IOBIO: Web-based, interactive tools for real-time analysis in genomic big data

IOBIO：基于网络的交互式工具，用于基因组大数据的实时分析

• 批准号: 9311909
• 负责人: Gabor T Marth
• 金额: $ 74.77万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311909
• 关键词: Address; Adopted; Affect; Algorithms; Area; Big Data; Bioinformatics; Biological; Biopsy; Cancer Patient; Chest; Clinic; Clinical; Communicable Diseases; Communities; Computer Hardware; Computer software; Computers; Custom; Data; Data Analyses; Data Quality; Data Set; Data Storage and Retrieval; Development; Disease; Disease Progression; Ecosystem; Environment; Evolution; Galaxy; Genes; Genetic; Genomic medicine; Genomics; Hour; Individual; Inherited; Internet; Intuition; Investigation; Libraries; Malignant Neoplasms; Metagenomics; Oncologist; Online Systems; Output; Patients; Process; Quality Control; Research; Research Infrastructure; Research Personnel; Resources; Sampling; Scientist; Secure; Series; Services; Somatic Mutation; System; Systems Analysis; Technology; Testing; Time; Training; Variant; base; big biomedical data; biomedical scientist; cancer diagnosis; cancer genome; cancer genomics; computing resources; exhaustion; exome; experience; flexibility; genome-wide; genomic data; interactive tool; laptop; novel; task analysis; tool; tumor; web app; web interface; web-accessible; whole genome

Genomic analyses have the potential to revolutionize the way inherited disease, infectious disease, and cancer are diagnosed and treated. However, existing genomic analysis tools have been optimized for processing whole genome or exome datasets from end to end, requiring bioinformatics training, expensive computer hardware and data storage, rendering them unavailable to many biomedical researchers who don't have access to such resources. These tools take hours or days to complete analysis, and produce large, static output files that require considerable expertise to interpret. This exhaustive “top-down” approach does not provide individual researchers with the means to quickly examine and troubleshoot their datasets, or test hypotheses formulated at the bench or in the clinic. Thus, existing genomic analysis tools do not adequately reach the end users, e.g. research clinicians who need them most to affect major advances in genomic medicine, but because of their clinical duties have the least amount and most fragmented time for their research. We are developing iobio (http://iobio.io), a novel genomic analysis system that will enable biomedical professionals without computational resources to access, and interactively analyze biomedical big data at the genome scale, using only a laptop computer. Instead of analyzing complete genomic datasets end to end, each iobio app performs focused genomic analyses (e.g. in the region of a gene) and returns the results in seconds. Results are displayed visually using a sophisticated and intuitive web interface, allowing scientists to quickly process their data using web server versions of the same powerful UNIX tools used in end- to-end genomic analyses but without the need for computing hardware and tool installation, visualize their results, expand or refine and immediately repeat to customize their analysis strategy. The iobio toolkit (http://iobio.io) currently includes four full-featured web apps, already facilitating sophisticated inherited variant prioritization and metagenomic analyses. Our growing user base is already in the thousands, many of them returning “customers” who have incorporated our apps into their analysis routine. Here, we propose to vastly expand our existing tool chest for supporting cancer genomic investigation. Cancer genomes are highly variable from patient to patient, requiring customizable analyses, tasks ideally suited for our interactive iobio web tools. Realizing that our team alone will not be able to develop and maintain tools for every task in every subdomain of genomics research, we will build extensive software libraries to support iobio app development by third-party developer groups. We will also develop flexible options for operating our tools efficiently and securely, on local server hardware or in computational cloud environments. iobio will grow into a rich and vibrant analysis ecosystem that will empower biomedical researchers at all levels of bioinformatics expertise, computationally skilled researchers and bench scientists, to carry out intuitive data analyses that are difficult to accomplish with existing genomic analysis tools.

基因组分析有可能彻底改变遗传病、传染病和癌症的治疗方式