Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline
With the global human population growing rapidly, agricultural production must increase to meet crop demand. Improving crops through breeding is a sustainable approach to increase yield and yield stability without intensifying the use of fertilisers and pesticides. Current advances in genomics and b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Agriculture |
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| Online Access: | http://www.mdpi.com/2077-0472/8/6/75 |
| _version_ | 1819067372819972096 |
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| author | Haifei Hu Armin Scheben David Edwards |
| author_facet | Haifei Hu Armin Scheben David Edwards |
| author_sort | Haifei Hu |
| collection | DOAJ |
| description | With the global human population growing rapidly, agricultural production must increase to meet crop demand. Improving crops through breeding is a sustainable approach to increase yield and yield stability without intensifying the use of fertilisers and pesticides. Current advances in genomics and bioinformatics provide opportunities for accelerating crop improvement. The rise of third generation sequencing technologies is helping overcome challenges in plant genome assembly caused by polyploidy and frequent repetitive elements. As a result, high-quality crop reference genomes are increasingly available, benefitting downstream analyses such as variant calling and association mapping that identify breeding targets in the genome. Machine learning also helps identify genomic regions of agronomic value by facilitating functional annotation of genomes and enabling real-time high-throughput phenotyping of agronomic traits in the glasshouse and in the field. Furthermore, crop databases that integrate the growing volume of genotype and phenotype data provide a valuable resource for breeders and an opportunity for data mining approaches to uncover novel trait-associated candidate genes. As knowledge of crop genetics expands, genomic selection and genome editing hold promise for breeding diseases-resistant and stress-tolerant crops with high yields. |
| first_indexed | 2024-12-21T16:17:13Z |
| format | Article |
| id | doaj.art-ee892cc3971d483d83d194ee6155d71e |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-12-21T16:17:13Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-ee892cc3971d483d83d194ee6155d71e2022-12-21T18:57:39ZengMDPI AGAgriculture2077-04722018-05-01867510.3390/agriculture8060075agriculture8060075Advances in Integrating Genomics and Bioinformatics in the Plant Breeding PipelineHaifei Hu0Armin Scheben1David Edwards2School of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth 6009, AustraliaSchool of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth 6009, AustraliaSchool of Biological Sciences and Institute of Agriculture, The University of Western Australia, Perth 6009, AustraliaWith the global human population growing rapidly, agricultural production must increase to meet crop demand. Improving crops through breeding is a sustainable approach to increase yield and yield stability without intensifying the use of fertilisers and pesticides. Current advances in genomics and bioinformatics provide opportunities for accelerating crop improvement. The rise of third generation sequencing technologies is helping overcome challenges in plant genome assembly caused by polyploidy and frequent repetitive elements. As a result, high-quality crop reference genomes are increasingly available, benefitting downstream analyses such as variant calling and association mapping that identify breeding targets in the genome. Machine learning also helps identify genomic regions of agronomic value by facilitating functional annotation of genomes and enabling real-time high-throughput phenotyping of agronomic traits in the glasshouse and in the field. Furthermore, crop databases that integrate the growing volume of genotype and phenotype data provide a valuable resource for breeders and an opportunity for data mining approaches to uncover novel trait-associated candidate genes. As knowledge of crop genetics expands, genomic selection and genome editing hold promise for breeding diseases-resistant and stress-tolerant crops with high yields.http://www.mdpi.com/2077-0472/8/6/75breedingcropsgenomicsthird generation sequencing |
| spellingShingle | Haifei Hu Armin Scheben David Edwards Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline Agriculture breeding crops genomics third generation sequencing |
| title | Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline |
| title_full | Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline |
| title_fullStr | Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline |
| title_full_unstemmed | Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline |
| title_short | Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline |
| title_sort | advances in integrating genomics and bioinformatics in the plant breeding pipeline |
| topic | breeding crops genomics third generation sequencing |
| url | http://www.mdpi.com/2077-0472/8/6/75 |
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