9 results on '"Kojima-Ishiyama M."'
Search Results
2. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network
- Author
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Grapotte M., Saraswat M., Bessiere C., Menichelli C., Ramilowski J. A., Severin J., Hayashizaki Y., Itoh M., Tagami M., Murata M., Kojima-Ishiyama M., Noma S., Noguchi S., Kasukawa T., Hasegawa A., Suzuki H., Nishiyori-Sueki H., Frith M. C., Abugessaisa I., Aitken S., Aken B. L., Alam I., Alam T., Alasiri R., Alhendi A. M. N., Alinejad-Rokny H., Alvarez M. J., Andersson R., Arakawa T., Araki M., Arbel T., Archer J., Archibald A. L., Arner E., Arner P., Asai K., Ashoor H., Astrom G., Babina M., Baillie J. K., Bajic V. B., Bajpai A., Baker S., Baldarelli R. M., Balic A., Bansal M., Batagov A. O., Batzoglou S., Beckhouse A. G., Beltrami A. P., Beltrami C. A., Bertin N., Bhattacharya S., Bickel P. J., Blake J. A., Blanchette M., Bodega B., Bonetti A., Bono H., Bornholdt J., Bttcher M., Bougouffa S., Boyd M., Breda J., Brombacher F., Brown J. B., Bult C. J., Burroughs A. M., Burt D. W., Busch A., Caglio G., Califano A., Cameron C. J., Cannistraci C. V., Carbone A., Carlisle A. J., Carninci P., Carter K. W., Cesselli D., Chang J. -C., Chen J. C., Chen Y., Chierici M., Christodoulou J., Ciani Y., Clark E. L., Coskun M., Dalby M., Dalla E., Daub C. O., Davis C. A., de Hoon M. J. L., de Rie D., Denisenko E., Deplancke B., Detmar M., Deviatiiarov R., Di Bernardo D., Diehl A. D., Dieterich L. C., Dimont E., Djebali S., Dohi T., Dostie J., Drablos F., Edge A. S. B., Edinger M., Ehrlund A., Ekwall K., Elofsson A., Endoh M., Enomoto H., Enomoto S., Faghihi M., Fagiolini M., Farach-Carson M. C., Faulkner G. J., Favorov A., Fernandes A. M., Ferrai C., Forrest A. R. R., Forrester L. M., Forsberg M., Fort A., Francescatto M., Freeman T. C., Frith M., Fukuda S., Funayama M., Furlanello C., Furuno M., Furusawa C., Gao H., Gazova I., Gebhard C., Geier F., Geijtenbeek T. B. H., Ghosh S., Ghosheh Y., Gingeras T. R., Gojobori T., Goldberg T., Goldowitz D., Gough J., Greco D., Gruber A. J., Guhl S., Guigo R., Guler R., Gusev O., Gustincich S., Ha T. J., Haberle V., Hale P., Hallstrom B. M., Hamada M., Handoko L., Hara M., Harbers M., Harrow J., Harshbarger J., Hase T., Hashimoto K., Hatano T., Hattori N., Hayashi R., Herlyn M., Hettne K., Heutink P., Hide W., Hitchens K. J., Sui S. H., 't Hoen P. A. C., Hon C. C., Hori F., Horie M., Horimoto K., Horton P., Hou R., Huang E., Huang Y., Hugues R., Hume D., Ienasescu H., Iida K., Ikawa T., Ikemura T., Ikeo K., Inoue N., Ishizu Y., Ito Y., Ivshina A. V., Jankovic B. R., Jenjaroenpun P., Johnson R., Jorgensen M., Jorjani H., Joshi A., Jurman G., Kaczkowski B., Kai C., Kaida K., Kajiyama K., Kaliyaperumal R., Kaminuma E., Kanaya T., Kaneda H., Kapranov P., Kasianov A. S., Katayama T., Kato S., Kawaguchi S., Kawai J., Kawaji H., Kawamoto H., Kawamura Y. I., Kawasaki S., Kawashima T., Kempfle J. S., Kenna T. J., Kere J., Khachigian L., Kiryu H., Kishima M., Kitajima H., Kitamura T., Kitano H., Klaric E., Klepper K., Klinken S. P., Kloppmann E., Knox A. J., Kodama Y., Kogo Y., Kojima M., Kojima S., Komatsu N., Komiyama H., Kono T., Koseki H., Koyasu S., Kratz A., Kukalev A., Kulakovskiy I., Kundaje A., Kunikata H., Kuo R., Kuo T., Kuraku S., Kuznetsov V. A., Kwon T. J., Larouche M., Lassmann T., Law A., Le-Cao K. -A., Lecellier C. -H., Lee W., Lenhard B., Lennartsson A., Li K., Li R., Lilje B., Lipovich L., Lizio M., Lopez G., Magi S., Mak G. K., Makeev V., Manabe R., Mandai M., Mar J., Maruyama K., Maruyama T., Mason E., Mathelier A., Matsuda H., Medvedeva Y. A., Meehan T. F., Mejhert N., Meynert A., Mikami N., Minoda A., Miura H., Miyagi Y., Miyawaki A., Mizuno Y., Morikawa H., Morimoto M., Morioka M., Morishita S., Moro K., Motakis E., Motohashi H., Mukarram A. K., Mummery C. L., Mungall C. J., Murakawa Y., Muramatsu M., Nagasaka K., Nagase T., Nakachi Y., Nakahara F., Nakai K., Nakamura K., Nakamura Y., Nakazawa T., Nason G. P., Nepal C., Nguyen Q. H., Nielsen L. K., Nishida K., Nishiguchi K. M., Nishiyori H., Nitta K., Notredame C., Ogishima S., Ohkura N., Ohno H., Ohshima M., Ohtsu T., Okada Y., Okada-Hatakeyama M., Okazaki Y., Oksvold P., Orlando V., Ow G. S., Ozturk M., Pachkov M., Paparountas T., Parihar S. P., Park S. -J., Pascarella G., Passier R., Persson H., Philippens I. H., Piazza S., Plessy C., Pombo A., Ponten F., Poulain S., Poulsen T. M., Pradhan S., Prezioso C., Pridans C., Qin X. -Y., Quackenbush J., Rackham O., Ramilowski J., Ravasi T., Rehli M., Rennie S., Rito T., Rizzu P., Robert C., Roos M., Rost B., Roudnicky F., Roy R., Rye M. B., Sachenkova O., Saetrom P., Sai H., Saiki S., Saito M., Saito A., Sakaguchi S., Sakai M., Sakaue S., Sakaue-Sawano A., Sandelin A., Sano H., Sasamoto Y., Sato H., Saxena A., Saya H., Schafferhans A., Schmeier S., Schmidl C., Schmocker D., Schneider C., Schueler M., Schultes E. A., Schulze-Tanzil G., Semple C. A., Seno S., Seo W., Sese J., Sheng G., Shi J., Shimoni Y., Shin J. W., SimonSanchez J., Sivertsson A., Sjostedt E., Soderhall C., Laurent G. S., Stoiber M. H., Sugiyama D., Summers K. M., Suzuki A. M., Suzuki K., Suzuki M., Suzuki N., Suzuki T., Swanson D. J., Swoboda R. K., Taguchi A., Takahashi H., Takahashi M., Takamochi K., Takeda S., Takenaka Y., Tam K. T., Tanaka H., Tanaka R., Tanaka Y., Tang D., Taniuchi I., Tanzer A., Tarui H., Taylor M. S., Terada A., Terao Y., Testa A. C., Thomas M., Thongjuea S., Tomii K., Triglia E. T., Toyoda H., Tsang H. G., Tsujikawa M., Uhlen M., Valen E., van de Wetering M., van Nimwegen E., Velmeshev D., Verardo R., Vitezic M., Vitting-Seerup K., von Feilitzen K., Voolstra C. R., Vorontsov I. E., Wahlestedt C., Wasserman W. W., Watanabe K., Watanabe S., Wells C. A., Winteringham L. N., Wolvetang E., Yabukami H., Yagi K., Yamada T., Yamaguchi Y., Yamamoto M., Yamamoto Y., Yamanaka Y., Yano K., Yasuzawa K., Yatsuka Y., Yo M., Yokokura S., Yoneda M., Yoshida E., Yoshida Y., Yoshihara M., Young R., Young R. S., Yu N. Y., Yumoto N., Zabierowski S. E., Zhang P. G., Zucchelli S., Zwahlen M., Chatelain C., Brehelin L., Grapotte, M., Saraswat, M., Bessiere, C., Menichelli, C., Ramilowski, J. A., Severin, J., Hayashizaki, Y., Itoh, M., Tagami, M., Murata, M., Kojima-Ishiyama, M., Noma, S., Noguchi, S., Kasukawa, T., Hasegawa, A., Suzuki, H., Nishiyori-Sueki, H., Frith, M. C., Abugessaisa, I., Aitken, S., Aken, B. L., Alam, I., Alam, T., Alasiri, R., Alhendi, A. M. N., Alinejad-Rokny, H., Alvarez, M. J., Andersson, R., Arakawa, T., Araki, M., Arbel, T., Archer, J., Archibald, A. L., Arner, E., Arner, P., Asai, K., Ashoor, H., Astrom, G., Babina, M., Baillie, J. K., Bajic, V. B., Bajpai, A., Baker, S., Baldarelli, R. M., Balic, A., Bansal, M., Batagov, A. O., Batzoglou, S., Beckhouse, A. G., Beltrami, A. P., Beltrami, C. A., Bertin, N., Bhattacharya, S., Bickel, P. J., Blake, J. A., Blanchette, M., Bodega, B., Bonetti, A., Bono, H., Bornholdt, J., Bttcher, M., Bougouffa, S., Boyd, M., Breda, J., Brombacher, F., Brown, J. B., Bult, C. J., Burroughs, A. M., Burt, D. W., Busch, A., Caglio, G., Califano, A., Cameron, C. J., Cannistraci, C. V., Carbone, A., Carlisle, A. J., Carninci, P., Carter, K. W., Cesselli, D., Chang, J. -C., Chen, J. C., Chen, Y., Chierici, M., Christodoulou, J., Ciani, Y., Clark, E. L., Coskun, M., Dalby, M., Dalla, E., Daub, C. O., Davis, C. A., de Hoon, M. J. L., de Rie, D., Denisenko, E., Deplancke, B., Detmar, M., Deviatiiarov, R., Di Bernardo, D., Diehl, A. D., Dieterich, L. C., Dimont, E., Djebali, S., Dohi, T., Dostie, J., Drablos, F., Edge, A. S. B., Edinger, M., Ehrlund, A., Ekwall, K., Elofsson, A., Endoh, M., Enomoto, H., Enomoto, S., Faghihi, M., Fagiolini, M., Farach-Carson, M. C., Faulkner, G. J., Favorov, A., Fernandes, A. M., Ferrai, C., Forrest, A. R. R., Forrester, L. M., Forsberg, M., Fort, A., Francescatto, M., Freeman, T. C., Frith, M., Fukuda, S., Funayama, M., Furlanello, C., Furuno, M., Furusawa, C., Gao, H., Gazova, I., Gebhard, C., Geier, F., Geijtenbeek, T. B. H., Ghosh, S., Ghosheh, Y., Gingeras, T. R., Gojobori, T., Goldberg, T., Goldowitz, D., Gough, J., Greco, D., Gruber, A. J., Guhl, S., Guigo, R., Guler, R., Gusev, O., Gustincich, S., Ha, T. J., Haberle, V., Hale, P., Hallstrom, B. M., Hamada, M., Handoko, L., Hara, M., Harbers, M., Harrow, J., Harshbarger, J., Hase, T., Hashimoto, K., Hatano, T., Hattori, N., Hayashi, R., Herlyn, M., Hettne, K., Heutink, P., Hide, W., Hitchens, K. J., Sui, S. H., 't Hoen, P. A. C., Hon, C. C., Hori, F., Horie, M., Horimoto, K., Horton, P., Hou, R., Huang, E., Huang, Y., Hugues, R., Hume, D., Ienasescu, H., Iida, K., Ikawa, T., Ikemura, T., Ikeo, K., Inoue, N., Ishizu, Y., Ito, Y., Ivshina, A. V., Jankovic, B. R., Jenjaroenpun, P., Johnson, R., Jorgensen, M., Jorjani, H., Joshi, A., Jurman, G., Kaczkowski, B., Kai, C., Kaida, K., Kajiyama, K., Kaliyaperumal, R., Kaminuma, E., Kanaya, T., Kaneda, H., Kapranov, P., Kasianov, A. S., Katayama, T., Kato, S., Kawaguchi, S., Kawai, J., Kawaji, H., Kawamoto, H., Kawamura, Y. I., Kawasaki, S., Kawashima, T., Kempfle, J. S., Kenna, T. J., Kere, J., Khachigian, L., Kiryu, H., Kishima, M., Kitajima, H., Kitamura, T., Kitano, H., Klaric, E., Klepper, K., Klinken, S. P., Kloppmann, E., Knox, A. J., Kodama, Y., Kogo, Y., Kojima, M., Kojima, S., Komatsu, N., Komiyama, H., Kono, T., Koseki, H., Koyasu, S., Kratz, A., Kukalev, A., Kulakovskiy, I., Kundaje, A., Kunikata, H., Kuo, R., Kuo, T., Kuraku, S., Kuznetsov, V. A., Kwon, T. J., Larouche, M., Lassmann, T., Law, A., Le-Cao, K. -A., Lecellier, C. -H., Lee, W., Lenhard, B., Lennartsson, A., Li, K., Li, R., Lilje, B., Lipovich, L., Lizio, M., Lopez, G., Magi, S., Mak, G. K., Makeev, V., Manabe, R., Mandai, M., Mar, J., Maruyama, K., Maruyama, T., Mason, E., Mathelier, A., Matsuda, H., Medvedeva, Y. A., Meehan, T. F., Mejhert, N., Meynert, A., Mikami, N., Minoda, A., Miura, H., Miyagi, Y., Miyawaki, A., Mizuno, Y., Morikawa, H., Morimoto, M., Morioka, M., Morishita, S., Moro, K., Motakis, E., Motohashi, H., Mukarram, A. K., Mummery, C. L., Mungall, C. J., Murakawa, Y., Muramatsu, M., Nagasaka, K., Nagase, T., Nakachi, Y., Nakahara, F., Nakai, K., Nakamura, K., Nakamura, Y., Nakazawa, T., Nason, G. P., Nepal, C., Nguyen, Q. H., Nielsen, L. K., Nishida, K., Nishiguchi, K. M., Nishiyori, H., Nitta, K., Notredame, C., Ogishima, S., Ohkura, N., Ohno, H., Ohshima, M., Ohtsu, T., Okada, Y., Okada-Hatakeyama, M., Okazaki, Y., Oksvold, P., Orlando, V., Ow, G. S., Ozturk, M., Pachkov, M., Paparountas, T., Parihar, S. P., Park, S. -J., Pascarella, G., Passier, R., Persson, H., Philippens, I. H., Piazza, S., Plessy, C., Pombo, A., Ponten, F., Poulain, S., Poulsen, T. M., Pradhan, S., Prezioso, C., Pridans, C., Qin, X. -Y., Quackenbush, J., Rackham, O., Ramilowski, J., Ravasi, T., Rehli, M., Rennie, S., Rito, T., Rizzu, P., Robert, C., Roos, M., Rost, B., Roudnicky, F., Roy, R., Rye, M. B., Sachenkova, O., Saetrom, P., Sai, H., Saiki, S., Saito, M., Saito, A., Sakaguchi, S., Sakai, M., Sakaue, S., Sakaue-Sawano, A., Sandelin, A., Sano, H., Sasamoto, Y., Sato, H., Saxena, A., Saya, H., Schafferhans, A., Schmeier, S., Schmidl, C., Schmocker, D., Schneider, C., Schueler, M., Schultes, E. A., Schulze-Tanzil, G., Semple, C. A., Seno, S., Seo, W., Sese, J., Sheng, G., Shi, J., Shimoni, Y., Shin, J. W., Simonsanchez, J., Sivertsson, A., Sjostedt, E., Soderhall, C., Laurent, G. S., Stoiber, M. H., Sugiyama, D., Summers, K. M., Suzuki, A. M., Suzuki, K., Suzuki, M., Suzuki, N., Suzuki, T., Swanson, D. J., Swoboda, R. K., Taguchi, A., Takahashi, H., Takahashi, M., Takamochi, K., Takeda, S., Takenaka, Y., Tam, K. T., Tanaka, H., Tanaka, R., Tanaka, Y., Tang, D., Taniuchi, I., Tanzer, A., Tarui, H., Taylor, M. S., Terada, A., Terao, Y., Testa, A. C., Thomas, M., Thongjuea, S., Tomii, K., Triglia, E. T., Toyoda, H., Tsang, H. G., Tsujikawa, M., Uhlen, M., Valen, E., van de Wetering, M., van Nimwegen, E., Velmeshev, D., Verardo, R., Vitezic, M., Vitting-Seerup, K., von Feilitzen, K., Voolstra, C. R., Vorontsov, I. E., Wahlestedt, C., Wasserman, W. W., Watanabe, K., Watanabe, S., Wells, C. A., Winteringham, L. N., Wolvetang, E., Yabukami, H., Yagi, K., Yamada, T., Yamaguchi, Y., Yamamoto, M., Yamamoto, Y., Yamanaka, Y., Yano, K., Yasuzawa, K., Yatsuka, Y., Yo, M., Yokokura, S., Yoneda, M., Yoshida, E., Yoshida, Y., Yoshihara, M., Young, R., Young, R. S., Yu, N. Y., Yumoto, N., Zabierowski, S. E., Zhang, P. G., Zucchelli, S., Zwahlen, M., Chatelain, C., Brehelin, L., Institute of Biotechnology, Biosciences, Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), National Institute of Advanced Industrial Science and Technology (AIST), SANOFI Recherche, University of British Columbia (UBC), Experimental Immunology, Infectious diseases, AII - Infectious diseases, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)
- Subjects
0301 basic medicine ,General Physics and Astronomy ,Genome ,Mice ,0302 clinical medicine ,Transcription (biology) ,Promoter Regions, Genetic ,Transcription Initiation, Genetic ,0303 health sciences ,Multidisciplinary ,1184 Genetics, developmental biology, physiology ,High-Throughput Nucleotide Sequencing ,Neurodegenerative Diseases ,222 Other engineering and technologies ,Genomics ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,humanities ,Enhancer Elements, Genetic ,Microsatellite Repeat ,Transcription Initiation Site ,Sequence motif ,Transcription Initiation ,Human ,Enhancer Elements ,Neural Networks ,Science ,610 Medicine & health ,Computational biology ,Biology ,Article ,General Biochemistry, Genetics and Molecular Biology ,Promoter Regions ,03 medical and health sciences ,Computer ,Deep Learning ,Tandem repeat ,Genetic ,Clinical Research ,[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN] ,Machine learning ,Genetics ,Animals ,Humans ,Polymorphism ,Enhancer ,Transcriptomics ,Gene ,A549 Cell ,030304 developmental biology ,Polymorphism, Genetic ,Neurodegenerative Disease ,Base Sequence ,Animal ,Genome, Human ,Human Genome ,Computational Biology ,Promoter ,General Chemistry ,113 Computer and information sciences ,Cap analysis gene expression ,030104 developmental biology ,[SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics ,Cardiovascular and Metabolic Diseases ,A549 Cells ,Minion ,Generic health relevance ,3111 Biomedicine ,Neural Networks, Computer ,610 Medizin und Gesundheit ,030217 neurology & neurosurgery ,FANTOM consortium ,Microsatellite Repeats - Abstract
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism., Nature Communications, 12 (1), ISSN:2041-1723
- Published
- 2020
- Full Text
- View/download PDF
3. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network.
- Author
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Grapotte, M, Saraswat, M, Bessière, C, Menichelli, C, Ramilowski, JA, Severin, J, Hayashizaki, Y, Itoh, M, Tagami, M, Murata, M, Kojima-Ishiyama, M, Noma, S, Noguchi, S, Kasukawa, T, Hasegawa, A, Suzuki, H, Nishiyori-Sueki, H, Frith, MC, FANTOM consortium, Chatelain, C, Carninci, P, de Hoon, MJL, Wasserman, WW, Bréhélin, L, Lecellier, C-H, Grapotte, M, Saraswat, M, Bessière, C, Menichelli, C, Ramilowski, JA, Severin, J, Hayashizaki, Y, Itoh, M, Tagami, M, Murata, M, Kojima-Ishiyama, M, Noma, S, Noguchi, S, Kasukawa, T, Hasegawa, A, Suzuki, H, Nishiyori-Sueki, H, Frith, MC, FANTOM consortium, Chatelain, C, Carninci, P, de Hoon, MJL, Wasserman, WW, Bréhélin, L, and Lecellier, C-H
- Abstract
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
- Published
- 2021
4. High quality genome assembly of the anhydrobiotic midge provides insights on a single chromosome-based emergence of extreme desiccation tolerance.
- Author
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Yoshida Y, Shaikhutdinov N, Kozlova O, Itoh M, Tagami M, Murata M, Nishiyori-Sueki H, Kojima-Ishiyama M, Noma S, Cherkasov A, Gazizova G, Nasibullina A, Deviatiiarov R, Shagimardanova E, Ryabova A, Yamaguchi K, Bino T, Shigenobu S, Tokumoto S, Miyata Y, Cornette R, Yamada TG, Funahashi A, Tomita M, Gusev O, and Kikawada T
- Abstract
Non-biting midges (Chironomidae) are known to inhabit a wide range of environments, and certain species can tolerate extreme conditions, where the rest of insects cannot survive. In particular, the sleeping chironomid Polypedilum vanderplanki is known for the remarkable ability of its larvae to withstand almost complete desiccation by entering a state called anhydrobiosis. Chromosome numbers in chironomids are higher than in other dipterans and this extra genomic resource might facilitate rapid adaptation to novel environments. We used improved sequencing strategies to assemble a chromosome-level genome sequence for P. vanderplanki for deep comparative analysis of genomic location of genes associated with desiccation tolerance. Using whole genome-based cross-species and intra-species analysis, we provide evidence for the unique functional specialization of Chromosome 4 through extensive acquisition of novel genes. In contrast to other insect genomes, in the sleeping chironomid a uniquely high degree of subfunctionalization in paralogous anhydrobiosis genes occurs in this chromosome, as well as pseudogenization in a highly duplicated gene family. Our findings suggest that the Chromosome 4 in Polypedilum is a site of high genetic turnover, allowing it to act as a 'sandbox' for evolutionary experiments, thus facilitating the rapid adaptation of midges to harsh environments., (© The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)
- Published
- 2022
- Full Text
- View/download PDF
5. Author Correction: Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network.
- Author
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Grapotte M, Saraswat M, Bessière C, Menichelli C, Ramilowski JA, Severin J, Hayashizaki Y, Itoh M, Tagami M, Murata M, Kojima-Ishiyama M, Noma S, Noguchi S, Kasukawa T, Hasegawa A, Suzuki H, Nishiyori-Sueki H, Frith MC, Chatelain C, Carninci P, de Hoon MJL, Wasserman WW, Bréhélin L, and Lecellier CH
- Published
- 2022
- Full Text
- View/download PDF
6. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network.
- Author
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Grapotte M, Saraswat M, Bessière C, Menichelli C, Ramilowski JA, Severin J, Hayashizaki Y, Itoh M, Tagami M, Murata M, Kojima-Ishiyama M, Noma S, Noguchi S, Kasukawa T, Hasegawa A, Suzuki H, Nishiyori-Sueki H, Frith MC, Chatelain C, Carninci P, de Hoon MJL, Wasserman WW, Bréhélin L, and Lecellier CH
- Subjects
- A549 Cells, Animals, Base Sequence, Computational Biology methods, Deep Learning, Enhancer Elements, Genetic, Genome, Human, High-Throughput Nucleotide Sequencing, Humans, Mice, Neurodegenerative Diseases diagnosis, Neurodegenerative Diseases metabolism, Polymorphism, Genetic, Promoter Regions, Genetic, Microsatellite Repeats, Neural Networks, Computer, Neurodegenerative Diseases genetics, Transcription Initiation Site, Transcription Initiation, Genetic
- Abstract
Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.
- Published
- 2021
- Full Text
- View/download PDF
7. Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites.
- Author
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Morioka MS, Kawaji H, Nishiyori-Sueki H, Murata M, Kojima-Ishiyama M, Carninci P, and Itoh M
- Subjects
- Animals, Gene Expression, Humans, Mice, Promoter Regions, Genetic, Genomics methods, High-Throughput Nucleotide Sequencing methods, Transcription Initiation Site, Transcriptional Activation
- Abstract
Cap analysis of gene expression (CAGE) is an approach to identify and monitor the activity (transcription initiation frequency) of transcription start sites (TSSs) at single base-pair resolution across the genome. It has been effectively used to identify active promoter and enhancer regions in cancer cells, with potential utility to identify key factors to immunotherapy. Here, we overview a series of CAGE protocols and describe detailed experimental steps of the latest protocol based on the Illumina sequencing platform; both experimental steps (see Subheadings 3.1-3.11) and computational processing steps (see Subheadings 3.12-3.20) are described.
- Published
- 2020
- Full Text
- View/download PDF
8. Comparison of CAGE and RNA-seq transcriptome profiling using clonally amplified and single-molecule next-generation sequencing.
- Author
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Kawaji H, Lizio M, Itoh M, Kanamori-Katayama M, Kaiho A, Nishiyori-Sueki H, Shin JW, Kojima-Ishiyama M, Kawano M, Murata M, Ninomiya-Fukuda N, Ishikawa-Kato S, Nagao-Sato S, Noma S, Hayashizaki Y, Forrest AR, and Carninci P
- Subjects
- Gene Expression Profiling, Humans, Sequence Analysis, RNA methods, High-Throughput Nucleotide Sequencing methods, RNA genetics, Transcriptome genetics
- Abstract
CAGE (cap analysis gene expression) and RNA-seq are two major technologies used to identify transcript abundances as well as structures. They measure expression by sequencing from either the 5' end of capped molecules (CAGE) or tags randomly distributed along the length of a transcript (RNA-seq). Library protocols for clonally amplified (Illumina, SOLiD, 454 Life Sciences [Roche], Ion Torrent), second-generation sequencing platforms typically employ PCR preamplification prior to clonal amplification, while third-generation, single-molecule sequencers can sequence unamplified libraries. Although these transcriptome profiling platforms have been demonstrated to be individually reproducible, no systematic comparison has been carried out between them. Here we compare CAGE, using both second- and third-generation sequencers, and RNA-seq, using a second-generation sequencer based on a panel of RNA mixtures from two human cell lines to examine power in the discrimination of biological states, detection of differentially expressed genes, linearity of measurements, and quantification reproducibility. We found that the quantified levels of gene expression are largely comparable across platforms and conclude that CAGE and RNA-seq are complementary technologies that can be used to improve incomplete gene models. We also found systematic bias in the second- and third-generation platforms, which is likely due to steps such as linker ligation, cleavage by restriction enzymes, and PCR amplification. This study provides a perspective on the performance of these platforms, which will be a baseline in the design of further experiments to tackle complex transcriptomes uncovered in a wide range of cell types.
- Published
- 2014
- Full Text
- View/download PDF
9. Detecting expressed genes using CAGE.
- Author
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Murata M, Nishiyori-Sueki H, Kojima-Ishiyama M, Carninci P, Hayashizaki Y, and Itoh M
- Subjects
- Alkaline Phosphatase metabolism, Animals, Base Sequence, Biotinylation methods, DNA, Complementary isolation & purification, DNA, Complementary metabolism, Exodeoxyribonucleases metabolism, Gene Library, Humans, Mice, Promoter Regions, Genetic, RNA metabolism, Reverse Transcription, Ribonuclease, Pancreatic metabolism, DNA, Complementary genetics, Gene Expression Profiling methods, High-Throughput Nucleotide Sequencing methods, RNA genetics, Transcription Initiation Site
- Abstract
Cap analysis of gene expression (CAGE) provides accurate high-throughput measurement of RNA expression. By the large-scale analysis of 5' end of transcripts using CAGE method, it enables not only determination of the transcription start site but also prediction of promoter region. Here we provide a protocol for the construction of no-amplification non-tagging CAGE libraries for Illumina next-generation sequencers (nAnT-iCAGE). We have excluded the commonly used PCR amplification and cleavage of restriction enzyme to eliminate any potential biases. As a result, we achieved less biased simple preparation process.
- Published
- 2014
- Full Text
- View/download PDF
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