metaBIT, an integrative and automated metagenomic pipeline for analysing microbial profiles from high-throughput sequencing shotgun data

被引:28
|
作者
Louvel, Guillaume [1 ]
Sarkissian, Clio Der [1 ]
Hanghoj, Kristian [1 ]
Orlando, Ludovic [1 ,2 ]
机构
[1] Univ Copenhagen, Nat Hist Museum Denmark, Ctr Geogenet, Voldgade 5-7, DK-1350 Copenhagen, Denmark
[2] Univ Toulouse, UPS, Lab AMIS, CNRS,UMR 5288, 37 Allees Jules Guesde, F-31000 Toulouse, France
基金
新加坡国家研究基金会;
关键词
ancient DNA; metagenomics; microbial profiling; microbiome; shotgun sequencing; GENOME SEQUENCE; GUT MICROBIOTA; ANCIENT; DNA; COMMUNITIES; DIVERSITY; SAMPLES; TIME;
D O I
10.1111/1755-0998.12546
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Micro-organisms account for most of the Earth's biodiversity and yet remain largely unknown. The complexity and diversity of microbial communities present in clinical and environmental samples can now be robustly investigated in record times and prices thanks to recent advances in high-throughput DNA sequencing (HTS). Here, we develop metaBIT, an open-source computational pipeline automatizing routine microbial profiling of shotgun HTS data. Customizable by the user at different stringency levels, it performs robust taxonomy-based assignment and relative abundance calculation of microbial taxa, as well as cross-sample statistical analyses of microbial diversity distributions. We demonstrate the versatility of metaBIT within a range of published HTS data sets sampled from the environment (soil and seawater) and the human body (skin and gut), but also from archaeological specimens. We present the diversity of outputs provided by the pipeline for the visualization of microbial profiles (barplots, heatmaps) and for their characterization and comparison (diversity indices, hierarchical clustering and principal coordinates analyses). We show that metaBIT allows an automatic, fast and user-friendly profiling of the microbial DNA present in HTS shotgun data sets. The applications of metaBIT are vast, from monitoring of laboratory errors and contaminations, to the reconstruction of past and present microbiota, and the detection of candidate species, including pathogens.
引用
收藏
页码:1415 / 1427
页数:13
相关论文
共 50 条
  • [41] Microbial Diversity Analysis of Sufu from Different Origins Based on High-throughput Sequencing
    Fu R.
    Liu C.
    Xu L.
    Zhang H.
    Xia T.
    Chen W.
    Science and Technology of Food Industry, 2023, 44 (02) : 134 - 142
  • [42] A high-throughput pipeline for DNA/RNA/small RNA purification from tissue samples for sequencing
    Xu, Jing
    Pandoh, Pawan K.
    Corbett, Richard D.
    Smailus, Duane
    Bowlby, Reanne
    Brooks, Denise
    McDonald, Helen
    Haile, Simon
    Chahal, Sundeep
    Bilobram, Steve
    Mungall, Karen L.
    Mungall, Andrew J.
    Coope, Robin
    Moore, Richard A.
    Zhao, Yongjun
    Jones, Steven J. M.
    Marra, Marco A.
    BIOTECHNIQUES, 2023, 75 (02) : 47 - 55
  • [43] Evaluation of a Transposase Protocol for Rapid Generation of Shotgun High-Throughput Sequencing Libraries from Nanogram Quantities of DNA
    Marine, Rachel
    Polson, Shawn W.
    Ravel, Jacques
    Hatfull, Graham
    Russell, Daniel
    Sullivan, Matthew
    Syed, Fraz
    Dumas, Michael
    Wommack, K. Eric
    APPLIED AND ENVIRONMENTAL MICROBIOLOGY, 2011, 77 (22) : 8071 - 8079
  • [44] eccDNA-pipe: an integrated pipeline for identification, analysis and visualization of extrachromosomal circular DNA from high-throughput sequencing data
    Fang, Minghao
    Fang, Jingwen
    Luo, Songwen
    Liu, Ke
    Yu, Qiaoni
    Yang, Jiaxuan
    Zhou, Youyang
    Li, Zongkai
    Sun, Ruoming
    Guo, Chuang
    Qu, Kun
    BRIEFINGS IN BIOINFORMATICS, 2024, 25 (02)
  • [45] reconCNV: interactive visualization of copy number data from high-throughput sequencing
    Chandramohan, Raghu
    Kakkar, Nipun
    Roy, Angshumoy
    Parsons, D. Williams
    BIOINFORMATICS, 2021, 37 (08) : 1164 - 1167
  • [46] Finding sRNA generative locales from high-throughput sequencing data with NiBLS
    MacLean, Daniel
    Moulton, Vincent
    Studholme, David J.
    BMC BIOINFORMATICS, 2010, 11
  • [47] Recent advances in inferring viral diversity from high-throughput sequencing data
    Posada-Cespedes, Susana
    Seifert, David
    Beerenwinkel, Niko
    VIRUS RESEARCH, 2017, 239 : 17 - 32
  • [48] Finding sRNA generative locales from high-throughput sequencing data with NiBLS
    Daniel MacLean
    Vincent Moulton
    David J Studholme
    BMC Bioinformatics, 11
  • [49] Whole Genome Mapping with Feature Sets from High-Throughput Sequencing Data
    Pan, Yonglong
    Wang, Xiaoming
    Liu, Lin
    Wang, Hao
    Luo, Meizhong
    PLOS ONE, 2016, 11 (09):
  • [50] A Bayesian Framework to Identify Methylcytosines from High-Throughput Bisulfite Sequencing Data
    Xie, Qing
    Liu, Qi
    Mao, Fengbiao
    Cai, Wanshi
    Wu, Honghu
    You, Mingcong
    Wang, Zhen
    Chen, Bingyu
    Sun, Zhong Sheng
    Wu, Jinyu
    PLOS COMPUTATIONAL BIOLOGY, 2014, 10 (09)