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Development of a metagenomic analysis method that enables whole-genome construction from metagenome using the Hi-C method

Research Project

Project/Area Number 18K19286
Research Category

Grant-in-Aid for Challenging Research (Exploratory)

Allocation TypeMulti-year Fund
Review Section Medium-sized Section 43:Biology at molecular to cellular levels, and related fields
Research InstitutionTokyo Institute of Technology

Principal Investigator

Itoh Takehiko  東京工業大学, 生命理工学院, 教授 (90501106)

Project Period (FY) 2018-06-29 – 2020-03-31
Project Status Completed (Fiscal Year 2019)
Budget Amount *help
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2018: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
KeywordsHi-C法 / メタゲノム / HI-C法 / ビンニング / HiC法 / メタゲノム解析 / ゲノムアセンブラ / Hi-C
Outline of Final Research Achievements

The Hi-C method is developed for analyzing the higher-order structure of chromosomes. The purpose of this study was to apply the Hi-C method to metagenomic analysis and binning after metagenome assembly. We succeeded in developing a binning tool exceeding the existing methods in terms of accuracy. This tool uses continuous sequences (scaffolds) assembled by the metagenomic assembler and Hi-C-derived sequence data as an input and provides the binning result as an output. First, the paired-end sequence data derived from the Hi-C method are mapped to the scaffolds. Then, a graph with each scaffold sequence as nodes and links by the paired Hi-C data as edges is created, and this graph is then solved using the Infomap method. We also confirmed the practical applicability of this method by applying it to newly acquired metagenomic data of the bovine rumen.

Academic Significance and Societal Importance of the Research Achievements

ある環境を構成する個々の細菌ゲノムの再構築を目指したメタゲノムアセンブルは幅広く実施されているが、その鍵となるのは情報解析手法である。一般的には、シークエンスデータをアセンブル後、得られた配列を特徴量に基づいてクラスタリングすることで分類し、個々の細菌ゲノムの再構築を目指す。様々なクラスタリング手法が開発されているが、アセンブル配列が短い場合には特徴量抽出が困難となり、精度高くクラスタリングすることは原理的に難しい。その点本研究で取り扱うHi-Cデータはアセンブル長に依存しないため、新たな情報量を付与することが可能となり、既存手法との組み合わせによりブレークスルーを与えることが期待される。

Report

(3 results)
  • 2019 Annual Research Report   Final Research Report ( PDF )
  • 2018 Research-status Report
  • Research Products

    (1 results)

All 2019

All Presentation (1 results)

  • [Presentation] Comprehensive detection of insertion sequences in bacterial genomes2019

    • Author(s)
      Jun Hattori, Takehiko Itoh and Yoshimura Dai
    • Organizer
      第8回生命医薬情報学連合大会IIBMP2019
    • Related Report
      2019 Annual Research Report

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Published: 2018-07-25   Modified: 2021-02-19  

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