Shotgun and Hi-C Sequencing Datasets for Binning Wheat Rhizosphere Microbiome

被引:0
|
作者
Regmi, Roshan [1 ,2 ]
Anderson, Jonathan [1 ,3 ,4 ]
Burgess, Lauren [5 ]
Mangelson, Hayley [5 ]
Liachko, Ivan [5 ]
Vadakattu, Gupta [1 ,2 ]
机构
[1] CSIRO, Microbiomes One Syst Hlth MOSH, Adelaide, Australia
[2] CSIRO, Agr & Food, Urrbrae, SA, Australia
[3] CSIRO, Agr & Food, Floreat, WA, Australia
[4] Univ Western Australia, UWA Inst Agr, Crawley, WA, Australia
[5] Phase Genom, Seattle, WA USA
关键词
METAGENOMIC CONTIGS; GENOMES; ACQUISITION; ALIGNMENT; EVOLUTION; COVERAGE; ELEMENTS;
D O I
10.1038/s41597-025-04651-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Binning is a crucial process in metagenomics studies, where sequenced reads are combined to form longer contigs and assigned to individual genomes. Conventional methods, such as shotgun binning, rely on similarity measurements and abundance profiles across multiple samples. However, cost constraints for sequencing and limited sample collection capacity hinder their effectiveness. High-throughput chromosome conformation capture (Hi-C), a DNA proximity ligation technique, has been adapted to accurately bin metagenome-assembled genomes (MAGs) from a single sample, addressing challenges like chimeric MAGs. In this study, we generated over 190 Gb of metagenomic data from wheat rhizospheres grown in two highly calcareous soils of South Australian region and compared conventional and Hi-C binning methods. Two shotgun metagenomes and Hi-C libraries were generated, assembling 1089 shotgun MAGs across 39 bacterial and one archaeal taxon, including 94 Hi-C based bins. Binning performed using only short read sequences was prone to high contamination, while the addition of Hi-C binning improved MAG quality and identified mobile element-host-infection interaction. This dataset provides important tools for studying microbial communities in wheat rhizosphere soils.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] HolistIC: leveraging Hi-C and whole genome shotgun sequencing for double minute chromosome discovery
    Hayes, Matthew
    Nguyen, Angela
    Islam, Rahib
    Butler, Caryn
    Tran, Ethan
    Mullins, Derrick
    Hicks, Chindo
    BIOINFORMATICS, 2022, 38 (05) : 1208 - 1215
  • [2] The first Taxus rhizosphere microbiome revealed by shotgun metagenomic sequencing
    Hao, Da-Cheng
    Zhang, Cai-Rong
    Xiao, Pei-Gen
    JOURNAL OF BASIC MICROBIOLOGY, 2018, 58 (06) : 501 - 512
  • [3] The Immense Functional Attributes of Maize Rhizosphere Microbiome: A Shotgun Sequencing Approach
    Akinola, Saheed Adekunle
    Ayangbenro, Ayansina Segun
    Babalola, Olubukola Oluranti
    AGRICULTURE-BASEL, 2021, 11 (02): : 1 - 14
  • [4] GrapHi-C: Graph-based visualization of Hi-C datasets
    MacKay K.
    Kusalik A.
    Eskiw C.H.
    BMC Research Notes, 11 (1)
  • [5] Serpentine: a flexible 2D binning method for differential Hi-C analysis
    Baudry, Lyam
    Millot, Gael A.
    Thierry, Agnes
    Koszul, Romain
    Scolari, Vittore F.
    BIOINFORMATICS, 2020, 36 (12) : 3645 - 3651
  • [6] dcHiC detects differential compartments across multiple Hi-C datasets
    Abhijit Chakraborty
    Jeffrey G. Wang
    Ferhat Ay
    Nature Communications, 13
  • [7] dcHiC detects differential compartments across multiple Hi-C datasets
    Chakraborty, Abhijit
    Wang, Jeffrey G.
    Ay, Ferhat
    NATURE COMMUNICATIONS, 2022, 13 (01)
  • [8] HiCBricks: building blocks for efficient handling of large Hi-C datasets
    Pal, Koustav
    Tagliaferri, Ilario
    Livi, Carmen Maria
    Ferrari, Francesco
    BIOINFORMATICS, 2020, 36 (06) : 1917 - 1919
  • [9] Metagenomic Hi-C of a Healthy Human Fecal Microbiome Transplant Donor
    DeMaere, Matthew Z.
    Liu, Michael Y. Z.
    Lin, Enmoore
    Djordjevic, Steven P.
    Charles, Ian G.
    Worden, Paul
    Burke, Catherine M.
    Monahan, Leigh G.
    Gardiner, Melissa
    Borody, Thomas J.
    Darling, Aaron E.
    MICROBIOLOGY RESOURCE ANNOUNCEMENTS, 2020, 9 (06):
  • [10] HiCcompare: an R-package for joint normalization and comparison of HI-C datasets
    John C. Stansfield
    Kellen G. Cresswell
    Vladimir I. Vladimirov
    Mikhail G. Dozmorov
    BMC Bioinformatics, 19