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Next Generation Computational Tools for Functional Genomics

下一代功能基因组学计算工具

基本信息

批准号：
9979396
负责人：
Rafael Angel Irizarry
金额：
$ 66.55万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
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/9979396
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

项目摘要

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