The progression in developing genomic resources for crop improvement
Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the numbe...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/132197 |
| _version_ | 1855513638577635328 |
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| author | Ruperao, P. Rangan, P. Shah, T. Thakur, V. Kalia, S. Mayes, S. Rathore, A. |
| author_browse | Kalia, S. Mayes, S. Rangan, P. Rathore, A. Ruperao, P. Shah, T. Thakur, V. |
| author_facet | Ruperao, P. Rangan, P. Shah, T. Thakur, V. Kalia, S. Mayes, S. Rathore, A. |
| author_sort | Ruperao, P. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques. |
| format | Journal Article |
| id | CGSpace132197 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1321972025-12-08T10:29:22Z The progression in developing genomic resources for crop improvement Ruperao, P. Rangan, P. Shah, T. Thakur, V. Kalia, S. Mayes, S. Rathore, A. plant genomes bioinformatics databases big data artificial intelligence machine learning Sequencing technologies have rapidly evolved over the past two decades, and new technologies are being continually developed and commercialized. The emerging sequencing technologies target generating more data with fewer inputs and at lower costs. This has also translated to an increase in the number and type of corresponding applications in genomics besides enhanced computational capacities (both hardware and software). Alongside the evolving DNA sequencing landscape, bioinformatics research teams have also evolved to accommodate the increasingly demanding techniques used to combine and interpret data, leading to many researchers moving from the lab to the computer. The rich history of DNA sequencing has paved the way for new insights and the development of new analysis methods. Understanding and learning from past technologies can help with the progress of future applications. This review focuses on the evolution of sequencing technologies, their significant enabling role in generating plant genome assemblies and downstream applications, and the parallel development of bioinformatics tools and skills, filling the gap in data analysis techniques. 2023 2023-10-11T14:37:40Z 2023-10-11T14:37:40Z Journal Article https://hdl.handle.net/10568/132197 en Open Access application/pdf MDPI Ruperao, P., Rangan, P., Shah, T., Thakur, V., Kalia, S., Mayes, S. & Rathore, A. (2023). The progression in developing genomic resources for crop improvement. Life, 13(8): 1668, 1-28. |
| spellingShingle | plant genomes bioinformatics databases big data artificial intelligence machine learning Ruperao, P. Rangan, P. Shah, T. Thakur, V. Kalia, S. Mayes, S. Rathore, A. The progression in developing genomic resources for crop improvement |
| title | The progression in developing genomic resources for crop improvement |
| title_full | The progression in developing genomic resources for crop improvement |
| title_fullStr | The progression in developing genomic resources for crop improvement |
| title_full_unstemmed | The progression in developing genomic resources for crop improvement |
| title_short | The progression in developing genomic resources for crop improvement |
| title_sort | progression in developing genomic resources for crop improvement |
| topic | plant genomes bioinformatics databases big data artificial intelligence machine learning |
| url | https://hdl.handle.net/10568/132197 |
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