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dc.contributor.authorIgarashi, Yoji
dc.contributor.authorMori, Daisuke
dc.contributor.authorMitsuyama, Susumu
dc.contributor.authorYoshitake, Kazutoshi
dc.contributor.authorOno, Hiroaki
dc.contributor.authorWatanabe, Tsuyoshi
dc.contributor.authorTaniuchi, Yukiko
dc.contributor.authorSakami, Tomoko
dc.contributor.authorKuwata, Akira
dc.contributor.authorKobayashi, Takanori
dc.contributor.authorIshino, Yoshizumi
dc.contributor.authorWatabe, Shugo
dc.contributor.authorGojobori, Takashi
dc.contributor.authorAsakawa, Shuichi
dc.date.accessioned2019-05-13T11:34:04Z
dc.date.available2019-05-13T11:34:04Z
dc.date.issued2019-04-29
dc.identifier.citationIgarashi Y, Mori D, Mitsuyama S, Yoshitake K, Ono H, et al. (2019) A Preliminary Metagenome Analysis Based on a Combination of Protein Domains. Proteomes 7: 19. Available: http://dx.doi.org/10.3390/proteomes7020019.
dc.identifier.issn2227-7382
dc.identifier.doi10.3390/proteomes7020019
dc.identifier.urihttp://hdl.handle.net/10754/652833
dc.description.abstractMetagenomic data have mainly been addressed by showing the composition of organisms based on a small part of a well-examined genomic sequence, such as ribosomal RNA genes and mitochondrial DNAs. On the contrary, whole metagenomic data obtained by the shotgun sequence method have not often been fully analyzed through a homology search because the genomic data in databases for living organisms on earth are insufficient. In order to complement the results obtained through homology-search-based methods with shotgun metagenomes data, we focused on the composition of protein domains deduced from the sequences of genomes and metagenomes, and we utilized them in characterizing genomes and metagenomes, respectively. First, we compared the relationships based on similarities in the protein domain composition with the relationships based on sequence similarities. We searched for protein domains of 325 bacterial species produced using the Pfam database. Next, the correlation coefficients of protein domain compositions between every pair of bacteria were examined. Every pairwise genetic distance was also calculated from 16S rRNA or DNA gyrase subunit B. We compared the results of these methods and found a moderate correlation between them. Essentially, the same results were obtained when we used partial random 100 bp DNA sequences of the bacterial genomes, which simulated raw sequence data obtained from short-read next-generation sequences. Then, we applied the method for analyzing the actual environmental data obtained by shotgun sequencing. We found that the transition of the microbial phase occurred because the seasonal change in water temperature was shown by the method. These results showed the usability of the method in characterizing metagenomic data based on protein domain compositions.
dc.description.sponsorshipThis work was supported by CREST (Core Research for Evolutional Science and Technology) of the Japan Science and Technology Corporation (JST).
dc.publisherMDPI AG
dc.relation.urlhttps://www.mdpi.com/2227-7382/7/2/19
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectprotein domain
dc.subjectcorrelation coefficient
dc.subjectphylogenetic analysis
dc.subjectmetagenomics
dc.subjectenvironmental DNA
dc.titleA Preliminary Metagenome Analysis Based on a Combination of Protein Domains
dc.typeArticle
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentBioscience Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalProteomes
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Aquatic Bioscience, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo 113-8657, Japan
dc.contributor.institutionJapan Software Management Co, Ltd., Yokohama, Kanagawa 221-0056, Japan
dc.contributor.institutionTohoku National Fisheries Research Institute, Japan Fisheries Research and Education Agency, Shiogama, Miyagi 985-0001, Japan
dc.contributor.institutionHokkaido National Fisheries Research Institute, Japan Fisheries Research and Education Agency, Kushiro, Hokkaido 085-0802, Japan
dc.contributor.institutionResearch Center for Aquaculture Systems, National Research Institute of Aquaculture, Japan Fisheries Research and Education Agency, Minami-ise, Mie 516-0193, Japan
dc.contributor.institutionNational Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency, Yokohama, Kanagawa 236-8648, Japan
dc.contributor.institutionGraduate School of Bioresorce and Bioenvironmental Sciences, Kyushu University, Fukuoka, Fukuoka 812-0053, Japan
dc.contributor.institutionSchool of Marine Biosciences, Kitasato University, Sagamihara, Kanagawa 252-0373, Japan
kaust.personGojobori, Takashi
refterms.dateFOA2019-05-14T06:48:53Z


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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Except where otherwise noted, this item's license is described as This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).