Large-scale 16S gene assembly using metagenomics shotgun sequences

Zeng, Feng; Wang, Zicheng; Wang, Ying; Zhou, Jizhong; Chen, Ting

doi:10.1093/bioinformatics/btx018

Large-scale 16S gene assembly using metagenomics shotgun sequences

Journal Article · Tue Jan 31 00:00:00 EST 2017 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btx018· OSTI ID:1567080

Zeng, Feng ^[1]; Wang, Zicheng ^[2]; Wang, Ying ^[3]; Zhou, Jizhong ^[4]; Chen, Ting ^[5]

Xiamen Univ. (China)
Tsinghua Univ., Beijing (China)
Department of Automation, Xiamen University, Xiamen, Fujian, China
Univ. of Oklahoma, Norman, OK (United States); Tsinghua Univ., Beijing (China); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Tsinghua Univ., Beijing (China); Univ. of Southern California, Los Angeles, CA (United States)

Combining a 16S rRNA (16S) gene database with metagenomic shotgun sequences allows unbiased identification of known and novel microbes.To attain this, we herein report reference-based ribosome assembly (RAMBL), a computational pipeline, which integrates taxonomic tree search and Dirichlet process clustering to reconstruct full-length 16S gene sequences from metagenomic sequencing data with high accuracy. By benchmarking against the synthetic and real shotgun sequences, we demonstrated that full-length 16S gene assemblies of RAMBL were a good proxy for known and putative microbes, including Candidate Phyla Radiation. Here, we found that 30-40% of bacteria genera in the terrestrial and intestinal biomes have no closely related genome sequences. We also observed that RAMBL was able to generate a more accurate determination of environmental microbial diversity and yield better disease classification, suggesting that full-length 16S gene assemblies are a powerful alternative to marker gene set and 16S short reads. RAMBL first realizes the access to full-length 16S gene sequences in the near-terabase-scale metagenomic shotgun sequences, which markedly improve metagenomic data analysis and interpretation.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Natural Science Foundation of China (NNSFC); USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1567080

Journal Information:: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 10 Vol. 33; ISSN 1367-4803

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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