•  
  •  
 

Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

microbiome; standards; big data; China Microbiome Data Center

Document Type

Article

Abstract

Microbiome is the total microbial community of certain environment. Microbiome is considered to play a crucial role on the nutrition metabolism, degradation of pollutant, maintain a balance of ecosystem of animal, plant and human beings although the fundamental mechanism is still unknown. The tremendous development of broad application of high throughput sequencing technology provides the possibility to comprehensive understanding of the composition and functions of microbiome from the view of whole genome sequencing. Microbiome has gradually become a research focus recently. The United States and EU launched national and international projects on microbiome. However, data management and high through-put data analysis still bottlenecks for microbiome research. This paper pointed out current problems for microbiome data management, including the standardization, cross-fields data integration, and high quality reference databases, summarized international microbiome projects and data platforms, and then analyzed current status and questions to be addressed by Chinese researches. Finally, the authors proposed suggestions and strategies for the development of Chinese microbiome data researches and the establishment of national data center.

First page

290

Last Page

296

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

Grice E A, Segre J A. The Human Microbiome:our second Genome. Annu Rev Genomics Hum Genet, 2012, 13(1):151-170.

Gevers D, Knight R, Petrosino J F, et al. Human Microbiome Project Consortium:A framework for human microbiome research. Nature, 2012, 486(7402):215-221.

Kurokawa K, Itoh T, Kuwahara T, et al. Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res, 2007, 14:169-181.

Choi J, Yang F, Stepanauskas R, et al. Strategies to improve reference databases for soil Microbiomes.The ISME Journal, 2016, 1-6.

Almeida M, Hébert A, Abraham A L, et al. Construction of a dairy microbial genome catalog opens new perspectives for the metagenomics analysis of dairy fermented products. BMC Genomics, 2014, 15:1101.

Stulberg E, Fravel D, Proctor L M, et al. An assessment of US microbiome research. Nat Biotechnol, 2016, 1(1):15015.

Lita M P. The National Institutes of Health Human Microbiome Project. Seminars in Fetal & Neonatal Medicine, 2016, 21(6): 368-372.

Gilbert J A, Meyer F, Jansson J, et al. The Earth Microbiome Project:Meeting report of the "1st EMP meeting on sample selection and acquisition" at Argonne National Laboratory. Standards in Genomic Sciences, 2010, 3(3):249-253.

Field D, Garrity G, Gray T, et al. The minimum information about a genome sequence (MIGS) specification. Nat Biotechnol, 2008, 26(5):541-547.

Yilmaz P, Kottmann R, Field D, et al. The "Minimum Information about an Environmental Sequence" (MIENS) specification. Nat Biotechnol, 2011, 29:415-420.

EMP.[2016-12-3]. http://www.earthmicrobiome.org/empstandard-protocols/.

Gilbert J A, Jansson J K, Knight R, et al. The Earth Microbiome project:successes and aspirations. BMC Biol, 2014, 12(1):69.

Chen I A, Markowitz V M, Chu K, et al. IMG/M:integrated genome and metagenome comparative data analysis system. Nucleic Acids Res, 2017, 45(D1):D507-D516.

Meyer F, Paarmann D, D' Souza M, et al. The metagenomics RAST server-a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 2008, 9(1):386.

Mukherjee S, Stamatis D, Bertsch J, et al. Genomes OnLine Database (GOLD) v.6:data updates and feature enhancements. Nucleic Acids Res, 2017, 45(D):D446-D456.

Field D, Sterk P, Kottmann R, et al. Genomic Standards Consortium Projects. Standards in Genomic Sciences, 2014, 9(3):599-601.

Kyrpides N C, Eloe-Fadrosh E A, Ivanova N N. Microbiome data science:understanding our microbial planet. Trends Microbiol, 2016, 24(6):425-427.

Wu L, Sun Q, Desmeth P, et al. World data centre for microorganisms:an information infrastructure to explore and utilize preserved microbial strains worldwide. Nucleic Acids Res, 2017, 45(D):D611-D618.

Zhang Y, Ji P, Wang J, et al. RiboFR-Seq:a novel approach to linking16S rRNA amplicon profiles to metagenomes. Nucleic Acids Res, 2016, 44(10):e99.

Ji P, Zhang Y, Wang J, et al. MetaSort untangles metagenome assembly by reducing microbial community complexity. Nat Commun, 2017, 8:14306.

Shi W, Ji P, Zhao F. The combination of direct and paired link graphs can boost repetitive genome assembly. Nucleic Acids Res, 2016. DOI: https://doi.org/10.1093/nar/gkw1191.

Peng G, Ji P, Zhao F. A novel codon-based de Bruijn graph algorithm for gene construction from unassembled transcriptomes. Genome Biol, 2016, 17(1):232.

Jing G, Sun Z, Wang H, et al. Parallel-META 3:Comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities. Sci Rep, 2017, 7:40371.

Su X, Xu J, Ning K. Meta-Storms:Efficient Search for Similar Microbial Communities Based on a Novel Indexing Scheme and Similarity Score for Metagenomic Data. Bioinformatics, 2012, 28(19):2493-2501.

Su X, Wang X, Jing G, et al. GPU-Meta-Storms:Computing the structure similarities among massive amount of microbial community samples using GPU. Bioinformatics, 2014, 30(7): 1031-1033.

Download
Request a Copy

Share

COinS