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Prediction of Plausible Bacterial Composition Based on Terminal Restriction Fragment Length Polymorphisms using a Monte Carlo Method

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Abstract

We have developed a new approach for the estimation of bacterial proportional compositions in microbiota based on terminal restriction length polymorphism (T-RFLP) data and a Monte Carlo algorithm. This program estimates proportional compositions by minimizing distances between peak values and the relative abundance of each group, containing several species, estimated from peak areas of capillary electrophoresis for T-RFLP analysis. Oral bacteria in 36 saliva samples obtained from three individuals were analyzed using the program. Upon comparison, the estimated proportional composition obtained from one of the samples matched that from a clone library. Additionally, comparisons among the bacterial proportional compositions of saliva samples obtained from three individuals four times per day for 3 days revealed that the types of microbiota present in each individual did not change within each 24-h time period and were distinguishable from those in other individuals.

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Acknowledgements

This work was supported in part by Grants-in-Aid for Scientific Research 21592652 (Y.N.), 19390541 (Y.Y.), and 21659486 (Y.Y.) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.

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  1. Yoshio Nakano

    Present address: Department of Chemistry, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan

Authors and Affiliations

  1. Section of Preventive Dentistry, Division of Oral Health, Growth and Development, Faculty of Dental Science, Kyushu University, Fukuoka, Japan

    Yoshio Nakano, Toru Takeshita, Masaki Yasui & Yoshihisa Yamashita

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  1. Yoshio Nakano
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  2. Toru Takeshita
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  3. Masaki Yasui
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  4. Yoshihisa Yamashita
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Correspondence to Yoshio Nakano.

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Nakano, Y., Takeshita, T., Yasui, M. et al. Prediction of Plausible Bacterial Composition Based on Terminal Restriction Fragment Length Polymorphisms using a Monte Carlo Method. Microb Ecol 60, 364–372 (2010). https://doi.org/10.1007/s00248-010-9703-9

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  • DOI: https://doi.org/10.1007/s00248-010-9703-9

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