関連論文

[1]

Takayama, Tadaka, Yano, Katsuoka, Gocho, Funayama, Makino, Okamura, Kikuchi, Sugimoto, Kawashima, Otsuki, Sakurai-Yageta, Yasuda, Kure, Kinoshita, Yamamoto, and Tamiya. Construction and integration of three de novo japanese human genome assemblies toward a population-specific reference. Nature communications, 2021. doi:10.1038/s41467-020-20146-8.

[2]

Tadaka, Katsuoka, Ueki, Kojima, Makino, Saito, Otsuki, Gocho, Sakurai-Yageta, Danjoh, Motoike, Yamaguchi-Kabata, Shirota, Koshiba, Nagasaki, Minegishi, Hozawa, Kuriyama, Shimizu, Yasuda, Fuse, Tamiya, Yamamoto, and Kinoshita. 3.5kjpnv2: an allele frequency panel of 3552 japanese individuals including the x chromosome. Human genome variation, 2019. doi:10.1038/s41439-019-0059-5.

[3]

Otsuki, Okamura, Ishida, Tadaka, Takayama, Kumada, Kawashima, Taguchi, Minegishi, Kuriyama, Tamiya, Kinoshita, Katsuoka, and Yamamoto. Construction of a trio-based structural variation panel utilizing activated t lymphocytes and long-read sequencing technology. Communications biology, 2022. doi:10.1038/s42003-022-03953-1.

[4]

Sakurai-Yageta, Kumada, Gocho, Makino, Uruno, Tadaka, Motoike, Kimura, Ito, Otsuki, Narita, Kudo, Aoki, Danjoh, Yasuda, Kawame, Minegishi, Koshiba, Fuse, Tamiya, Yamamoto, and Kinoshita. Japonica array neo with increased genome-wide coverage and abundant disease risk snps. Journal of biochemistry, 2021. doi:10.1093/jb/mvab060.

[5]

Fuse, Sakurai-Yageta, Katsuoka, Danjoh, Shimizu, Tamiya, Nagami, Kawame, Higuchi, Kinoshita, Kure, and Yamamoto. Establishment of integrated biobank for precision medicine and personalized healthcare: the tohoku medical megabank project. JMA journal, 2019. doi:10.31662/jmaj.2019-0014.

[6]

Hachiya, Furukawa, Shiwa, Ohmomo, Ono, Katsuoka, Nagasaki, Yasuda, Fuse, Kinoshita, Yamamoto, Tanno, Satoh, Endo, Sasaki, Sakata, Kobayashi, Ogasawara, Hitomi, Sobue, and Shimizu. Genome-wide identification of inter-individually variable dna methylation sites improves the efficacy of epigenetic association studies. NPJ genomic medicine, 2017. doi:10.1038/s41525-017-0016-5.

[7]

Komaki, Shiwa, Furukawa, Hachiya, Ohmomo, Otomo, Satoh, Hitomi, Sobue, Sasaki, and Shimizu. Imethyl: an integrative database of human dna methylation, gene expression, and genomic variation. Human genome variation, 2018. doi:10.1038/hgv.2018.8.

[8]

Otsuki, Okamura, Aoki, Ishida, Kumada, Minegishi, Katsuoka, Kinoshita, and Yamamoto. Identification of dominant transcripts in oxidative stress response by a full-length transcriptome analysis. Molecular and cellular biology, 2021. doi:10.1128/MCB.00472-20.

[9]

Koshiba, Motoike, Saigusa, Inoue, Shirota, Katoh, Katsuoka, Danjoh, Hozawa, Kuriyama, Minegishi, Nagasaki, Takai-Igarashi, Ogishima, Fuse, Kure, Tamiya, Tanabe, Yasuda, Kinoshita, and Yamamoto. Omics research project on prospective cohort studies from the tohoku medical megabank project. Genes to cells : devoted to molecular & cellular mechanisms, 2018. doi:10.1111/gtc.12588.

[10]

Saigusa, Matsukawa, Tadaka, Motoike, and Koshiba. Metabolome analysis of human plasma by gc-ms/ms in a large-scale cohort. Proteome Letters, 4(1):31–40, 2019. doi:10.14889/jpros.4.1_31.

[11]

Saigusa, Hishinuma, Matsukawa, Takahashi, Inoue, Tadaka, Motoike, Hozawa, Izumi, Bamba, Kinoshita, Ekroos, Koshiba, and Yamamoto. Comparison of kit-based metabolomics with other methodologies in a large cohort, towards establishing reference values. Metabolites, 2021. doi:10.3390/metabo11100652.

[12]

Saito, Aoki, Tamahara, Goto, Matsui, Kawashima, Danjoh, Hozawa, Kuriyama, Suzuki, Fuse, Kure, Yamashita, Tanabe, Minegishi, Kinoshita, Tsuboi, Shimizu, and Yamamoto. Oral microbiome analysis in prospective genome cohort studies of the tohoku medical megabank project. Frontiers in cellular and infection microbiology, 2020. doi:10.3389/fcimb.2020.604596.

[13]

DePristo, Banks, Poplin, Garimella, Maguire, Hartl, Philippakis, del Angel, Rivas, Hanna, McKenna, Fennell, Kernytsky, Sivachenko, Cibulskis, Gabriel, Altshuler, and Daly. A framework for variation discovery and genotyping using next-generation dna sequencing data. Nature genetics, 2011. doi:10.1038/ng.806.

[14]

Karczewski, Francioli, Tiao, Cummings, Alföldi, Wang, Collins, Laricchia, Ganna, Birnbaum, Gauthier, Brand, Solomonson, Watts, Rhodes, Singer-Berk, England, Seaby, Kosmicki, Walters, Tashman, Farjoun, Banks, Poterba, Wang, Seed, Whiffin, Chong, Samocha, Pierce-Hoffman, Zappala, O'Donnell-Luria, Minikel, Weisburd, Lek, Ware, Vittal, Armean, Bergelson, Cibulskis, Connolly, Covarrubias, Donnelly, Ferriera, Gabriel, Gentry, Gupta, Jeandet, Kaplan, Llanwarne, Munshi, Novod, Petrillo, Roazen, Ruano-Rubio, Saltzman, Schleicher, Soto, Tibbetts, Tolonen, Wade, Talkowski, Neale, Daly, and MacArthur. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature, 2020. doi:10.1038/s41586-020-2308-7.

[15]

Liu, Jian, and Boerwinkle. Dbnsfp: a lightweight database of human nonsynonymous snps and their functional predictions. Human mutation, 2011. doi:10.1002/humu.21517.

[16]

Liu, Li, Mou, Dong, and Tu. Dbnsfp v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site snvs. Genome medicine, 2020. doi:10.1186/s13073-020-00803-9.

[17]

Dong, Wei, Jian, Gibbs, Boerwinkle, Wang, and Liu. Comparison and integration of deleteriousness prediction methods for nonsynonymous snvs in whole exome sequencing studies. Human molecular genetics, 2015. doi:10.1093/hmg/ddu733.

[18]

Hadley Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. ISBN 978-3-319-24277-4. URL: \url{https://ggplot2.tidyverse.org}.

[19]

Athanasia M. Mowinckel and Didac Vidal-Piñeiro. Visualization of brain statistics with r packages ggseg and ggseg3d. Advances in Methods and Practices in Psychological Science, 3(4):466–483, 2020. URL: \url{https://doi.org/10.1177/2515245920928009}, doi:10.1177/2515245920928009.

[20]

Sato, Hishinuma, Matsukawa, Shima, Saigusa, Motoike, Kogure, Nakaya, Hozawa, Kuriyama, Yamamoto, Koshiba, and Kinoshita. Dietary habits and plasma lipid concentrations in a general japanese population. Metabolomics : Official journal of the Metabolomic Society, 2024. doi:10.1007/s11306-024-02087-1.

[21]

Freesurfer software suite. https://surfer.nmr.mgh.harvard.edu/.

[22]

Fischl, Salat, Busa, Albert, Dieterich, Haselgrove, van der Kouwe, Killiany, Kennedy, Klaveness, Montillo, Makris, Rosen, and Dale. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 2002. doi:10.1016/s0896-6273(02)00569-x.

[23]

Taira, Mugikura, Mori, Hozawa, Saito, Nakamura, Kiyomoto, Kobayashi, Ogishima, Nagami, Uruno, Shimizu, Kobayashi, Yasuda, Kure, Sakurai, Motoike, Kumada, Nakaya, Obara, Oba, Sekiguchi, Thyreau, Mutoh, Takano, Abe, Maikusa, Tatewaki, Taki, Yaegashi, Tomita, Kinoshita, Kuriyama, Fuse, and Yamamoto. Tohoku medical megabank brain magnetic resonance imaging study: rationale, design, and background. JMA journal, 2023. doi:10.31662/jmaj.2022-0220.

[24]

Landrum, Chitipiralla, Brown, Chen, Gu, Hart, Hoffman, Jang, Kaur, Liu, Lyoshin, Maddipatla, Maiti, Mitchell, O'Leary, Riley, Shi, Zhou, Schneider, Maglott, Holmes, and Kattman. Clinvar: improvements to accessing data. Nucleic acids research, 2020. doi:10.1093/nar/gkz972.

[25]

Sayers, Beck, Bolton, Bourexis, Brister, Canese, Comeau, Funk, Kim, Klimke, Marchler-Bauer, Landrum, Lathrop, Lu, Madden, O'Leary, Phan, Rangwala, Schneider, Skripchenko, Wang, Ye, Trawick, Pruitt, and Sherry. Database resources of the national center for biotechnology information. Nucleic acids research, 2021. doi:10.1093/nar/gkaa892.

[26]

O'Leary, Wright, Brister, Ciufo, Haddad, McVeigh, Rajput, Robbertse, Smith-White, Ako-Adjei, Astashyn, Badretdin, Bao, Blinkova, Brover, Chetvernin, Choi, Cox, Ermolaeva, Farrell, Goldfarb, Gupta, Haft, Hatcher, Hlavina, Joardar, Kodali, Li, Maglott, Masterson, McGarvey, Murphy, O'Neill, Pujar, Rangwala, Rausch, Riddick, Schoch, Shkeda, Storz, Sun, Thibaud-Nissen, Tolstoy, Tully, Vatsan, Wallin, Webb, Wu, Landrum, Kimchi, Tatusova, DiCuccio, Kitts, Murphy, and Pruitt. Reference sequence (refseq) database at ncbi: current status, taxonomic expansion, and functional annotation. Nucleic acids research, 2016. doi:10.1093/nar/gkv1189.

[27]

Frankish, Diekhans, Jungreis, Lagarde, Loveland, Mudge, Sisu, Wright, Armstrong, Barnes, Berry, Bignell, Boix, Carbonell Sala, Cunningham, Di Domenico, Donaldson, Fiddes, García Girón, Gonzalez, Grego, Hardy, Hourlier, Howe, Hunt, Izuogu, Johnson, Martin, Martínez, Mohanan, Muir, Navarro, Parker, Pei, Pozo, Riera, Ruffier, Schmitt, Stapleton, Suner, Sycheva, Uszczynska-Ratajczak, Wolf, Xu, Yang, Yates, Zerbino, Zhang, Choudhary, Gerstein, Guigó, Hubbard, Kellis, Paten, Tress, and Flicek. Gencode 2021. Nucleic acids research, 2021. doi:10.1093/nar/gkaa1087.

[28]

UniProt Consortium. Uniprot: the universal protein knowledgebase in 2021. Nucleic acids research, 2021. doi:10.1093/nar/gkaa1100.