Reply to Sun et al., "Identifying Composition Novelty in Microbiome Studies: Improvement of Prediction Accuracy"

Xiaoquan Su^{1

2

3}, Gongchao Jing^{4

2

3}, Daniel McDonald⁵, Honglei Wang^{4

2

3}, Zengbin Wang^{4

2

3}, Antonio Gonzalez⁵, Zheng Sun^{4

2

3}, Shi Huang^{4

2

3}, Jose Navas⁶, Rob Knight^{7

6

8

9}, Jian Xu^{1

2

3}

Affiliations

¹ Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China suxq@qibebt.ac.cn robknight@ucsd.edu xujian@qibebt.ac.cn.
² Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China.
³ University of the Chinese Academy of Sciences, Beijing, China.
⁴ Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China.
⁵ Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
⁶ Department of Computer Science & Engineering, University of California San Diego, La Jolla, California, USA.
⁷ Department of Pediatrics, University of California San Diego, La Jolla, California, USA suxq@qibebt.ac.cn robknight@ucsd.edu xujian@qibebt.ac.cn.
⁸ Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
⁹ Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA.

PMID: 31387904
PMCID: PMC6686038
DOI: 10.1128/mBio.01234-19

Comment

Reply to Sun et al., "Identifying Composition Novelty in Microbiome Studies: Improvement of Prediction Accuracy"

Xiaoquan Su et al. mBio. 2019.

. 2019 Aug 6;10(4):e01234-19.

doi: 10.1128/mBio.01234-19.

Authors

Affiliations

¹ Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China suxq@qibebt.ac.cn robknight@ucsd.edu xujian@qibebt.ac.cn.
² Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China.
³ University of the Chinese Academy of Sciences, Beijing, China.
⁴ Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Key Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China.
⁵ Department of Pediatrics, University of California San Diego, La Jolla, California, USA.
⁶ Department of Computer Science & Engineering, University of California San Diego, La Jolla, California, USA.
⁷ Department of Pediatrics, University of California San Diego, La Jolla, California, USA suxq@qibebt.ac.cn robknight@ucsd.edu xujian@qibebt.ac.cn.
⁸ Department of Bioengineering, University of California San Diego, La Jolla, California, USA.
⁹ Center for Microbiome Innovation, University of California San Diego, La Jolla, California, USA.

PMID: 31387904
PMCID: PMC6686038
DOI: 10.1128/mBio.01234-19

No abstract available

Keywords: bioinformatics; community similarity; data mining; database search; microbial ecology; microbiome; microbiome novelty; novelty; search.

PubMed Disclaimer

Figures

**FIG 1**
Comparison between the query microbiome (dorm dust) and the top hits reported by MSE-based searches. (a) Distribution of OTUs in the common phylogeny tree between the query and the top hit from the full MSE reference database. Those abundant OTUs from the *Pseudomonas* genus are marked in the red box, and the shared subbranches of the query and the hits are indicated in blue. (b) The similarities between the query sample and each of the top 10 hits against the building reference samples are significantly lower than those between the query and each of the 10 hits against the entire database, as suggested by both t test (b) and PCoA (c). PC1 and PC2, principal components 1 and 2, respectively.

See this image and copyright information in PMC

Comment on

Identifying Composition Novelty in Microbiome Studies: Improvement for Prediction Accuracy.
Sun Y, Li Y, Yuan Q, Fu X. Sun Y, et al. mBio. 2019 Jul 30;10(4):e00892-19. doi: 10.1128/mBio.00892-19. mBio. 2019. PMID: 31363024 Free PMC article. No abstract available.

References

1. Su X, Jing G, McDonald D, Wang H, Wang Z, Gonzalez A, Sun Z, Huang S, Navas J, Knight R, Xu J. 2018. Identifying and predicting novelty in microbiome studies. mBio 9:e02099-18. doi:10.1128/mBio.02099-18. - DOI - PMC - PubMed
1. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. 2012. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6:610–618. doi:10.1038/ismej.2011.139. - DOI - PMC - PubMed
1. Su X, Xu J, Ning K. 2012. Meta-Storms: efficient search for similar microbial communities based on a novel indexing scheme and similarity score for metagenomic data. Bioinformatics 28:2493. doi:10.1093/bioinformatics/bts470. - DOI - PubMed
1. Jing G, Sun Z, Wang H, Gong Y, Huang S, Ning K, Xu J, Su X. 2017. Parallel-META 3: comprehensive taxonomical and functional analysis platform for efficient comparison of microbial communities. Sci Rep 7:40371. doi:10.1038/srep40371. - DOI - PMC - PubMed
1. Hamady M, Lozupone C, Knight R. 2010. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J 4:17–27. doi:10.1038/ismej.2009.97. - DOI - PMC - PubMed

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Reply to Sun et al., "Identifying Composition Novelty in Microbiome Studies: Improvement of Prediction Accuracy"

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Reply to Sun et al., "Identifying Composition Novelty in Microbiome Studies: Improvement of Prediction Accuracy"

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