Smart breeding approaches in post-genomics era for developing climate-resilient food crops
暂无分享,去创建一个
I. Amin | M. Asif | S. Mansoor | Muhammad Farooq | Aiman Ehsan | Z. Mukhtar | M. A. Mahmood | H. Siddiqui | S. Asad | R. Naqvi | Syed Najeebullah | M. Azhar | Muhammad A. Asif | Syed Najeebullah
[1] N. Tuteja,et al. Genome editing (CRISPR-Cas)-mediated virus resistance in potato (Solanum tuberosum L.) , 2022, Molecular Biology Reports.
[2] Shizhong Xu,et al. Graph pangenome captures missing heritability and empowers tomato breeding , 2022, Nature.
[3] K. Nazari,et al. QTL Mapping of Adult Plant Resistance to Stripe Rust in a Doubled Haploid Wheat Population , 2022, Frontiers in Genetics.
[4] M. Kumar,et al. Germplasm, Breeding, and Genomics in Potato Improvement of Biotic and Abiotic Stresses Tolerance , 2022, Frontiers in Plant Science.
[5] M. Kumar,et al. CRISPR/Cas Genome Editing in Potato: Current Status and Future Perspectives , 2022, Frontiers in Genetics.
[6] S. Khan,et al. Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed , 2021, Biomolecules.
[7] M. Cáccamo,et al. Reap the crop wild relatives for breeding future crops. , 2021, Trends in biotechnology.
[8] R. Varshney,et al. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. , 2021, Trends in plant science.
[9] R. Varshney,et al. Can omics deliver temperature resilient ready-to-grow crops? , 2021, Critical reviews in biotechnology.
[10] Gurdeep Singh Malhi,et al. Impact of Climate Change on Agriculture and Its Mitigation Strategies: A Review , 2021, Sustainability.
[11] M. Bhatta,et al. Need for speed: manipulating plant growth to accelerate breeding cycles. , 2021, Current opinion in plant biology.
[12] Z. Nikoloski,et al. Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. , 2020, Journal of plant physiology.
[13] R. Singh,et al. Prospects of Improving Nitrogen Use Efficiency in Potato: Lessons From Transgenics to Genome Editing Strategies in Plants , 2020, Frontiers in Plant Science.
[14] Atul K. Jain,et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis , 2020, Science.
[15] G. Kootstra,et al. Machine learning in plant science and plant breeding , 2020, iScience.
[16] Jianbing Yan,et al. Application of deep learning in genomics , 2020, Science China Life Sciences.
[17] H. Dempewolf,et al. The Potential of Payment for Ecosystem Services for Crop Wild Relative Conservation , 2020, Plants.
[18] M. Gumma,et al. Harnessing wild relatives of pearl millet for germplasm enhancement: Challenges and opportunities , 2020, Crop Science.
[19] Raja Purushothaman,et al. Deep learning based assessment of disease severity for early blight in tomato crop , 2020, Multimedia Tools and Applications.
[20] G. Moghe,et al. Machine learning: A powerful tool for gene function prediction in plants , 2020, Applications in plant sciences.
[21] K. Glenn,et al. The role of conventional plant breeding in ensuring safe levels of naturally occurring toxins in food crops , 2020 .
[22] T. Widiez,et al. Puzzling out plant reproduction by haploid induction for innovations in plant breeding , 2020, Nature Plants.
[23] R. Singh,et al. Genome-wide identification and characterization of microRNAs by small RNA sequencing for low nitrogen stress in potato , 2020, PloS one.
[24] I. Żur,et al. Candidate Genes for Freezing and Drought Tolerance Selected on the Basis of Proteome Analysis in Doubled Haploid Lines of Barley , 2020, International journal of molecular sciences.
[25] R. Singh,et al. Transcriptome analysis of potato shoots, roots and stolons under nitrogen stress , 2020, Scientific Reports.
[26] E. Buckler,et al. Deep learning for plant genomics and crop improvement. , 2020, Current opinion in plant biology.
[27] P. Bayer,et al. Plant pangenomics: approaches, applications and advancements. , 2020, Current opinion in plant biology.
[28] D. Carputo,et al. Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding , 2019, Plants.
[29] F. Saleem,et al. Modern Trends in Plant Genome Editing: An Inclusive Review of the CRISPR/Cas9 Toolbox , 2019, International journal of molecular sciences.
[30] M. Rafii,et al. Drought Resistance in Rice from Conventional to Molecular Breeding: A Review , 2019, International journal of molecular sciences.
[31] Xin Zhang,et al. A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images , 2019, Remote. Sens..
[32] M. Tester,et al. Breeding crops to feed 10 billion , 2019, Nature Biotechnology.
[33] C. Gersbach,et al. The next generation of CRISPR–Cas technologies and applications , 2019, Nature Reviews Molecular Cell Biology.
[34] R. H. Mumm,et al. African Orphan Crops Consortium (AOCC): status of developing genomic resources for African orphan crops , 2019, Planta.
[35] D. Hunter,et al. The potential of neglected and underutilized species for improving diets and nutrition , 2019, Planta.
[36] Jong-Wook Kim,et al. A Comparative Study of Deep CNN in Forecasting and Classifying the Macronutrient Deficiencies on Development of Tomato Plant , 2019, Applied Sciences.
[37] S. Jackson,et al. Machine learning and complex biological data , 2019, Genome Biology.
[38] G. Fitzgerald,et al. Grain mineral quality of dryland legumes as affected by elevated CO2 and drought: a FACE study on lentil (Lens culinaris) and faba bean (Vicia faba) , 2019, Crop and Pasture Science.
[39] P. Kersey,et al. Plant genome sequences: past, present, future. , 2019, Current opinion in plant biology.
[40] Peter McCloskey,et al. A Mobile-Based Deep Learning Model for Cassava Disease Diagnosis , 2019, Front. Plant Sci..
[41] M. K. Mejía-Guerra,et al. A k-mer grammar analysis to uncover maize regulatory architecture , 2019, BMC Plant Biology.
[42] Gerrit Polder,et al. Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images , 2019, Front. Plant Sci..
[43] R. Jun,et al. Development and Application of CRISPR/Cas System in Rice , 2019, Rice Science.
[44] N. N. Das. Relevance of Poly-Omics in System Biology Studies of Industrial Crops , 2019, OMICS-Based Approaches in Plant Biotechnology.
[45] L. Hickey,et al. Q&A: modern crop breeding for future food security , 2019, BMC Biology.
[46] R. Henry,et al. Exploring and Exploiting Pan-genomics for Crop Improvement. , 2019, Molecular plant.
[47] N. Provart,et al. An updated gene atlas for maize reveals organ‐specific and stress‐induced genes , 2019, The Plant journal : for cell and molecular biology.
[48] Jacob D. Washburn,et al. Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence , 2019, Proceedings of the National Academy of Sciences.
[49] M. Gawłowska,et al. Production of wheat-doubled haploids resistant to eyespot supported by marker-assisted selection , 2019, Electronic Journal of Biotechnology.
[50] S. Rhee,et al. QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice , 2018, G3: Genes, Genomes, Genetics.
[51] R. Snowdon,et al. Connecting genome structural variation with complex traits in crop plants , 2018, Theoretical and Applied Genetics.
[52] A. Crane-Droesch. Machine learning methods for crop yield prediction and climate change impact assessment in agriculture , 2018, Environmental Research Letters.
[53] D. Edwards,et al. Bottlenecks for genome-edited crops on the road from lab to farm , 2018, Genome Biology.
[54] T. Iizumi,et al. Crop production losses associated with anthropogenic climate change for 1981–2010 compared with preindustrial levels , 2018, International Journal of Climatology.
[55] S. Lam,et al. Effects of Elevated CO2 on Nutritional Quality of Vegetables: A Review , 2018, Front. Plant Sci..
[56] K. Dassanayake,et al. Interaction of Elevated Carbon Dioxide and Temperature on Strawberry (Fragaria × ananassa) Growth and Fruit Yield , 2018 .
[57] Patrizia Busato,et al. Machine Learning in Agriculture: A Review , 2018, Sensors.
[58] Huwaida S. Rabie,et al. Mapping of novel salt tolerance QTL in an Excalibur × Kukri doubled haploid wheat population , 2018, Theoretical and Applied Genetics.
[59] Marcela A. Mendoza-Suárez,et al. Speed breeding in growth chambers and glasshouses for crop breeding and model plant research , 2018, bioRxiv.
[60] M. Otim,et al. Grain-yield stability among tropical maize hybrids derived from doubled-haploid inbred lines under random drought stress and optimum moisture conditions , 2018, Crop and Pasture Science.
[61] P. Larmande,et al. Evaluating Named-Entity Recognition approaches in plant molecular biology , 2018, bioRxiv.
[62] C. K. Chan,et al. Variation in abundance of predicted resistance genes in the Brassica oleracea pangenome , 2018, Plant biotechnology journal.
[63] M. Zaman-Allah,et al. Translating High-Throughput Phenotyping into Genetic Gain , 2018, Trends in plant science.
[64] Kenneth L. McNally,et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice , 2018, Nature.
[65] Baskar Ganapathysubramanian,et al. An explainable deep machine vision framework for plant stress phenotyping , 2018, Proceedings of the National Academy of Sciences.
[66] Zhi Wei,et al. DeepPolyA: A Convolutional Neural Network Approach for Polyadenylation Site Prediction , 2018, IEEE Access.
[67] W. Heyer,et al. Homologous recombination and the repair of DNA double-strand breaks , 2018, The Journal of Biological Chemistry.
[68] A. Rasheed,et al. Fast-Forwarding Genetic Gain. , 2018, Trends in plant science.
[69] Qun Xu,et al. Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice , 2018, Nature Genetics.
[70] C. K. Chan,et al. Homoeologous exchange is a major cause of gene presence/absence variation in the amphidiploid Brassica napus , 2018, Plant biotechnology journal.
[71] E. Waltz. With a free pass, CRISPR-edited plants reach market in record time , 2018, Nature Biotechnology.
[72] Wendy S. Schackwitz,et al. Extensive gene content variation in the Brachypodium distachyon pan-genome correlates with population structure , 2017, Nature Communications.
[73] Yang Lu,et al. Identification of rice diseases using deep convolutional neural networks , 2017, Neurocomputing.
[74] Armin Scheben,et al. Towards CRISPR/Cas crops - bringing together genomics and genome editing. , 2017, The New phytologist.
[75] G. de los Campos,et al. Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. , 2017, Trends in plant science.
[76] J. Yosinski,et al. Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. , 2017, Phytopathology.
[77] G. S. Miglani. Genome editing in crop improvement: Present scenario and future prospects , 2017 .
[78] C. Gardner,et al. Emerging Avenues for Utilization of Exotic Germplasm. , 2017, Trends in plant science.
[79] O. Mitrofanova,et al. New genetic resources in wheat breeding for increased grain protein content , 2017, Russian Journal of Genetics: Applied Research.
[80] C. K. Chan,et al. The pangenome of hexaploid bread wheat , 2017, The Plant journal : for cell and molecular biology.
[81] Bing Yang,et al. New variants of CRISPR RNA‐guided genome editing enzymes , 2017, Plant biotechnology journal.
[82] Justin E. Anderson,et al. Past and Future Use of Wild Relatives in Crop Breeding , 2017 .
[83] Xiao Zhang,et al. Genome-wide comparative analysis of NBS-encoding genes in four Gossypium species , 2017, BMC Genomics.
[84] Jiawei Zhao,et al. A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa) , 2017, The Plant journal : for cell and molecular biology.
[85] Armin Scheben,et al. Genome editors take on crops , 2017, Science.
[86] C. K. Chan,et al. The pangenome of an agronomically important crop plant Brassica oleracea , 2016, Nature Communications.
[87] S. Brady,et al. Plant developmental responses to climate change. , 2016, Developmental biology.
[88] V. Ravindra babu,et al. Marker assisted introgression of blast (Pi-2 and Pi-54) genes in to the genetic background of elite, bacterial blight resistant indica rice variety, Improved Samba Mahsuri , 2016, Euphytica.
[89] D. Edwards,et al. Advances in genomics for adapting crops to climate change , 2016 .
[90] R. Qin,et al. Rapid improvement of grain weight via highly efficient CRISPR/Cas9-mediated multiplex genome editing in rice. , 2016, Journal of genetics and genomics = Yi chuan xue bao.
[91] Hongyu Wang,et al. ARGOS8 variants generated by CRISPR‐Cas9 improve maize grain yield under field drought stress conditions , 2016, Plant biotechnology journal.
[92] K. Schmid,et al. Crossing Methods and Cultivation Conditions for Rapid Production of Segregating Populations in Three Grain Amaranth Species , 2016, Front. Plant Sci..
[93] Emily Waltz,et al. CRISPR-edited crops free to enter market, skip regulation , 2016, Nature Biotechnology.
[94] Emily Waltz,et al. Gene-edited CRISPR mushroom escapes US regulation , 2016, Nature.
[95] J. Batley,et al. Towards plant pangenomics. , 2016, Plant biotechnology journal.
[96] Ashutosh Kumar Singh,et al. Machine Learning for High-Throughput Stress Phenotyping in Plants. , 2016, Trends in plant science.
[97] Jiming Jiang,et al. Genome Reduction Uncovers a Large Dispensable Genome and Adaptive Role for Copy Number Variation in Asexually Propagated Solanum tuberosum[OPEN] , 2016, Plant Cell.
[98] A. Paterson,et al. Global agricultural intensification during climate change: a role for genomics , 2015, Plant biotechnology journal.
[99] N. Riaz,et al. Identification of stripe rust resistant genes in resistant synthetic hexaploid wheat accessions using linked markers , 2015, Plant Genetic Resources.
[100] N. Kandemir,et al. Apomixis: new horizons in plant breeding , 2015 .
[101] Rajeev K. Varshney,et al. Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects , 2015, Front. Plant Sci..
[102] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[103] Yong Liang,et al. Genome-wide analysis of the gene families of resistance gene analogues in cotton and their response to Verticillium wilt , 2015, BMC Plant Biology.
[104] William Stafford Noble,et al. Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.
[105] Peter J. Bradbury,et al. High-resolution genetic mapping of maize pan-genome sequence anchors , 2015, Nature Communications.
[106] U. D. Singh,et al. Development and evaluation of near-isogenic lines for major blast resistance gene(s) in Basmati rice , 2015, Theoretical and Applied Genetics.
[107] G. Moore. Strategic pre-breeding for wheat improvement , 2015, Nature Plants.
[108] Jing Zhao,et al. A maize wall-associated kinase confers quantitative resistance to head smut , 2014, Nature Genetics.
[109] Jan-Peter Nap,et al. Prioritization of candidate genes in QTL regions based on associations between traits and biological processes , 2014, BMC Plant Biology.
[110] Doreen Ware,et al. Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica , 2014, Genome Biology.
[111] Emily J. Warschefsky,et al. Back to the wilds: tapping evolutionary adaptations for resilient crops through systematic hybridization with crop wild relatives. , 2014, American journal of botany.
[112] Ruiqiang Li,et al. De novo assembly of soybean wild relatives for pan-genome analysis of diversity and agronomic traits , 2014, Nature Biotechnology.
[113] Corinne Da Silva,et al. Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome , 2014, Science.
[114] J. Batley,et al. A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome , 2014, Science.
[115] Y. Saranga,et al. Plant domestication versus crop evolution: a conceptual framework for cereals and grain legumes. , 2014, Trends in plant science.
[116] R. Terauchi,et al. Harvesting the Promising Fruits of Genomics: Applying Genome Sequencing Technologies to Crop Breeding , 2014, PLoS biology.
[117] R. Visser,et al. Beyond genomic variation - comparison and functional annotation of three Brassica rapa genomes: a turnip, a rapid cycling and a Chinese cabbage , 2014, BMC Genomics.
[118] Xiangfeng Wang,et al. Machine Learning–Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis[W] , 2014, Plant Cell.
[119] Jingyin Yu,et al. Genome-wide comparative analysis of NBS-encoding genes between Brassica species and Arabidopsis thaliana , 2014, BMC Genomics.
[120] M. A. Pedraza,et al. Insights into the Maize Pan-Genome and Pan-Transcriptome[W][OPEN] , 2014, Plant Cell.
[121] M. Muthamilarasan,et al. Development of 5123 Intron-Length Polymorphic Markers for Large-Scale Genotyping Applications in Foxtail Millet , 2013, DNA research : an international journal for rapid publication of reports on genes and genomes.
[122] K. Shinozaki,et al. Genome-Wide Analysis of ZmDREB Genes and Their Association with Natural Variation in Drought Tolerance at Seedling Stage of Zea mays L , 2013, PLoS genetics.
[123] J. Foley,et al. Yield Trends Are Insufficient to Double Global Crop Production by 2050 , 2013, PloS one.
[124] W. Cowling. Sustainable plant breeding , 2013 .
[125] Le Cong,et al. Multiplex Genome Engineering Using CRISPR/Cas Systems , 2013, Science.
[126] Mihaela M. Martis,et al. A physical, genetic and functional sequence assembly of the barley genome. , 2022 .
[127] P. Pesaresi,et al. The protein kinase Pstol1 from traditional rice confers tolerance of phosphorus deficiency , 2012, Nature.
[128] J. Doudna,et al. A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity , 2012, Science.
[129] B. Segerman. The genetic integrity of bacterial species: the core genome and the accessory genome, two different stories , 2012, Front. Cell. Inf. Microbio..
[130] E. T. Lammerts van Bueren,et al. The need to breed crop varieties suitable for organic farming, using wheat, tomato and broccoli as examples: A review , 2011 .
[131] David M. A. Martin,et al. Genome sequence and analysis of the tuber crop potato , 2011, Nature.
[132] A. Crabb. The Hybrid Corn Makers: Prophets Of Plenty , 2011 .
[133] P. Gupta,et al. Marker‐Assisted Selection as a Component of Conventional Plant Breeding , 2010 .
[134] Yunbi Xu,et al. Molecular Plant Breeding , 2010 .
[135] T. Sakurai,et al. Genome sequence of the palaeopolyploid soybean , 2010, Nature.
[136] Patrick S. Schnable,et al. Maize Inbreds Exhibit High Levels of Copy Number Variation (CNV) and Presence/Absence Variation (PAV) in Genome Content , 2009, PLoS genetics.
[137] Kenneth L. McNally,et al. Genomewide SNP variation reveals relationships among landraces and modern varieties of rice , 2009, Proceedings of the National Academy of Sciences.
[138] Dorian Q. Fuller,et al. The nature of selection during plant domestication , 2009, Nature.
[139] Mihaela M. Martis,et al. The Sorghum bicolor genome and the diversification of grasses , 2009, Nature.
[140] R. Twyman,et al. Precision breeding for novel starch variants in potato. , 2008, Plant biotechnology journal.
[141] M. R. Vishnupriya,et al. Marker assisted introgression of bacterial blight resistance in Samba Mahsuri, an elite indica rice variety , 2008, Euphytica.
[142] A. Fehér,et al. The effect of drought and heat stress on reproductive processes in cereals. , 2007, Plant, cell & environment.
[143] W. Pfeiffer,et al. Simulation Modeling in Plant Breeding: Principles and Applications , 2007 .
[144] Robbie Waugh,et al. Applying plant genomics to crop improvement , 2007, Genome Biology.
[145] D. Conrad,et al. Global variation in copy number in the human genome , 2006, Nature.
[146] R. Varshney,et al. Genomics-assisted breeding for crop improvement. , 2005, Trends in plant science.
[147] Jaideep P. Sundaram,et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome". , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[148] T. Mitchell-Olds,et al. A Multilocus Sequence Survey in Arabidopsis thaliana Reveals a Genome-Wide Departure From a Neutral Model of DNA Sequence Polymorphism , 2005, Genetics.
[149] Bingru Huang,et al. Physiological Recovery of Kentucky Bluegrass from Simultaneous Drought and Heat Stress , 2004 .
[150] W. G. Hill,et al. D. S. Falconer and Introduction to quantitative genetics. , 2004, Genetics.
[151] G. Hewitt,et al. Nuclear DNA analyses in genetic studies of populations: practice, problems and prospects , 2003, Molecular ecology.
[152] D. Zamir. Improving plant breeding with exotic genetic libraries , 2001, Nature Reviews Genetics.
[153] M. Wilkinson. Broadening the Genetic Base of Crop Production , 2001, Heredity.
[154] The Arabidopsis Genome Initiative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana , 2000, Nature.
[155] J. Ali,et al. Doubled Haploids in Rice Improvement: Approaches, Applications, and Future Prospects , 2021, Rice Improvement.
[156] Kareem A. Mosa,et al. Omics and System Biology Approaches in Plant Stress Research , 2017 .
[157] J. Batley,et al. Speed breeding: a powerful tool to accelerate crop research and breeding , 2017, bioRxiv.
[158] Hikmet Budak,et al. CRISPR/Cas9 genome editing in wheat , 2017, Functional & Integrative Genomics.
[159] Kareem A. Mosa,et al. Plant Stress Tolerance , 2017, SpringerBriefs in Systems Biology.
[160] José Crossa,et al. Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.). , 2016, Plant science : an international journal of experimental plant biology.
[161] C. K. Chan,et al. Identification and characterization of more than 4 million intervarietal SNPs across the group 7 chromosomes of bread wheat. , 2015, Plant biotechnology journal.
[162] Nigel Maxted,et al. Predictive characterization of crop wild relatives and landraces: technical guidelines version 1 , 2014 .
[163] J. Cairns,et al. Genomic Tools and Strategies for Breeding Climate Resilient Cereals , 2013 .
[164] W. Martin,et al. Genetic Diversity, Evolution and Domestication of Wheat and Barley in the Fertile Crescent , 2010 .
[165] B. Gill,et al. Development of a PCR Assay and Marker-Assisted Transfer of Leaf Rust and Stripe Rust Resistance Genes Lr57 and Yr40 into Hard Red Winter Wheats , 2009 .
[166] Anil Kumar Singh,et al. Adaptation and quality traits of a germplasm-derived commercial seed parent of pearl millet , 2008 .
[167] J. Bennetzen,et al. Transposable element contributions to plant gene and genome evolution , 2004, Plant Molecular Biology.
[168] D. Zilberman,et al. Is marker-assisted selection cost-effective compared with conventional plant breeding methods? The case of quality protein Maize. , 2002 .
[169] J. Welsh,et al. Fingerprinting genomes using PCR with arbitrary primers. , 1990, Nucleic acids research.
[170] L. Pauling,et al. Evolutionary Divergence and Convergence in Proteins , 1965 .
[171] B. Mcclintock,et al. Controlling elements and the gene. , 1956, Cold Spring Harbor symposia on quantitative biology.
[172] L. Stadler,et al. Genetic Effects of X-Rays in Maize. , 1928, Proceedings of the National Academy of Sciences of the United States of America.