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5,563 results on '"Fernie, Alisdair"'

8. Genomic insight into the origin, domestication, dispersal, diversification and human selection of Tartary buckwheat

Author

He, Yuqi, Zhang, Kaixuan, Shi, Yaliang, Lin, Hao, Huang, Xu, Lu, Xiang, Wang, Zhirong, Li, Wei, Feng, Xibo, Shi, Taoxiong, Chen, Qingfu, Wang, Junzhen, Tang, Yu, Chapman, Mark A., Germ, Mateja, Luthar, Zlata, Kreft, Ivan, Janovská, Dagmar, Meglič, Vladimir, Woo, Sun-Hee, Quinet, Muriel, Fernie, Alisdair R., Liu, Xu, and Zhou, Meiliang

Published
2024
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14. Selection and adaptive introgression guided the complex evolutionary history of the European common bean

Author

Bellucci, Elisa, Benazzo, Andrea, Xu, Chunming, Bitocchi, Elena, Rodriguez, Monica, Alseekh, Saleh, Di Vittori, Valerio, Gioia, Tania, Neumann, Kerstin, Cortinovis, Gaia, Frascarelli, Giulia, Murube, Ester, Trucchi, Emiliano, Nanni, Laura, Ariani, Andrea, Logozzo, Giuseppina, Shin, Jin Hee, Liu, Chaochih, Jiang, Liang, Ferreira, Juan José, Campa, Ana, Attene, Giovanna, Morrell, Peter L, Bertorelle, Giorgio, Graner, Andreas, Gepts, Paul, Fernie, Alisdair R, Jackson, Scott A, and Papa, Roberto

Subjects
Biological Sciences, Genetics, Human Genome, Humans, Phaseolus, Genetic Variation, Genotype, Biological Evolution, Hybridization, Genetic
Abstract

Domesticated crops have been disseminated by humans over vast geographic areas. Common bean (Phaseolus vulgaris L.) was introduced in Europe after 1492. Here, by combining whole-genome profiling, metabolic fingerprinting and phenotypic characterisation, we show that the first common bean cultigens successfully introduced into Europe were of Andean origin, after Francisco Pizarro's expedition to northern Peru in 1529. We reveal that hybridisation, selection and recombination have shaped the genomic diversity of the European common bean in parallel with political constraints. There is clear evidence of adaptive introgression into the Mesoamerican-derived European genotypes, with 44 Andean introgressed genomic segments shared by more than 90% of European accessions and distributed across all chromosomes except PvChr11. Genomic scans for signatures of selection highlight the role of genes relevant to flowering and environmental adaptation, suggesting that introgression has been crucial for the dissemination of this tropical crop to the temperate regions of Europe.

Published
2023

37. The INCREASE project: Intelligent Collections of food‐legume genetic resources for European agrofood systems

Author

Bellucci, Elisa, Aguilar, Orlando Mario, Alseekh, Saleh, Bett, Kirstin, Brezeanu, Creola, Cook, Douglas, De la Rosa, Lucía, Delledonne, Massimo, Dostatny, Denise F, Ferreira, Juan J, Geffroy, Valérie, Ghitarrini, Sofia, Kroc, Magdalena, Agrawal, Shiv Kumar, Logozzo, Giuseppina, Marino, Mario, Mary‐Huard, Tristan, McClean, Phil, Meglič, Vladimir, Messer, Tamara, Muel, Frédéric, Nanni, Laura, Neumann, Kerstin, Servalli, Filippo, Străjeru, Silvia, Varshney, Rajeev K, Vasconcelos, Marta W, Zaccardelli, Massimo, Zavarzin, Aleksei, Bitocchi, Elena, Frontoni, Emanuele, Fernie, Alisdair R, Gioia, Tania, Graner, Andreas, Guasch, Luis, Prochnow, Lena, Oppermann, Markus, Susek, Karolina, Tenaillon, Maud, and Papa, Roberto

Subjects
Plant Biology, Biochemistry and Cell Biology, Biological Sciences, Zero Hunger, Climate Action, Crops, Agricultural, Databases, Genetic, Europe, Fabaceae, Genotype, International Cooperation, Seed Bank, Seeds, plant genetic resources, symbiosis, high-throughput phenotyping, artificial intelligence, metabolomics, Plant Biology & Botany, Biochemistry and cell biology, Plant biology
Abstract

Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.

Published
2021

44. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

Author

Aksenov, Alexander A, Laponogov, Ivan, Zhang, Zheng, Doran, Sophie LF, Belluomo, Ilaria, Veselkov, Dennis, Bittremieux, Wout, Nothias, Louis Felix, Nothias-Esposito, Mélissa, Maloney, Katherine N, Misra, Biswapriya B, Melnik, Alexey V, Smirnov, Aleksandr, Du, Xiuxia, Jones, Kenneth L, Dorrestein, Kathleen, Panitchpakdi, Morgan, Ernst, Madeleine, van der Hooft, Justin JJ, Gonzalez, Mabel, Carazzone, Chiara, Amézquita, Adolfo, Callewaert, Chris, Morton, James T, Quinn, Robert A, Bouslimani, Amina, Orio, Andrea Albarracín, Petras, Daniel, Smania, Andrea M, Couvillion, Sneha P, Burnet, Meagan C, Nicora, Carrie D, Zink, Erika, Metz, Thomas O, Artaev, Viatcheslav, Humston-Fulmer, Elizabeth, Gregor, Rachel, Meijler, Michael M, Mizrahi, Itzhak, Eyal, Stav, Anderson, Brooke, Dutton, Rachel, Lugan, Raphaël, Boulch, Pauline Le, Guitton, Yann, Prevost, Stephanie, Poirier, Audrey, Dervilly, Gaud, Le Bizec, Bruno, Fait, Aaron, Persi, Noga Sikron, Song, Chao, Gashu, Kelem, Coras, Roxana, Guma, Monica, Manasson, Julia, Scher, Jose U, Barupal, Dinesh Kumar, Alseekh, Saleh, Fernie, Alisdair R, Mirnezami, Reza, Vasiliou, Vasilis, Schmid, Robin, Borisov, Roman S, Kulikova, Larisa N, Knight, Rob, Wang, Mingxun, Hanna, George B, Dorrestein, Pieter C, and Veselkov, Kirill

Subjects
Animals, Anura, Humans, Algorithms, Gas Chromatography-Mass Spectrometry, Metabolomics
Abstract

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Published
2021

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