Tohoku University Tohoku Medical Megabank Organization (Kengo Kinoshita)
2020.11.20

1.   Anzawa Hayato, Yamagata Hitoshi, Kinoshita Kengo. Theoretical characterisation of strand cross-correlation in ChIP-seq. BMC Bioinformatics. 2020; 21 (1): 417. doi:10.1186/s12859-020-03729-6  
2.   Saigusa Daisuke, Motoike Ikuko N., Saito Sakae, et al. Impacts of NRF2 activation in non–small‐cell lung cancer cell lines on extracellular metabolites. Cancer Science. 2020; 111 (2): 667-678. doi:10.1111/cas.14278  
3.   Koshiba Seizo, Motoike Ikuko N., Saigusa Daisuke, et al. Identification of critical genetic variants associated with metabolic phenotypes of the Japanese population. Communications Biology. 2020; 3 (1): 662. doi:10.1038/s42003-020-01383-5  
4.   Ishida Noriko, Aoki Yuichi, Katsuoka Fumiki, et al. Landscape of electrophilic and inflammatory stress-mediated gene regulation in human lymphoblastoid cell lines. Free Radical Biology and Medicine. 2020; 161 : 71-83. doi:10.1016/j.freeradbiomed.2020.09.023  
5.   Kuriyama Shinichi, Metoki Hirohito, Kikuya Masahiro, et al. Cohort Profile: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study): rationale, progress and perspective. International Journal of Epidemiology. 2020; 49 (1): 18-19m. doi:10.1093/ije/dyz169  
6.   Sugawara Junichi, Ishikuro Mami, Obara Taku, et al. Maternal Baseline Characteristics and Perinatal Outcomes: the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. Journal of Epidemiology. 2020; : . doi:10.2188/jea.JE20200338  
7.   Hozawa Atsushi, Tanno Kozo, Nakaya Naoki, et al. Study Profile of the Tohoku Medical Megabank Community-Based Cohort Study. Journal of Epidemiology. 2020; : . doi:10.2188/jea.JE20190271  
8.   Shirota Matsuyuki, Saigusa Daisuke, Yamashita Riu, et al. Longitudinal plasma amino acid profiling with maternal genomic background throughout human pregnancy. Medical Mass Spectrometry. 2020; 4 (1): 36-49. doi:10.24508/mms.2020.06.001  
9.   Kagaya Yuki, Minei Ryuhei, Duong Ha T T, et al. Metagenome Sequences from the Environment of Diseased Otter Clams, Lutraria rhynchaena, from a Farm in Vietnam. Microbiology Resource Announcements. 2020; 9 (2): . doi:10.1128/MRA.01068-19  
10.   Otsuki Akihito, Okamura Yasunobu, Aoki Yuichi, et al. Identification of dominant transcripts in oxidative stress response by a full-length transcriptome analysis. Molecular and Cellular Biology. 2020; : . doi:10.1128/MCB.00472-20  
11.   Lin Yingsong, Nakatochi Masahiro, Hosono Yasuyuki, et al. Genome-wide association meta-analysis identifies GP2 gene risk variants for pancreatic cancer. Nature Communications. 2020; 11 (1): 3175. doi:10.1038/s41467-020-16711-w  
12.   Tadaka Shu, Hishinuma Eiji, Komaki Shohei, et al. jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population. Nucleic Acids Research. 2020; : . doi:10.1093/nar/gkaa1034  
13.   Kojima Kaname, Tadaka Shu, Katsuoka Fumiki, et al. A genotype imputation method for de-identified haplotype reference information by using recurrent neural network. PLOS Computational Biology. 2020; 16 (10): e1008207. doi:10.1371/journal.pcbi.1008207  
14.   Wagata Maiko, Ishikuro Mami, Obara Taku, et al. Low birth weight and abnormal pre-pregnancy body mass index were at higher risk for hypertensive disorders of pregnancy. Pregnancy Hypertension. 2020; 22 : 119-125. doi:10.1016/j.preghy.2020.08.001  
15.   Amano Ryota, Karashima Akihiro, Motoike Ikuko, et al. Consistency index of daily activity pattern and its correlations with subjective ratings of QOL. Sleep and Biological Rhythms. 2020; : . doi:10.1007/s41105-020-00271-z  
16.   Tsuboi Akito, Matsui Hiroyuki, Shiraishi Naru, et al. Design and Progress of Oral Health Examinations in the Tohoku Medical Megabank Project. The Tohoku Journal of Experimental Medicine. 2020; 251 (2): 97-115. doi:10.1620/tjem.251.97  
17.   Takahashi Yuta, Ueki Masao, Yamada Makoto, et al. Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection. Translational Psychiatry. 2020; 10 (1): 157. doi:10.1038/s41398-020-0831-9  
18.   Takahashi Yuta, Ueki Masao, Tamiya Gen, et al. Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes. Translational Psychiatry. 2020; 10 (1): 294. doi:10.1038/s41398-020-00957-5  
19.   Sugawara Junichi, Ochi Daisuke, Yamashita Riu, et al. Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy. BMJ Open. 2019; 9 (2): e025939. doi:10.1136/bmjopen-2018-025939  
20.   Mori Minako, Hira Asuka, Yoshida Kenichi, et al. Pathogenic mutations identified by a multimodality approach in 117 Japanese Fanconi anemia patients. Haematologica. 2019; 104 (10): 1962-1973. doi:10.3324/haematol.2018.207241  
21.   Yamaguchi-Kabata Yumi, Yasuda Jun, Uruno Akira, et al. Estimating carrier frequencies of newborn screening disorders using a whole-genome reference panel of 3552 Japanese individuals. Human Genetics. 2019; 138 (4): 389-409. doi:10.1007/s00439-019-01998-7  
22.   Tadaka Shu, Katsuoka Fumiki, Ueki Masao, et al. 3.5KJPNv2: an allele frequency panel of 3552 Japanese individuals including the X chromosome. Human Genome Variation. 2019; 6 (1): 28. doi:10.1038/s41439-019-0059-5  
23.   Nagasaki Masao, Kuroki Yoko, Shibata Tomoko F., et al. Construction of JRG (Japanese reference genome) with single-molecule real-time sequencing. Human Genome Variation. 2019; 6 (1): 27. doi:10.1038/s41439-019-0057-7  
24.   Sato Tomonori, Kawasaki Yoshihide, Maekawa Masamitsu, et al. Value of global metabolomics in association with diagnosis and clinicopathological factors of renal cell carcinoma. International Journal of Cancer. 2019; 145 (2): 484-493. doi:10.1002/ijc.32115  
25.   Sakurai Rieko, Ueki Masao, Makino Satoshi, et al. Outlier detection for questionnaire data in biobanks. International Journal of Epidemiology. 2019; 48 (4): 1305-1315. doi:10.1093/ije/dyz012  
26.   Fuse Nobuo, Sakurai-Yageta Mika, Katsuoka Fumiki, et al. Establishment of Integrated Biobank for Precision Medicine and Personalized Healthcare: The Tohoku Medical Megabank Project. JMA Journal. 2019; 2 (2): 113-122. doi:10.31662/jmaj.2019-0014  
27.   Kiniwa Yukiko, Yasuda Jun, Saito Sakae, et al. Identification of genetic alterations in extramammary Paget disease using whole exome analysis. Journal of Dermatological Science. 2019; 94 (1): 229-235. doi:10.1016/j.jdermsci.2019.03.006  
28.   Mizuguchi Takeshi, Suzuki Takeshi, Abe Chihiro, et al. A 12-kb structural variation in progressive myoclonic epilepsy was newly identified by long-read whole-genome sequencing. Journal of Human Genetics. 2019; 64 (5): 359-368. doi:10.1038/s10038-019-0569-5  
29.   Obayashi Takeshi, Kagaya Yuki, Aoki Yuichi, et al. COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference. Nucleic Acids Research. 2019; 47 (D1): D55-D62. doi:10.1093/nar/gky1155  
30.   Yasuda Jun, Kinoshita Kengo, Katsuoka Fumiki, et al. Genome analyses for the Tohoku Medical Megabank Project towards establishment of personalized healthcare. The Journal of Biochemistry. 2019; 165 (2): 139-158. doi:10.1093/jb/mvy096  
31.   Mimori Takahiro, Yasuda Jun, Kuroki Yoko, et al. Construction of full-length Japanese reference panel of class I HLA genes with single-molecule, real-time sequencing. The Pharmacogenomics Journal. 2019; 19 (2): 136-146. doi:10.1038/s41397-017-0010-4  
32.   Yamada Kazunori D., Kinoshita Kengo. De novo profile generation based on sequence context specificity with the long short-term memory network. BMC Bioinformatics. 2018; 19 (1): 272. doi:10.1186/s12859-018-2284-1  
33.   Okamura Yasunobu, Kinoshita Kengo. Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data. BMC Bioinformatics. 2018; 19 (1): 266. doi:10.1186/s12859-018-2279-y  
34.   Yasuda Jun, Katsuoka Fumiki, Danjoh Inaho, et al. Regional genetic differences among Japanese populations and performance of genotype imputation using whole-genome reference panel of the Tohoku Medical Megabank Project. BMC Genomics. 2018; 19 (1): 551. doi:10.1186/s12864-018-4942-0  
35.   Koshiba Seizo, Motoike Ikuko, Saigusa Daisuke, et al. Omics research project on prospective cohort studies from the Tohoku Medical Megabank Project. Genes to Cells. 2018; 23 (6): 406-417. doi:10.1111/gtc.12588  
36.   Yamaguchi-Kabata Yumi, Yasuda Jun, Tanabe Osamu, et al. Evaluation of reported pathogenic variants and their frequencies in a Japanese population based on a whole-genome reference panel of 2049 individuals. Journal of Human Genetics. 2018; 63 (2): 213-230. doi:10.1038/s10038-017-0347-1  
37.   Tadaka Shu, Saigusa Daisuke, Motoike Ikuko N., et al. jMorp: Japanese Multi Omics Reference Panel. Nucleic Acids Research. 2018; 46 (D1): D551-D557. doi:10.1093/nar/gkx978  
38.   Obayashi Takeshi, Aoki Yuichi, Tadaka Shu, et al. ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index. Plant and Cell Physiology. 2018; 59 (1): e3-e3. doi:10.1093/pcp/pcx191  
39.   Kondo Hiroko X., Yoshida Norio, Shirota Matsuyuki, Kinoshita Kengo. Molecular Mechanism of Depolarization-Dependent Inactivation in W366F Mutant of Kv1.2. The Journal of Physical Chemistry B. 2018; 122 (48): 10825-10833. doi:10.1021/acs.jpcb.8b09446  
40.   Iwaki Masayo, Takeshita Kohei, Kondo Hiroko X., et al. Zn 2+ -Binding to the Voltage-Gated Proton Channel Hv1/VSOP. The Journal of Physical Chemistry B. 2018; 122 (39): 9076-9080. doi:10.1021/acs.jpcb.8b04890  
41.   Takai-Igarashi Takako, Kinoshita Kengo, Nagasaki Masao, et al. Security controls in an integrated Biobank to protect privacy in data sharing: rationale and study design. BMC Medical Informatics and Decision Making. 2017; 17 (1): 100. doi:10.1186/s12911-017-0494-5  
42.   Tsugita Misato, Morimoto Nobuyuki, Tashiro Manabu, et al. SR-B1 Is a Silica Receptor that Mediates Canonical Inflammasome Activation. Cell Reports. 2017; 18 (5): 1298-1311. doi:10.1016/j.celrep.2017.01.004  
43.   Matsuura Kentaro, Sawai Hiromi, Ikeo Kazuho, et al. Genome-Wide Association Study Identifies TLL1 Variant Associated With Development of Hepatocellular Carcinoma After Eradication of Hepatitis C Virus Infection. Gastroenterology. 2017; 152 (6): 1383-1394. doi:10.1053/j.gastro.2017.01.041  
44.   Hachiya Tsuyoshi, Furukawa Ryohei, Shiwa Yuh, et al. Genome-wide identification of inter-individually variable DNA methylation sites improves the efficacy of epigenetic association studies. npj Genomic Medicine. 2017; 2 (1): 11. doi:10.1038/s41525-017-0016-5  
45.   Tadaka Shu, Kinoshita Kengo. NCMine: Core-peripheral based functional module detection using near-clique mining. Bioinformatics (Oxford, England). 2016; 32 (22): 3454-3460. doi:10.1093/bioinformatics/btw488  
46.   Yamada Kazunori D, Nishi Hafumi, Nakata Junichi, Kinoshita Kengo. Structural characterization of single nucleotide variants at ligand binding sites and enzyme active sites of human proteins. Biophysics and Physicobiology. 2016; 13 : 157-163. doi:10.2142/biophysico.13.0_157  
47.   Shirota Matsuyuki, Kinoshita Kengo. Discrepancies between human DNA, mRNA and protein reference sequences and their relation to single nucleotide variants in the human population. Database. 2016; 2016 : baw124. doi:10.1093/database/baw124  
48.   Kuriyama Shinichi, Yaegashi Nobuo, Nagami Fuji, et al. The Tohoku Medical Megabank Project: Design and Mission. Journal of Epidemiology. 2016; 26 (9): 493-511. doi:10.2188/jea.JE20150268  
49.   Murakami Yoichi, Omori Satoshi, Kinoshita Kengo. NLDB: a database for 3D protein–ligand interactions in enzymatic reactions. Journal of Structural and Functional Genomics. 2016; 17 (4): 101-110. doi:10.1007/s10969-016-9206-0  
50.   Gojobori Takashi, Ikeo Kazuho, Katayama Yukie, et al. VaProS: a database-integration approach for protein/genome information retrieval. Journal of Structural and Functional Genomics. 2016; 17 (4): 69-81. doi:10.1007/s10969-016-9211-3  
51.   Aoki Yuichi, Okamura Yasunobu, Ohta Hiroyuki, et al. ALCOdb: Gene Coexpression Database for Microalgae. Plant and Cell Physiology. 2016; 57 (1): e3-e3. doi:10.1093/pcp/pcv190  
52.   Aoki Yuichi, Okamura Yasunobu, Tadaka Shu, et al. ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression. Plant and Cell Physiology. 2016; 57 (1): e5-e5. doi:10.1093/pcp/pcv165  
53.   Saigusa Daisuke, Okamura Yasunobu, Motoike Ikuko N., et al. Establishment of Protocols for Global Metabolomics by LC-MS for Biomarker Discovery. PLOS ONE. 2016; 11 (8): e0160555. doi:10.1371/journal.pone.0160555  
54.   Kasahara Kota, Kinoshita Kengo. IBiSA_Tools: A Computational Toolkit for Ion-Binding State Analysis in Molecular Dynamics Trajectories of Ion Channels. PLOS ONE. 2016; 11 (12): e0167524. doi:10.1371/journal.pone.0167524  
55.   Kasahara Kota, Shirota Matsuyuki, Kinoshita Kengo. Ion Concentration- and Voltage-Dependent Push and Pull Mechanisms of Potassium Channel Ion Conduction. PLOS ONE. 2016; 11 (3): e0150716. doi:10.1371/journal.pone.0150716  
56.   Nishi Hafumi, Nakata Junichi, Kinoshita Kengo. Distribution of single-nucleotide variants on protein-protein interaction sites and its relationship with minor allele frequency. Protein Science. 2016; 25 (2): 316-321. doi:10.1002/pro.2845  
57.   Kasahara Kota, Kinoshita Kengo. Landscape of protein-small ligand binding modes. Protein Science. 2016; 25 (9): 1659-1671. doi:10.1002/pro.2971  
58.   Fujiwara Yuichiro, Kondo Hiroko X., Shirota Matsuyuki, et al. Structural basis for the membrane association of ankyrinG via palmitoylation. Scientific Reports. 2016; 6 (1): 23981. doi:10.1038/srep23981  
59.   Koshiba Seizo, Motoike Ikuko, Kojima Kaname, et al. The structural origin of metabolic quantitative diversity. Scientific Reports. 2016; 6 (1): 31463. doi:10.1038/srep31463  
60.   Nagasaki Masao, Yasuda Jun, Katsuoka Fumiki, et al. Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals. Nature Communications. 2015; 6 (1): 8018. doi:10.1038/ncomms9018  
61.   Okamura Yasunobu, Aoki Yuichi, Obayashi Takeshi, et al. COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Nucleic Acids Research. 2015; 43 (D1): D82-D86. doi:10.1093/nar/gku1163  
62.   Okamura Yasunobu, Obayashi Takeshi, Kinoshita Kengo. Comparison of Gene Coexpression Profiles and Construction of Conserved Gene Networks to Find Functional Modules. PLOS ONE. 2015; 10 (7): e0132039. doi:10.1371/journal.pone.0132039  
63.   Kasahara Kota, Kinoshita Kengo. GIANT: pattern analysis of molecular interactions in 3D structures of protein–small ligand complexes. BMC Bioinformatics. 2014; 15 (1): 12. doi:10.1186/1471-2105-15-12  
64.   Motoike Ikuko N, Matsumoto Mitsuyo, Danjoh Inaho, et al. Validation of multiple single nucleotide variation calls by additional exome analysis with a semiconductor sequencer to supplement data of whole-genome sequencing of a human population. BMC Genomics. 2014; 15 (1): 673. doi:10.1186/1471-2164-15-673  
65.   Obayashi Takeshi, Okamura Yasunobu, Ito Satoshi, et al. ATTED-II in 2014: Evaluation of Gene Coexpression in Agriculturally Important Plants. Plant and Cell Physiology. 2014; 55 (1): e6-e6. doi:10.1093/pcp/pct178  
66.   Kasahara Kota, Shirota Matsuyuki, Kinoshita Kengo. Comprehensive Classification and Diversity Assessment of Atomic Contacts in Protein–Small Ligand Interactions. Journal of Chemical Information and Modeling. 2013; 53 (1): 241-248. doi:10.1021/ci300377f  
67.   Kasahara Kota, Shirota Matsuyuki, Kinoshita Kengo. Ion Concentration-Dependent Ion Conduction Mechanism of a Voltage-Sensitive Potassium Channel. PLoS ONE. 2013; 8 (2): e56342. doi:10.1371/journal.pone.0056342  
68.   Shirota Matsuyuki, Kinoshita Kengo. Analyses of the general rule on residue pair frequencies in local amino acid sequences of soluble, ordered proteins. Protein Science. 2013; 22 (6): 725-733. doi:10.1002/pro.2255  
69.   布施 昇男、清水 愛、木村 雅恵、高野 良真、石 棟、宮澤 晃子、国松 志保、劉 孟林、渡邊 亮、安田 正幸、横山 悠、檜森 紀子、津田 聡、山本 耕太郎、中澤 徹、安田 純、勝岡 史城、小島 要、成相 直樹、松本 光代、元池 育子、長崎 正朗、木下 賢吾、五十嵐 和彦、山本 雅之、新堀 哲也、青木 洋子、松原 洋一、舟山 亮、長嶋 剛史、中山 啓子、眞島 行彦、舟山 智代、田中 光一、原田 高幸、阿部 春樹、福地 健郎、安田 典子、出田 秀尚、鄭 暁東、白石 敦、大橋 祐一、石田 誠夫、原 岳、金森 章. 緑内障のゲノム解析―次世代医療•個別化医療に向けて. 日本眼科学会雑誌. 2013; 118 ((3)): 216-240.  
70.   Obayashi Takeshi, Okamura Yasunobu, Ito Satoshi, et al. COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals. Nucleic Acids Research. 2012; 41 (D1): D1014-D1020. doi:10.1093/nar/gks1014