Revealing sequence variation patterns in rice with machine learning methods
The major breakthrough at the turn of the millennium was the completion of genome sequences for individuals from many species, including human, worm and rice. More recently, it has also been important to describe sequence variation within one species, providing the first step towards the linkage of...
| Main Authors: | , , , , , , , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Springer
2008
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/166273 |
| _version_ | 1855516062634737664 |
|---|---|
| author | Bohnert, Regina Zeller, Georg Clark, Richard M. Childs, Kevin L. Ulat, Victor Stokowski, Renee Ballinger, Dennis Frazer, Kelly Cox, David Bruskiewich, Richard Buell, C. Robin Leach, Jan Leung, Hei McNally, Kenneth L. Weigel, Detlef Rätsch, Gunnar |
| author_browse | Ballinger, Dennis Bohnert, Regina Bruskiewich, Richard Buell, C. Robin Childs, Kevin L. Clark, Richard M. Cox, David Frazer, Kelly Leach, Jan Leung, Hei McNally, Kenneth L. Rätsch, Gunnar Stokowski, Renee Ulat, Victor Weigel, Detlef Zeller, Georg |
| author_facet | Bohnert, Regina Zeller, Georg Clark, Richard M. Childs, Kevin L. Ulat, Victor Stokowski, Renee Ballinger, Dennis Frazer, Kelly Cox, David Bruskiewich, Richard Buell, C. Robin Leach, Jan Leung, Hei McNally, Kenneth L. Weigel, Detlef Rätsch, Gunnar |
| author_sort | Bohnert, Regina |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The major breakthrough at the turn of the millennium was the completion of genome sequences for individuals from many species, including human, worm and rice. More recently, it has also been important to describe sequence variation within one species, providing the first step towards the linkage of genetic variation to traits. Today, rice is the most important source for human caloric intake, making up 20% of the calorie supply and feeding millions of people daily. The more detailed understanding and findings on the molecular assembly of phenotypic rice varieties will therefore be essential for future improvement in rice cultivation and breeding. In order to reveal patterns of sequence variation in Oryza sativa (rice), the non-repetitive portion of the genomes of 20 diverse rice cultivars was resequenced, in collaboration with Perlegen Sciences, Inc., using a high-density oligonucleotide microarray technology. |
| format | Journal Article |
| id | CGSpace166273 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2008 |
| publishDateRange | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1662732025-05-14T10:23:52Z Revealing sequence variation patterns in rice with machine learning methods Bohnert, Regina Zeller, Georg Clark, Richard M. Childs, Kevin L. Ulat, Victor Stokowski, Renee Ballinger, Dennis Frazer, Kelly Cox, David Bruskiewich, Richard Buell, C. Robin Leach, Jan Leung, Hei McNally, Kenneth L. Weigel, Detlef Rätsch, Gunnar methodology simple nucleotide polymorphism genotypes genomes genetic variation The major breakthrough at the turn of the millennium was the completion of genome sequences for individuals from many species, including human, worm and rice. More recently, it has also been important to describe sequence variation within one species, providing the first step towards the linkage of genetic variation to traits. Today, rice is the most important source for human caloric intake, making up 20% of the calorie supply and feeding millions of people daily. The more detailed understanding and findings on the molecular assembly of phenotypic rice varieties will therefore be essential for future improvement in rice cultivation and breeding. In order to reveal patterns of sequence variation in Oryza sativa (rice), the non-repetitive portion of the genomes of 20 diverse rice cultivars was resequenced, in collaboration with Perlegen Sciences, Inc., using a high-density oligonucleotide microarray technology. 2008-10 2024-12-19T12:56:04Z 2024-12-19T12:56:04Z Journal Article https://hdl.handle.net/10568/166273 en Open Access Springer Bohnert, Regina; Zeller, Georg; Clark, Richard M; Childs, Kevin L; Ulat, Victor; Stokowski, Renee; Ballinger, Dennis; Frazer, Kelly; Cox, David; Bruskiewich, Richard; Buell, C Robin; Leach, Jan; Leung, Hei; McNally, Kenneth L; Weigel, Detlef and Rätsch, Gunnar. 2008. Revealing sequence variation patterns in rice with machine learning methods. BMC Bioinformatics, Volume 9, no. S10 |
| spellingShingle | methodology simple nucleotide polymorphism genotypes genomes genetic variation Bohnert, Regina Zeller, Georg Clark, Richard M. Childs, Kevin L. Ulat, Victor Stokowski, Renee Ballinger, Dennis Frazer, Kelly Cox, David Bruskiewich, Richard Buell, C. Robin Leach, Jan Leung, Hei McNally, Kenneth L. Weigel, Detlef Rätsch, Gunnar Revealing sequence variation patterns in rice with machine learning methods |
| title | Revealing sequence variation patterns in rice with machine learning methods |
| title_full | Revealing sequence variation patterns in rice with machine learning methods |
| title_fullStr | Revealing sequence variation patterns in rice with machine learning methods |
| title_full_unstemmed | Revealing sequence variation patterns in rice with machine learning methods |
| title_short | Revealing sequence variation patterns in rice with machine learning methods |
| title_sort | revealing sequence variation patterns in rice with machine learning methods |
| topic | methodology simple nucleotide polymorphism genotypes genomes genetic variation |
| url | https://hdl.handle.net/10568/166273 |
| work_keys_str_mv | AT bohnertregina revealingsequencevariationpatternsinricewithmachinelearningmethods AT zellergeorg revealingsequencevariationpatternsinricewithmachinelearningmethods AT clarkrichardm revealingsequencevariationpatternsinricewithmachinelearningmethods AT childskevinl revealingsequencevariationpatternsinricewithmachinelearningmethods AT ulatvictor revealingsequencevariationpatternsinricewithmachinelearningmethods AT stokowskirenee revealingsequencevariationpatternsinricewithmachinelearningmethods AT ballingerdennis revealingsequencevariationpatternsinricewithmachinelearningmethods AT frazerkelly revealingsequencevariationpatternsinricewithmachinelearningmethods AT coxdavid revealingsequencevariationpatternsinricewithmachinelearningmethods AT bruskiewichrichard revealingsequencevariationpatternsinricewithmachinelearningmethods AT buellcrobin revealingsequencevariationpatternsinricewithmachinelearningmethods AT leachjan revealingsequencevariationpatternsinricewithmachinelearningmethods AT leunghei revealingsequencevariationpatternsinricewithmachinelearningmethods AT mcnallykennethl revealingsequencevariationpatternsinricewithmachinelearningmethods AT weigeldetlef revealingsequencevariationpatternsinricewithmachinelearningmethods AT ratschgunnar revealingsequencevariationpatternsinricewithmachinelearningmethods |