elements that control gene activity, are at the core of studies undertaken by large consortia and individual labs alike. These measurements introduce levels of variability that give rise to data analytic challenges related to distinguishing unwanted or uninterested sources of variability, from biologically relevant signals. Furthermore, new technologies and improved data analytic ideas are giving rise to a need for new mapping algorithms to facilitate deployment on increasingly larger datasets. While existing tools have provided effective ways to process and analyze data in functional genomics studies, new technologies, more complex biological questions, and the availability of increasingly complete datasets are posing new challenges. Single cell RNA-seq and single cell ATAC-seq technologies in particular have introduced complexities that current tools are not optimized to address. Our team has extensive experience developing computational tools and statistical methodology for functional genomics, disseminated as open source software. Many of our methods have become standards among users of high-throughput technologies and are commonly included as part of standard pipelines. Combined, these software packages receive hundreds of thousands of downloads each year and the papers describing the methods have been cited tens of thousands of times. Furthermore, Dr. Irizarry (PI) is a leader in the Bioconductor project, one of the most widely used open-source projects for the analysis of high-throughput genomics data which has greatly facilitated the development and dissemination of our and others state-of-the-art statistical methodologies. We have identified three specific computational challenges urgently requiring new or improved solutions that can greatly benefit from our expertise. Namely, we propose to develop: fast and accurate read mapping specialized for count-focused sequencing data; develop a unified statistical approach for normalization and downstream analysis​; ​developing computational tools to integrate scATAC-seq data with scRNA-seq and using public data to facilitate annotation and functional interpretation. We plan to disseminate our tools via open source software and provide a user friendly suite of packages that functional genomics researchers can use to extract knowledge from their single cell RNA-seq or ATAC-seq data.

项目总结 在过去的十年中，下一代测序(NGS)应用已经扩展到包括 测量发育和疾病中潜在的基因组功能的动态结果。测量结果 与在蛋白质和RNA水平上起作用的功能元件以及控制基因的调节元件有关 活动，是大财团和单个实验室进行的研究的核心。这些 测量引入了可变性水平，这导致了与区分相关的数据分析挑战 来自生物相关信号的不想要的或不感兴趣的可变性来源。此外，新的 技术和改进的数据分析思想催生了对新的映射算法的需求，以促进 部署在越来越大的数据集上。虽然现有工具提供了有效的方法来处理和 分析功能基因组研究中的数据，新技术，更复杂的生物学问题，以及 越来越完整的数据集的可用性带来了新的挑战。单细胞RNA-seq和单细胞 尤其是atac-seq技术引入了当前工具没有针对其进行优化的复杂性。 地址。 我们的团队在开发计算工具和统计方法方面拥有丰富的经验 基因组学，作为开源软件传播。我们的许多方法已经成为用户的标准 作为高通量技术的一部分，通常作为标准管道的一部分。加在一起，这些 软件包每年获得数十万的下载量，描述 这些方法已经被引用了数万次。此外，Irizarry博士(PI)是 BioConductor项目，用于高通量分析的最广泛使用的开源项目之一 基因组学数据，极大地促进了我们和其他人的发展和传播 最先进的统计方法。 我们已经确定了三个特定的计算挑战，迫切需要新的或改进的解决方案， 可以极大地受益于我们的专业知识。也就是说，我们建议开发：快速准确的读图 专门处理以计数为重点的测序数据；开发统一的统计方法进行标准化和 下游分析​；​开发计算工具以将scatac-seq数据与scrna-seq整合，并使用 公共数据，以方便注释和功能解释。我们计划通过开放的方式传播我们的工具 源码软件，并提供一套用户友好的软件包，供功能基因组学研究人员使用 从他们的单细胞RNA-seq或atac-seq数据中提取知识。