遗传 ›› 2019, Vol. 41 ›› Issue (9): 875-882.doi: 10.16288/j.yczz.19-121

• 资源与平台 • 上一篇    下一篇

从作物基因组分析到整合组学知识库建设

梁承志1,2   

  1. 1. 中国科学院遗传与发育生物学研究所,植物基因组学国家重点实验室,种子创新研究院,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2019-06-14 修回日期:2019-08-12 出版日期:2019-09-20 发布日期:2019-08-26
  • 作者简介:梁承志,博士,研究员,研究方向:基因组大数据分析。E-mail: cliang@genetics.ac.cn
  • 基金资助:
    中国科学院重点部署项目资助编号(ZDRW-ZS-2019-2-0105)

From genome analysis to construction of an integrated omics knowledgebase for crops

Chengzhi Liang1,2   

  1. 1. State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-06-14 Revised:2019-08-12 Online:2019-09-20 Published:2019-08-26
  • Supported by:
    Supported by the Key Program of Chinese Academy of Sciences No(ZDRW-ZS-2019-2-0105)

摘要:

高通量技术的广泛应用使得各类组学数据的产出速度越来越快,由此产生的海量数据蕴藏着大量的基因组变异和相关功能信息。如何对这些数据进行深度整合和利用将会是一个长期而艰巨的任务,这需要具备高效的数据存储、分析和挖掘的能力。在过去几年中,本课题组通过与所内外课题组的合作,在多个植物的基因组的组装、注释、比较基因组和群体基因组分析等方面进行了探索,同时也将大量的水稻种质信息和组学数据进行了整合,存储于结构化数据库中并开发了一些相应的网络查询展示和数据挖掘工具。本文对相关的研究成果及其进展进行了概括性介绍,并展望了下一步的目标:构建一个用于支持作物功能基因组学和分子设计育种研究的整合组学知识库。

关键词: 基因组分析, 数据库, 组学大数据, 分子设计育种, 作物, 组学知识库

Abstract:

The advances in high-throughput technologies have enabled high-speed accumulation of omics data, which contain a large amount of genetic variations and their functional information. The integration and deep utilization of those data will be a long-term and difficult task, which requires highly efficient data storage and powerful data analysis and mining tools. In the past several years, our group has conducted multi-level genomic analyses in several plants, including genome assembly and annotation, comparative and population genomic studies, through collaboration with other labs inside and outside of our institution. Meanwhile, we have integrated a large amount of rice germplasm information and omics data into a structural database and developed related data query, visual display and mining web tools. Here, we summarize some of those results and discuss our next goal to construct an integrated omics knowledgebase for crops to support functional genomics and molecular design breeding.

Key words: genome analysis, database, big omics data, molecular design breeding, crop, omics knowledgebase