Literature DB >> 17701278

A rice phenomics study--phenotype scoring and seed propagation of a T-DNA insertion-induced rice mutant population.

Chyr-Guan Chern1, Ming-Jen Fan, Su-May Yu, Ai-Ling Hour, Po-Chang Lu, Yao-Cheng Lin, Fu-Jin Wei, Sheng-Chung Huang, Shu Chen, Ming-Hsing Lai, Ching-Shan Tseng, Hsing-Mu Yen, Woei-Shyuan Jwo, Chen-Chia Wu, Tung-Lung Yang, Lung-Sheng Li, Yih-Cheng Kuo, Su-Mien Li, Charng-Pei Li, Chiu-Kai Wey, Arunee Trisiriroj, Hsing-Fang Lee, Yue-Ie C Hsing.   

Abstract

With the completion of the rice genome sequencing project, the next major challenge is the large-scale determination of gene function. As an important crop and a model organism, rice provides major insights into gene functions important for crop growth or production. Phenomics with detailed information about tagged populations provides a good tool for functional genomics analysis. By a T-DNA insertional mutagenesis approach, we have generated a rice mutant population containing 55,000 promoter trap and gene activation or knockout lines. Approximately 20,000 of these lines have known integration sites. The T0 and T1 plants were grown in net "houses" for two cropping seasons each year since 2003, with the mutant phenotypes recorded. Detailed data describing growth and development of these plants, in 11 categories and 65 subcategories, over the entire four-month growing season are available in a searchable database, along with the genetic segregation information and flanking sequence data. With the detailed data from more than 20,000 T1 lines and 12 plants per line, we estimated the mutation rates of the T1 population, as well the frequency of the dominant T0 mutants. The correlations among different mutation phenotypes are also calculated. Together, the information about mutant lines, their integration sites, and the phenotypes make this collection, the Taiwan Rice Insertion Mutants (TRIM), a good resource for rice phenomics study. Ten T2 seeds per line can be distributed to researchers upon request.

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Year:  2007        PMID: 17701278     DOI: 10.1007/s11103-007-9218-z

Source DB:  PubMed          Journal:  Plant Mol Biol        ISSN: 0167-4412            Impact factor:   4.076


  30 in total

1.  Development of enhancer trap lines for functional analysis of the rice genome.

Authors:  Changyin Wu; Xiangjun Li; Wenya Yuan; Guoxing Chen; Andrzej Kilian; Juan Li; Caiguo Xu; Xianghua Li; Dao-Xiu Zhou; Shiping Wang; Qifa Zhang
Journal:  Plant J       Date:  2003-08       Impact factor: 6.417

2.  High throughput T-DNA insertion mutagenesis in rice: a first step towards in silico reverse genetics.

Authors:  Christophe Sallaud; Céline Gay; Pierre Larmande; Martine Bès; Pietro Piffanelli; Benoit Piégu; Gaétan Droc; Farid Regad; Emmanuelle Bourgeois; Donaldo Meynard; Christophe Périn; Xavier Sabau; Alain Ghesquière; Jean Christophe Glaszmann; Michel Delseny; Emmanuel Guiderdoni
Journal:  Plant J       Date:  2004-08       Impact factor: 6.417

3.  Chemical- and irradiation-induced mutants of indica rice IR64 for forward and reverse genetics.

Authors:  Jian-Li Wu; Chanjian Wu; Cailin Lei; Marietta Baraoidan; Alicia Bordeos; Ma Reina Suzette Madamba; Marilou Ramos-Pamplona; Ramil Mauleon; Arlett Portugal; Victor Jun Ulat; Richard Bruskiewich; Guoliang Wang; Jan Leach; Gurdev Khush; Hei Leung
Journal:  Plant Mol Biol       Date:  2005-09       Impact factor: 4.076

4.  Generation of a flanking sequence-tag database for activation-tagging lines in japonica rice.

Authors:  Dong-Hoon Jeong; Suyoung An; Sunhee Park; Hong-Gyu Kang; Gi-Gyeong Park; Sung-Ryul Kim; Jayeon Sim; Young-Ock Kim; Min-Kyung Kim; Seong-Ryong Kim; Joowon Kim; Moonsoo Shin; Mooyoung Jung; Gynheung An
Journal:  Plant J       Date:  2006-01       Impact factor: 6.417

5.  Plant comparative genetics after 10 years.

Authors:  M D Gale; K M Devos
Journal:  Science       Date:  1998-10-23       Impact factor: 47.728

6.  Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana.

Authors:  Takeshi Itoh; Tsuyoshi Tanaka; Roberto A Barrero; Chisato Yamasaki; Yasuyuki Fujii; Phillip B Hilton; Baltazar A Antonio; Hideo Aono; Rolf Apweiler; Richard Bruskiewich; Thomas Bureau; Frances Burr; Antonio Costa de Oliveira; Galina Fuks; Takuya Habara; Georg Haberer; Bin Han; Erimi Harada; Aiko T Hiraki; Hirohiko Hirochika; Douglas Hoen; Hiroki Hokari; Satomi Hosokawa; Yue-ie Hsing; Hiroshi Ikawa; Kazuho Ikeo; Tadashi Imanishi; Yukiyo Ito; Pankaj Jaiswal; Masako Kanno; Yoshihiro Kawahara; Toshiyuki Kawamura; Hiroaki Kawashima; Jitendra P Khurana; Shoshi Kikuchi; Setsuko Komatsu; Kanako O Koyanagi; Hiromi Kubooka; Damien Lieberherr; Yao-Cheng Lin; David Lonsdale; Takashi Matsumoto; Akihiro Matsuya; W Richard McCombie; Joachim Messing; Akio Miyao; Nicola Mulder; Yoshiaki Nagamura; Jongmin Nam; Nobukazu Namiki; Hisataka Numa; Shin Nurimoto; Claire O'Donovan; Hajime Ohyanagi; Toshihisa Okido; Satoshi Oota; Naoki Osato; Lance E Palmer; Francis Quetier; Saurabh Raghuvanshi; Naomi Saichi; Hiroaki Sakai; Yasumichi Sakai; Katsumi Sakata; Tetsuya Sakurai; Fumihiko Sato; Yoshiharu Sato; Heiko Schoof; Motoaki Seki; Michie Shibata; Yuji Shimizu; Kazuo Shinozaki; Yuji Shinso; Nagendra K Singh; Brian Smith-White; Jun-ichi Takeda; Motohiko Tanino; Tatiana Tatusova; Supat Thongjuea; Fusano Todokoro; Mika Tsugane; Akhilesh K Tyagi; Apichart Vanavichit; Aihui Wang; Rod A Wing; Kaori Yamaguchi; Mayu Yamamoto; Naoyuki Yamamoto; Yeisoo Yu; Hao Zhang; Qiang Zhao; Kenichi Higo; Benjamin Burr; Takashi Gojobori; Takuji Sasaki
Journal:  Genome Res       Date:  2007-01-08       Impact factor: 9.043

7.  Activation of the cytochrome P450 gene, CYP72C1, reduces the levels of active brassinosteroids in vivo.

Authors:  Masanobu Nakamura; Tatsuro Satoh; Shin-Ichiro Tanaka; Nobuyoshi Mochizuki; Takao Yokota; Akira Nagatani
Journal:  J Exp Bot       Date:  2005-02-02       Impact factor: 6.992

8.  Rice mutant resources for gene discovery.

Authors:  Hirohiko Hirochika; Emmanuel Guiderdoni; Gynheung An; Yue-Ie Hsing; Moo Young Eun; Chang-Deok Han; Narayana Upadhyaya; Srinivasan Ramachandran; Qifa Zhang; Andy Pereira; Venkatesan Sundaresan; Hei Leung
Journal:  Plant Mol Biol       Date:  2004-02       Impact factor: 4.076

9.  Establishing an efficient Ac/Ds tagging system in rice: large-scale analysis of Ds flanking sequences.

Authors:  Tatiana Kolesnik; Ildiko Szeverenyi; Doris Bachmann; Chellian Santhosh Kumar; Shuye Jiang; Rengasamy Ramamoorthy; Minnie Cai; Zhi Gang Ma; Venkatesan Sundaresan; Srinivasan Ramachandran
Journal:  Plant J       Date:  2004-01       Impact factor: 6.417

10.  Plant MPSS databases: signature-based transcriptional resources for analyses of mRNA and small RNA.

Authors:  Mayumi Nakano; Kan Nobuta; Kalyan Vemaraju; Shivakundan Singh Tej; Jeremy W Skogen; Blake C Meyers
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

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  18 in total

1.  Mutant resources in rice for functional genomics of the grasses.

Authors:  Arjun Krishnan; Emmanuel Guiderdoni; Gynheung An; Yue-ie C Hsing; Chang-deok Han; Myung Chul Lee; Su-May Yu; Narayana Upadhyaya; Srinivasan Ramachandran; Qifa Zhang; Venkatesan Sundaresan; Hirohiko Hirochika; Hei Leung; Andy Pereira
Journal:  Plant Physiol       Date:  2009-01       Impact factor: 8.340

2.  Activation tagging, an efficient tool for functional analysis of the rice genome.

Authors:  Shuyan Wan; Jinxia Wu; Zhiguo Zhang; Xuehui Sun; Yaci Lv; Ci Gao; Yingda Ning; Jun Ma; Yupeng Guo; Qian Zhang; Xia Zheng; Caiying Zhang; Zhiying Ma; Tiegang Lu
Journal:  Plant Mol Biol       Date:  2008-10-02       Impact factor: 4.076

3.  A rice DEAD-box protein, OsRH36, can complement an Arabidopsis atrh36 mutant, but cannot functionally replace its yeast homolog Dbp8p.

Authors:  Chun-Kai Huang; Su-May Yu; Chung-An Lu
Journal:  Plant Mol Biol       Date:  2010-07-06       Impact factor: 4.076

Review 4.  Natural and artificial mutants as valuable resources for functional genomics and molecular breeding.

Authors:  Shu-Ye Jiang; Srinivasan Ramachandran
Journal:  Int J Biol Sci       Date:  2010-04-28       Impact factor: 6.580

Review 5.  Phenome analysis in plant species using loss-of-function and gain-of-function mutants.

Authors:  Takashi Kuromori; Shinya Takahashi; Youichi Kondou; Kazuo Shinozaki; Minami Matsui
Journal:  Plant Cell Physiol       Date:  2009-06-05       Impact factor: 4.927

6.  Growth platform-dependent and -independent phenotypic and metabolic responses of Arabidopsis and its halophytic relative, Eutrema salsugineum, to salt stress.

Authors:  Yana Kazachkova; Albert Batushansky; Aroldo Cisneros; Noemi Tel-Zur; Aaron Fait; Simon Barak
Journal:  Plant Physiol       Date:  2013-06-04       Impact factor: 8.340

7.  A software tool for the input and management of phenotypic data using personal digital assistants and other mobile devices.

Authors:  Karin Köhl; Jürgen Gremmels
Journal:  Plant Methods       Date:  2015-04-07       Impact factor: 4.993

8.  Increasing leaf vein density by mutagenesis: laying the foundations for C4 rice.

Authors:  Aryo B Feldman; Erik H Murchie; Hei Leung; Marietta Baraoidan; Robert Coe; Su-May Yu; Shuen-Fang Lo; William P Quick
Journal:  PLoS One       Date:  2014-04-23       Impact factor: 3.240

9.  International Consortium of Rice Mutagenesis: resources and beyond.

Authors:  Fu-Jin Wei; Gaëtan Droc; Emmanuel Guiderdoni; Yue-Ie C Hsing
Journal:  Rice (N Y)       Date:  2013-12-17       Impact factor: 4.783

10.  Analysis of the early-flowering mechanisms and generation of T-DNA tagging lines in Kitaake, a model rice cultivar.

Authors:  Song Lim Kim; Minkyung Choi; Ki-Hong Jung; Gynheung An
Journal:  J Exp Bot       Date:  2013-08-21       Impact factor: 6.992

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