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1. Multiple wheat genomes reveal global variation in modern breeding

Author

70983196, 10273492, Walkowiak, Sean, Gao, Liangliang, Monat, Cecile, Haberer, Georg, Kassa, Mulualem T., Brinton, Jemima, Ramirez-Gonzalez, Ricardo H., Kolodziej, Markus C., Delorean, Emily, Thambugala, Dinushika, Klymiuk, Valentyna, Byrns, Brook, Gundlach, Heidrun, Bandi, Venkat, Siri, Jorge Nunez, Nilsen, Kirby, Aquino, Catharine, Himmelbach, Axel, Copetti, Dario, Ban, Tomohiro, Venturini, Luca, Bevan, Michael, Clavijo, Bernardo, Koo, Dal-Hoe, Ens, Jennifer, Wiebe, Krystalee, N’Diaye, Amidou, Fritz, Allen K., Gutwin, Carl, Fiebig, Anne, Fosker, Christine, Fu, Bin Xiao, Accinelli, Gonzalo Garcia, Gardner, Keith A., Fradgley, Nick, Gutierrez-Gonzalez, Juan, Halstead-Nussloch, Gwyneth, Hatakeyama, Masaomi, Koh, Chu Shin, Deek, Jasline, Costamagna, Alejandro C., Fobert, Pierre, Heavens, Darren, Kanamori, Hiroyuki, Kawaura, Kanako, Kobayashi, Fuminori, Krasileva, Ksenia, Kuo, Tony, McKenzie, Neil, Murata, Kazuki, Nabeka, Yusuke, Paape, Timothy, Padmarasu, Sudharsan, Percival-Alwyn, Lawrence, Kagale, Sateesh, Scholz, Uwe, Sese, Jun, Juliana, Philomin, Singh, Ravi, Shimizu-Inatsugi, Rie, Swarbreck, David, Cockram, James, Budak, Hikmet, Tameshige, Toshiaki, Tanaka, Tsuyoshi, Tsuji, Hiroyuki, Wright, Jonathan, Wu, Jianzhong, Steuernagel, Burkhard, Small, Ian, Cloutier, Sylvie, Keeble-Gagnère, Gabriel, Muehlbauer, Gary, Tibbets, Josquin, Nasuda, Shuhei, Melonek, Joanna, Hucl, Pierre J., Sharpe, Andrew G., Clark, Matthew, Legg, Erik, Bharti, Arvind, Langridge, Peter, Hall, Anthony, Uauy, Cristobal, Mascher, Martin, Krattinger, Simon G., Handa, Hirokazu, Shimizu, Kentaro K., Distelfeld, Assaf, Chalmers, Ken, Keller, Beat, Mayer, Klaus F. X., Poland, Jesse, Stein, Nils, McCartney, Curt A., Spannagl, Manuel, Wicker, Thomas, Pozniak, Curtis J., 70983196, 10273492, Walkowiak, Sean, Gao, Liangliang, Monat, Cecile, Haberer, Georg, Kassa, Mulualem T., Brinton, Jemima, Ramirez-Gonzalez, Ricardo H., Kolodziej, Markus C., Delorean, Emily, Thambugala, Dinushika, Klymiuk, Valentyna, Byrns, Brook, Gundlach, Heidrun, Bandi, Venkat, Siri, Jorge Nunez, Nilsen, Kirby, Aquino, Catharine, Himmelbach, Axel, Copetti, Dario, Ban, Tomohiro, Venturini, Luca, Bevan, Michael, Clavijo, Bernardo, Koo, Dal-Hoe, Ens, Jennifer, Wiebe, Krystalee, N’Diaye, Amidou, Fritz, Allen K., Gutwin, Carl, Fiebig, Anne, Fosker, Christine, Fu, Bin Xiao, Accinelli, Gonzalo Garcia, Gardner, Keith A., Fradgley, Nick, Gutierrez-Gonzalez, Juan, Halstead-Nussloch, Gwyneth, Hatakeyama, Masaomi, Koh, Chu Shin, Deek, Jasline, Costamagna, Alejandro C., Fobert, Pierre, Heavens, Darren, Kanamori, Hiroyuki, Kawaura, Kanako, Kobayashi, Fuminori, Krasileva, Ksenia, Kuo, Tony, McKenzie, Neil, Murata, Kazuki, Nabeka, Yusuke, Paape, Timothy, Padmarasu, Sudharsan, Percival-Alwyn, Lawrence, Kagale, Sateesh, Scholz, Uwe, Sese, Jun, Juliana, Philomin, Singh, Ravi, Shimizu-Inatsugi, Rie, Swarbreck, David, Cockram, James, Budak, Hikmet, Tameshige, Toshiaki, Tanaka, Tsuyoshi, Tsuji, Hiroyuki, Wright, Jonathan, Wu, Jianzhong, Steuernagel, Burkhard, Small, Ian, Cloutier, Sylvie, Keeble-Gagnère, Gabriel, Muehlbauer, Gary, Tibbets, Josquin, Nasuda, Shuhei, Melonek, Joanna, Hucl, Pierre J., Sharpe, Andrew G., Clark, Matthew, Legg, Erik, Bharti, Arvind, Langridge, Peter, Hall, Anthony, Uauy, Cristobal, Mascher, Martin, Krattinger, Simon G., Handa, Hirokazu, Shimizu, Kentaro K., Distelfeld, Assaf, Chalmers, Ken, Keller, Beat, Mayer, Klaus F. X., Poland, Jesse, Stein, Nils, McCartney, Curt A., Spannagl, Manuel, Wicker, Thomas, and Pozniak, Curtis J.

Abstract

Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome, and the lack of genome-assembly data for multiple wheat lines. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm1, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.

Published
2020

2. Multiple wheat genomes reveal global variation in modern breeding.

Author

Walkowiak, Sean, Walkowiak, Sean, Gao, Liangliang, Monat, Cecile, Haberer, Georg, Kassa, Mulualem T, Brinton, Jemima, Ramirez-Gonzalez, Ricardo H, Kolodziej, Markus C, Delorean, Emily, Thambugala, Dinushika, Klymiuk, Valentyna, Byrns, Brook, Gundlach, Heidrun, Bandi, Venkat, Siri, Jorge Nunez, Nilsen, Kirby, Aquino, Catharine, Himmelbach, Axel, Copetti, Dario, Ban, Tomohiro, Venturini, Luca, Bevan, Michael, Clavijo, Bernardo, Koo, Dal-Hoe, Ens, Jennifer, Wiebe, Krystalee, N'Diaye, Amidou, Fritz, Allen K, Gutwin, Carl, Fiebig, Anne, Fosker, Christine, Fu, Bin Xiao, Accinelli, Gonzalo Garcia, Gardner, Keith A, Fradgley, Nick, Gutierrez-Gonzalez, Juan, Halstead-Nussloch, Gwyneth, Hatakeyama, Masaomi, Koh, Chu Shin, Deek, Jasline, Costamagna, Alejandro C, Fobert, Pierre, Heavens, Darren, Kanamori, Hiroyuki, Kawaura, Kanako, Kobayashi, Fuminori, Krasileva, Ksenia, Kuo, Tony, McKenzie, Neil, Murata, Kazuki, Nabeka, Yusuke, Paape, Timothy, Padmarasu, Sudharsan, Percival-Alwyn, Lawrence, Kagale, Sateesh, Scholz, Uwe, Sese, Jun, Juliana, Philomin, Singh, Ravi, Shimizu-Inatsugi, Rie, Swarbreck, David, Cockram, James, Budak, Hikmet, Tameshige, Toshiaki, Tanaka, Tsuyoshi, Tsuji, Hiroyuki, Wright, Jonathan, Wu, Jianzhong, Steuernagel, Burkhard, Small, Ian, Cloutier, Sylvie, Keeble-Gagnère, Gabriel, Muehlbauer, Gary, Tibbets, Josquin, Nasuda, Shuhei, Melonek, Joanna, Hucl, Pierre J, Sharpe, Andrew G, Clark, Matthew, Legg, Erik, Bharti, Arvind, Langridge, Peter, Hall, Anthony, Uauy, Cristobal, Mascher, Martin, Krattinger, Simon G, Handa, Hirokazu, Shimizu, Kentaro K, Distelfeld, Assaf, Chalmers, Ken, Keller, Beat, Mayer, Klaus FX, Poland, Jesse, Stein, Nils, McCartney, Curt A, Spannagl, Manuel, Wicker, Thomas, Pozniak, Curtis J, Walkowiak, Sean, Walkowiak, Sean, Gao, Liangliang, Monat, Cecile, Haberer, Georg, Kassa, Mulualem T, Brinton, Jemima, Ramirez-Gonzalez, Ricardo H, Kolodziej, Markus C, Delorean, Emily, Thambugala, Dinushika, Klymiuk, Valentyna, Byrns, Brook, Gundlach, Heidrun, Bandi, Venkat, Siri, Jorge Nunez, Nilsen, Kirby, Aquino, Catharine, Himmelbach, Axel, Copetti, Dario, Ban, Tomohiro, Venturini, Luca, Bevan, Michael, Clavijo, Bernardo, Koo, Dal-Hoe, Ens, Jennifer, Wiebe, Krystalee, N'Diaye, Amidou, Fritz, Allen K, Gutwin, Carl, Fiebig, Anne, Fosker, Christine, Fu, Bin Xiao, Accinelli, Gonzalo Garcia, Gardner, Keith A, Fradgley, Nick, Gutierrez-Gonzalez, Juan, Halstead-Nussloch, Gwyneth, Hatakeyama, Masaomi, Koh, Chu Shin, Deek, Jasline, Costamagna, Alejandro C, Fobert, Pierre, Heavens, Darren, Kanamori, Hiroyuki, Kawaura, Kanako, Kobayashi, Fuminori, Krasileva, Ksenia, Kuo, Tony, McKenzie, Neil, Murata, Kazuki, Nabeka, Yusuke, Paape, Timothy, Padmarasu, Sudharsan, Percival-Alwyn, Lawrence, Kagale, Sateesh, Scholz, Uwe, Sese, Jun, Juliana, Philomin, Singh, Ravi, Shimizu-Inatsugi, Rie, Swarbreck, David, Cockram, James, Budak, Hikmet, Tameshige, Toshiaki, Tanaka, Tsuyoshi, Tsuji, Hiroyuki, Wright, Jonathan, Wu, Jianzhong, Steuernagel, Burkhard, Small, Ian, Cloutier, Sylvie, Keeble-Gagnère, Gabriel, Muehlbauer, Gary, Tibbets, Josquin, Nasuda, Shuhei, Melonek, Joanna, Hucl, Pierre J, Sharpe, Andrew G, Clark, Matthew, Legg, Erik, Bharti, Arvind, Langridge, Peter, Hall, Anthony, Uauy, Cristobal, Mascher, Martin, Krattinger, Simon G, Handa, Hirokazu, Shimizu, Kentaro K, Distelfeld, Assaf, Chalmers, Ken, Keller, Beat, Mayer, Klaus FX, Poland, Jesse, Stein, Nils, McCartney, Curt A, Spannagl, Manuel, Wicker, Thomas, and Pozniak, Curtis J

Abstract

Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome1, and the lack of genome-assembly data for multiple wheat lines2,3. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses4,5. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm16, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.

Published
2020

3. Multiple wheat genomes reveal global variation in modern breeding.

Author

Walkowiak, Sean, Walkowiak, Sean, Gao, Liangliang, Monat, Cecile, Haberer, Georg, Kassa, Mulualem T, Brinton, Jemima, Ramirez-Gonzalez, Ricardo H, Kolodziej, Markus C, Delorean, Emily, Thambugala, Dinushika, Klymiuk, Valentyna, Byrns, Brook, Gundlach, Heidrun, Bandi, Venkat, Siri, Jorge Nunez, Nilsen, Kirby, Aquino, Catharine, Himmelbach, Axel, Copetti, Dario, Ban, Tomohiro, Venturini, Luca, Bevan, Michael, Clavijo, Bernardo, Koo, Dal-Hoe, Ens, Jennifer, Wiebe, Krystalee, N'Diaye, Amidou, Fritz, Allen K, Gutwin, Carl, Fiebig, Anne, Fosker, Christine, Fu, Bin Xiao, Accinelli, Gonzalo Garcia, Gardner, Keith A, Fradgley, Nick, Gutierrez-Gonzalez, Juan, Halstead-Nussloch, Gwyneth, Hatakeyama, Masaomi, Koh, Chu Shin, Deek, Jasline, Costamagna, Alejandro C, Fobert, Pierre, Heavens, Darren, Kanamori, Hiroyuki, Kawaura, Kanako, Kobayashi, Fuminori, Krasileva, Ksenia, Kuo, Tony, McKenzie, Neil, Murata, Kazuki, Nabeka, Yusuke, Paape, Timothy, Padmarasu, Sudharsan, Percival-Alwyn, Lawrence, Kagale, Sateesh, Scholz, Uwe, Sese, Jun, Juliana, Philomin, Singh, Ravi, Shimizu-Inatsugi, Rie, Swarbreck, David, Cockram, James, Budak, Hikmet, Tameshige, Toshiaki, Tanaka, Tsuyoshi, Tsuji, Hiroyuki, Wright, Jonathan, Wu, Jianzhong, Steuernagel, Burkhard, Small, Ian, Cloutier, Sylvie, Keeble-Gagnère, Gabriel, Muehlbauer, Gary, Tibbets, Josquin, Nasuda, Shuhei, Melonek, Joanna, Hucl, Pierre J, Sharpe, Andrew G, Clark, Matthew, Legg, Erik, Bharti, Arvind, Langridge, Peter, Hall, Anthony, Uauy, Cristobal, Mascher, Martin, Krattinger, Simon G, Handa, Hirokazu, Shimizu, Kentaro K, Distelfeld, Assaf, Chalmers, Ken, Keller, Beat, Mayer, Klaus FX, Poland, Jesse, Stein, Nils, McCartney, Curt A, Spannagl, Manuel, Wicker, Thomas, Pozniak, Curtis J, Walkowiak, Sean, Walkowiak, Sean, Gao, Liangliang, Monat, Cecile, Haberer, Georg, Kassa, Mulualem T, Brinton, Jemima, Ramirez-Gonzalez, Ricardo H, Kolodziej, Markus C, Delorean, Emily, Thambugala, Dinushika, Klymiuk, Valentyna, Byrns, Brook, Gundlach, Heidrun, Bandi, Venkat, Siri, Jorge Nunez, Nilsen, Kirby, Aquino, Catharine, Himmelbach, Axel, Copetti, Dario, Ban, Tomohiro, Venturini, Luca, Bevan, Michael, Clavijo, Bernardo, Koo, Dal-Hoe, Ens, Jennifer, Wiebe, Krystalee, N'Diaye, Amidou, Fritz, Allen K, Gutwin, Carl, Fiebig, Anne, Fosker, Christine, Fu, Bin Xiao, Accinelli, Gonzalo Garcia, Gardner, Keith A, Fradgley, Nick, Gutierrez-Gonzalez, Juan, Halstead-Nussloch, Gwyneth, Hatakeyama, Masaomi, Koh, Chu Shin, Deek, Jasline, Costamagna, Alejandro C, Fobert, Pierre, Heavens, Darren, Kanamori, Hiroyuki, Kawaura, Kanako, Kobayashi, Fuminori, Krasileva, Ksenia, Kuo, Tony, McKenzie, Neil, Murata, Kazuki, Nabeka, Yusuke, Paape, Timothy, Padmarasu, Sudharsan, Percival-Alwyn, Lawrence, Kagale, Sateesh, Scholz, Uwe, Sese, Jun, Juliana, Philomin, Singh, Ravi, Shimizu-Inatsugi, Rie, Swarbreck, David, Cockram, James, Budak, Hikmet, Tameshige, Toshiaki, Tanaka, Tsuyoshi, Tsuji, Hiroyuki, Wright, Jonathan, Wu, Jianzhong, Steuernagel, Burkhard, Small, Ian, Cloutier, Sylvie, Keeble-Gagnère, Gabriel, Muehlbauer, Gary, Tibbets, Josquin, Nasuda, Shuhei, Melonek, Joanna, Hucl, Pierre J, Sharpe, Andrew G, Clark, Matthew, Legg, Erik, Bharti, Arvind, Langridge, Peter, Hall, Anthony, Uauy, Cristobal, Mascher, Martin, Krattinger, Simon G, Handa, Hirokazu, Shimizu, Kentaro K, Distelfeld, Assaf, Chalmers, Ken, Keller, Beat, Mayer, Klaus FX, Poland, Jesse, Stein, Nils, McCartney, Curt A, Spannagl, Manuel, Wicker, Thomas, and Pozniak, Curtis J

Abstract

Advances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome1, and the lack of genome-assembly data for multiple wheat lines2,3. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses4,5. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm16, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.

Published
2020

4. Shifting the limits in wheat research and breeding using a fully annotated reference genome

Author

Appels, Rudi, Eversole, Kellye, Stein, Nils, Feuillet, Catherine, Keller, Beat, Rogers, Jane, Pozniak, Curtis J., Choulet, Frédéric, Distelfeld, Assaf, Poland, Jesse, Ronen, Gil, Sharpe, Andrew G., Barad, Omer, Baruch, Kobi, Keeble-gagnère, Gabriel, Mascher, Martin, Ben-zvi, Gil, Josselin, Ambre-aurore, Himmelbach, Axel, Balfourier, François, Gutierrez-gonzalez, Juan, Hayden, Matthew, Koh, Chushin, Muehlbauer, Gary, Pasam, Raj K., Paux, Etienne, Rigault, Philippe, Tibbits, Josquin, Tiwari, Vijay, Spannagl, Manuel, Lang, Daniel, Gundlach, Heidrun, Haberer, Georg, Mayer, Klaus F. X., Ormanbekova, Danara, Prade, Verena, Šimková, Hana, Wicker, Thomas, Swarbreck, David, Rimbert, Hélène, Felder, Marius, Guilhot, Nicolas, Kaithakottil, Gemy, Keilwagen, Jens, Leroy, Philippe, Lux, Thomas, Twardziok, Sven, Venturini, Luca, Juhász, Angéla, Abrouk, Michael, Fischer, Iris, Uauy, Cristobal, Borrill, Philippa, Ramirez-gonzalez, Ricardo H., Arnaud, Dominique, Chalabi, Smahane, Chalhoub, Boulos, Cory, Aron, Datla, Raju, Davey, Mark W., Jacobs, John, Robinson, Stephen J., Steuernagel, Burkhard, Van Ex, Fred, Wulff, Brande B. H., Benhamed, Moussa, Bendahmane, Abdelhafid, Concia, Lorenzo, Latrasse, David, Bartoš, Jan, Bellec, Arnaud, Berges, Hélène, Doležel, Jaroslav, Frenkel, Zeev, Gill, Bikram, Korol, Abraham, Letellier, Thomas, Olsen, Odd-arne, Singh, Kuldeep, Valárik, Miroslav, Van Der Vossen, Edwin, Vautrin, Sonia, Weining, Song, Fahima, Tzion, Glikson, Vladimir, Raats, Dina, Číhalíková, Jarmila, Toegelová, Helena, Vrána, Jan, Sourdille, Pierre, Darrier, Benoit, Barabaschi, Delfina, Cattivelli, Luigi, Hernandez, Pilar, Galvez, Sergio, Budak, Hikmet, Jones, Jonathan D. G., Witek, Kamil, Yu, Guotai, Small, Ian, Melonek, Joanna, Zhou, Ruonan, Belova, Tatiana, Kanyuka, Kostya, King, Robert, Nilsen, Kirby, Walkowiak, Sean, Cuthbert, Richard, Knox, Ron, Wiebe, Krysta, Xiang, Daoquan, Rohde, Antje, Golds, Timothy, Čížková, Jana, Akpinar, Bala Ani, Biyiklioglu, Sezgi, Gao, Liangliang, N’daiye, Amidou, Kubaláková, Marie, Šafář, Jan, Alfama, Françoise, Adam-blondon, Anne-françoise, Flores, Raphael, Guerche, Claire, Loaec, Mikaël, Quesneville, Hadi, Condie, Janet, Ens, Jennifer, Maclachlan, Ron, Tan, Yifang, Alberti, Adriana, Aury, Jean-marc, Barbe, Valérie, Couloux, Arnaud, Cruaud, Corinne, Labadie, Karine, Mangenot, Sophie, Wincker, Patrick, Kaur, Gaganpreet, Luo, Mingcheng, Sehgal, Sunish, Chhuneja, Parveen, Gupta, Om Prakash, Jindal, Suruchi, Kaur, Parampreet, Malik, Palvi, Sharma, Priti, Yadav, Bharat, Singh, Nagendra K., Khurana, Jitendra P., Chaudhary, Chanderkant, Khurana, Paramjit, Kumar, Vinod, Mahato, Ajay, Mathur, Saloni, Sevanthi, Amitha, Sharma, Naveen, Tomar, Ram Sewak, Holušová, Kateřina, Plíhal, Ondřej, Clark, Matthew D., Heavens, Darren, Kettleborough, George, Wright, Jon, Balcárková, Barbora, Hu, Yuqin, Salina, Elena, Ravin, Nikolai, Skryabin, Konstantin, Beletsky, Alexey, Kadnikov, Vitaly, Mardanov, Andrey, Nesterov, Michail, Rakitin, Andrey, Sergeeva, Ekaterina, Handa, Hirokazu, Kanamori, Hiroyuki, Katagiri, Satoshi, Kobayashi, Fuminori, Nasuda, Shuhei, Tanaka, Tsuyoshi, Wu, Jianzhong, Cattonaro, Federica, Jiumeng, Min, Kugler, Karl, Pfeifer, Matthias, Sandve, Simen, Xun, Xu, Zhan, Bujie, Batley, Jacqueline, Bayer, Philipp E., Edwards, David, Hayashi, Satomi, Tulpová, Zuzana, Visendi, Paul, Cui, Licao, Du, Xianghong, Feng, Kewei, Nie, Xiaojun, Tong, Wei, Wang, Le, Appels, Rudi, Eversole, Kellye, Stein, Nils, Feuillet, Catherine, Keller, Beat, Rogers, Jane, Pozniak, Curtis J., Choulet, Frédéric, Distelfeld, Assaf, Poland, Jesse, Ronen, Gil, Sharpe, Andrew G., Barad, Omer, Baruch, Kobi, Keeble-gagnère, Gabriel, Mascher, Martin, Ben-zvi, Gil, Josselin, Ambre-aurore, Himmelbach, Axel, Balfourier, François, Gutierrez-gonzalez, Juan, Hayden, Matthew, Koh, Chushin, Muehlbauer, Gary, Pasam, Raj K., Paux, Etienne, Rigault, Philippe, Tibbits, Josquin, Tiwari, Vijay, Spannagl, Manuel, Lang, Daniel, Gundlach, Heidrun, Haberer, Georg, Mayer, Klaus F. X., Ormanbekova, Danara, Prade, Verena, Šimková, Hana, Wicker, Thomas, Swarbreck, David, Rimbert, Hélène, Felder, Marius, Guilhot, Nicolas, Kaithakottil, Gemy, Keilwagen, Jens, Leroy, Philippe, Lux, Thomas, Twardziok, Sven, Venturini, Luca, Juhász, Angéla, Abrouk, Michael, Fischer, Iris, Uauy, Cristobal, Borrill, Philippa, Ramirez-gonzalez, Ricardo H., Arnaud, Dominique, Chalabi, Smahane, Chalhoub, Boulos, Cory, Aron, Datla, Raju, Davey, Mark W., Jacobs, John, Robinson, Stephen J., Steuernagel, Burkhard, Van Ex, Fred, Wulff, Brande B. H., Benhamed, Moussa, Bendahmane, Abdelhafid, Concia, Lorenzo, Latrasse, David, Bartoš, Jan, Bellec, Arnaud, Berges, Hélène, Doležel, Jaroslav, Frenkel, Zeev, Gill, Bikram, Korol, Abraham, Letellier, Thomas, Olsen, Odd-arne, Singh, Kuldeep, Valárik, Miroslav, Van Der Vossen, Edwin, Vautrin, Sonia, Weining, Song, Fahima, Tzion, Glikson, Vladimir, Raats, Dina, Číhalíková, Jarmila, Toegelová, Helena, Vrána, Jan, Sourdille, Pierre, Darrier, Benoit, Barabaschi, Delfina, Cattivelli, Luigi, Hernandez, Pilar, Galvez, Sergio, Budak, Hikmet, Jones, Jonathan D. G., Witek, Kamil, Yu, Guotai, Small, Ian, Melonek, Joanna, Zhou, Ruonan, Belova, Tatiana, Kanyuka, Kostya, King, Robert, Nilsen, Kirby, Walkowiak, Sean, Cuthbert, Richard, Knox, Ron, Wiebe, Krysta, Xiang, Daoquan, Rohde, Antje, Golds, Timothy, Čížková, Jana, Akpinar, Bala Ani, Biyiklioglu, Sezgi, Gao, Liangliang, N’daiye, Amidou, Kubaláková, Marie, Šafář, Jan, Alfama, Françoise, Adam-blondon, Anne-françoise, Flores, Raphael, Guerche, Claire, Loaec, Mikaël, Quesneville, Hadi, Condie, Janet, Ens, Jennifer, Maclachlan, Ron, Tan, Yifang, Alberti, Adriana, Aury, Jean-marc, Barbe, Valérie, Couloux, Arnaud, Cruaud, Corinne, Labadie, Karine, Mangenot, Sophie, Wincker, Patrick, Kaur, Gaganpreet, Luo, Mingcheng, Sehgal, Sunish, Chhuneja, Parveen, Gupta, Om Prakash, Jindal, Suruchi, Kaur, Parampreet, Malik, Palvi, Sharma, Priti, Yadav, Bharat, Singh, Nagendra K., Khurana, Jitendra P., Chaudhary, Chanderkant, Khurana, Paramjit, Kumar, Vinod, Mahato, Ajay, Mathur, Saloni, Sevanthi, Amitha, Sharma, Naveen, Tomar, Ram Sewak, Holušová, Kateřina, Plíhal, Ondřej, Clark, Matthew D., Heavens, Darren, Kettleborough, George, Wright, Jon, Balcárková, Barbora, Hu, Yuqin, Salina, Elena, Ravin, Nikolai, Skryabin, Konstantin, Beletsky, Alexey, Kadnikov, Vitaly, Mardanov, Andrey, Nesterov, Michail, Rakitin, Andrey, Sergeeva, Ekaterina, Handa, Hirokazu, Kanamori, Hiroyuki, Katagiri, Satoshi, Kobayashi, Fuminori, Nasuda, Shuhei, Tanaka, Tsuyoshi, Wu, Jianzhong, Cattonaro, Federica, Jiumeng, Min, Kugler, Karl, Pfeifer, Matthias, Sandve, Simen, Xun, Xu, Zhan, Bujie, Batley, Jacqueline, Bayer, Philipp E., Edwards, David, Hayashi, Satomi, Tulpová, Zuzana, Visendi, Paul, Cui, Licao, Du, Xianghong, Feng, Kewei, Nie, Xiaojun, Tong, Wei, and Wang, Le

Abstract

Wheat is one of the major sources of food for much of the world. However, because bread wheat's genome is a large hybrid mix of three separate subgenomes, it has been difficult to produce a high-quality reference sequence. Using recent advances in sequencing, the International Wheat Genome Sequencing Consortium presents an annotated reference genome with a detailed analysis of gene content among subgenomes and the structural organization for all the chromosomes. Examples of quantitative trait mapping and CRISPR-based genome modification show the potential for using this genome in agricultural research and breeding. Ramírez-González et al. exploited the fruits of this endeavor to identify tissue-specific biased gene expression and coexpression networks during development and exposure to stress. These resources will accelerate our understanding of the genetic basis of bread wheat.

Published
2018

5. Scaling associative classification for very large datasets

Author

Venturini, Luca, Baralis, Elena, Garza, Paolo, Venturini, Luca, Baralis, Elena, and Garza, Paolo

Abstract

Supervised learning algorithms are nowadays successfully scaling up to datasets that are very large in volume, leveraging the potential of in-memory cluster-computing Big Data frameworks. Still, massive datasets with a number of large-domain categorical features are a difficult challenge for any classifier. Most off-the-shelf solutions cannot cope with this problem. In this work we introduce DAC, a Distributed Associative Classifier. DAC exploits ensemble learning to distribute the training of an associative classifier among parallel workers and improve the final quality of the model. Furthermore, it adopts several novel techniques to reach high scalability without sacrificing quality, among which a preventive pruning of classification rules in the extraction phase based on Gini impurity. We ran experiments on Apache Spark, on a real large-scale dataset with more than 4 billion records and 800 million distinct categories. The results showed that DAC improves on a state-of-the-art solution in both prediction quality and execution time. Since the generated model is human-readable, it can not only classify new records, but also allow understanding both the logic behind the prediction and the properties of the model, becoming a useful aid for decision makers.

Published
2018
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6. An improved assembly and annotation of the allohexaploid wheat genome identifies complete families of agronomic genes and provides genomic evidence for chromosomal translocations.

Author

Clavijo, Bernardo J, Clavijo, Bernardo J, Venturini, Luca, Schudoma, Christian, Accinelli, Gonzalo Garcia, Kaithakottil, Gemy, Wright, Jonathan, Borrill, Philippa, Kettleborough, George, Heavens, Darren, Chapman, Helen, Lipscombe, James, Barker, Tom, Lu, Fu-Hao, McKenzie, Neil, Raats, Dina, Ramirez-Gonzalez, Ricardo H, Coince, Aurore, Peel, Ned, Percival-Alwyn, Lawrence, Duncan, Owen, Trösch, Josua, Yu, Guotai, Bolser, Dan M, Namaati, Guy, Kerhornou, Arnaud, Spannagl, Manuel, Gundlach, Heidrun, Haberer, Georg, Davey, Robert P, Fosker, Christine, Palma, Federica Di, Phillips, Andrew L, Millar, A Harvey, Kersey, Paul J, Uauy, Cristobal, Krasileva, Ksenia V, Swarbreck, David, Bevan, Michael W, Clark, Matthew D, Clavijo, Bernardo J, Clavijo, Bernardo J, Venturini, Luca, Schudoma, Christian, Accinelli, Gonzalo Garcia, Kaithakottil, Gemy, Wright, Jonathan, Borrill, Philippa, Kettleborough, George, Heavens, Darren, Chapman, Helen, Lipscombe, James, Barker, Tom, Lu, Fu-Hao, McKenzie, Neil, Raats, Dina, Ramirez-Gonzalez, Ricardo H, Coince, Aurore, Peel, Ned, Percival-Alwyn, Lawrence, Duncan, Owen, Trösch, Josua, Yu, Guotai, Bolser, Dan M, Namaati, Guy, Kerhornou, Arnaud, Spannagl, Manuel, Gundlach, Heidrun, Haberer, Georg, Davey, Robert P, Fosker, Christine, Palma, Federica Di, Phillips, Andrew L, Millar, A Harvey, Kersey, Paul J, Uauy, Cristobal, Krasileva, Ksenia V, Swarbreck, David, Bevan, Michael W, and Clark, Matthew D

Abstract

Advances in genome sequencing and assembly technologies are generating many high-quality genome sequences, but assemblies of large, repeat-rich polyploid genomes, such as that of bread wheat, remain fragmented and incomplete. We have generated a new wheat whole-genome shotgun sequence assembly using a combination of optimized data types and an assembly algorithm designed to deal with large and complex genomes. The new assembly represents >78% of the genome with a scaffold N50 of 88.8 kb that has a high fidelity to the input data. Our new annotation combines strand-specific Illumina RNA-seq and Pacific Biosciences (PacBio) full-length cDNAs to identify 104,091 high-confidence protein-coding genes and 10,156 noncoding RNA genes. We confirmed three known and identified one novel genome rearrangements. Our approach enables the rapid and scalable assembly of wheat genomes, the identification of structural variants, and the definition of complete gene models, all powerful resources for trait analysis and breeding of this key global crop.

Published
2017

7. Gemello digitale di un ponte in muratura mediante analisi modale operazionale e algoritmi genetici

Author

Venturini, Luca, thesis supervisor: Marzani, Alessandro, Venturini, Luca, and thesis supervisor: Marzani, Alessandro

Abstract

Il monitoraggio strutturale è un processo che, nell’ambito ingegneristico, mira a identificare la nascita e/o l’evoluzione del danno nelle strutture. Il paradigma prevede l’installazione di sensori sulla struttura, per monitorarne nel tempo alcuni parametri, e l’utilizzo di modelli strutturali e algoritmi che a partire dai dati misurati siano in grado di rilevarne eventuali danni. In seno al monitoraggio strutturale vive il problema di identificazione strutturale, cioè l’insieme di strategie analitiche e sperimentali atte a calibrare i modelli strutturali, quindi a generare dei gemelli digitali delle strutture. Queste strategie si basano sull'estrazione di caratteristiche dai dati misurati, con tecniche di identificazione quali la Analisi Modale Sperimentale (EMA) e l’Analisi Modale Operazionale (OMA), e di schemi di ottimizzazione euristici che operano allo scopo di “aggiornare” i modelli strutturali. La tesi si è focalizzata sulla generazione di un gemello digitale, ai fini del monitoraggio strutturale, di un ponte ferroviario multi-campata in muratura, sulla linea ferroviaria Foggia-Napoli. Attraverso l’analisi statica e dinamica, condotte su due modelli a elementi finiti (FEM) realizzati con i software Abaqus e SAP2000, è stato possibile calcolare dati quali le frequenze naturali e le deformate modali (caratteristiche del ponte iniziale). Si è poi proceduto a definire possibili scenari di danno variando la rigidezza di alcune parti/porzioni del ponte, utilizzati poi per estrarre i dati dei ponti danneggiati (caratteristiche del ponte danneggiato). Attraverso uno schema agli algoritmi genetici, il modello iniziale è stato aggiornato generando il gemello digitale del ponte danneggiato. L’algoritmo genetico implementato sul software Matlab prevede un processo iterativo volto a minimizzare una funzione obiettivo basata sulla differenza tra le frequenze del ponte iniziale e quelle del ponte danneggiato, al variale di alcuni parametri fisici del modello.

8. Gemello digitale di un ponte in muratura mediante analisi modale operazionale e algoritmi genetici

Author

Venturini, Luca, thesis supervisor: Marzani, Alessandro, Venturini, Luca, and thesis supervisor: Marzani, Alessandro

Abstract

Il monitoraggio strutturale è un processo che, nell’ambito ingegneristico, mira a identificare la nascita e/o l’evoluzione del danno nelle strutture. Il paradigma prevede l’installazione di sensori sulla struttura, per monitorarne nel tempo alcuni parametri, e l’utilizzo di modelli strutturali e algoritmi che a partire dai dati misurati siano in grado di rilevarne eventuali danni. In seno al monitoraggio strutturale vive il problema di identificazione strutturale, cioè l’insieme di strategie analitiche e sperimentali atte a calibrare i modelli strutturali, quindi a generare dei gemelli digitali delle strutture. Queste strategie si basano sull'estrazione di caratteristiche dai dati misurati, con tecniche di identificazione quali la Analisi Modale Sperimentale (EMA) e l’Analisi Modale Operazionale (OMA), e di schemi di ottimizzazione euristici che operano allo scopo di “aggiornare” i modelli strutturali. La tesi si è focalizzata sulla generazione di un gemello digitale, ai fini del monitoraggio strutturale, di un ponte ferroviario multi-campata in muratura, sulla linea ferroviaria Foggia-Napoli. Attraverso l’analisi statica e dinamica, condotte su due modelli a elementi finiti (FEM) realizzati con i software Abaqus e SAP2000, è stato possibile calcolare dati quali le frequenze naturali e le deformate modali (caratteristiche del ponte iniziale). Si è poi proceduto a definire possibili scenari di danno variando la rigidezza di alcune parti/porzioni del ponte, utilizzati poi per estrarre i dati dei ponti danneggiati (caratteristiche del ponte danneggiato). Attraverso uno schema agli algoritmi genetici, il modello iniziale è stato aggiornato generando il gemello digitale del ponte danneggiato. L’algoritmo genetico implementato sul software Matlab prevede un processo iterativo volto a minimizzare una funzione obiettivo basata sulla differenza tra le frequenze del ponte iniziale e quelle del ponte danneggiato, al variale di alcuni parametri fisici del modello.

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