| Title |
Application of resequencing to rice genomics, functional genomics and evolutionary analysis
|
|---|---|
| Published in |
Rice, July 2014
|
| DOI | 10.1186/s12284-014-0004-7 |
| Pubmed ID | |
| Authors |
Longbiao Guo, Zhenyu Gao, Qian Qian |
| Abstract |
Rice is a model system used for crop genomics studies. The completion of the rice genome draft sequences in 2002 not only accelerated functional genome studies, but also initiated a new era of resequencing rice genomes. Based on the reference genome in rice, next-generation sequencing (NGS) using the high-throughput sequencing system can efficiently accomplish whole genome resequencing of various genetic populations and diverse germplasm resources. Resequencing technology has been effectively utilized in evolutionary analysis, rice genomics and functional genomics studies. This technique is beneficial for both bridging the knowledge gap between genotype and phenotype and facilitating molecular breeding via gene design in rice. Here, we also discuss the limitation, application and future prospects of rice resequencing. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | <1% |
| Benin | 1 | <1% |
| France | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 97 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 28% |
| Researcher | 24 | 24% |
| Student > Master | 11 | 11% |
| Student > Doctoral Student | 9 | 9% |
| Student > Bachelor | 5 | 5% |
| Other | 12 | 12% |
| Unknown | 12 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 71 | 70% |
| Biochemistry, Genetics and Molecular Biology | 6 | 6% |
| Computer Science | 3 | 3% |
| Environmental Science | 2 | 2% |
| Immunology and Microbiology | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 17 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 July 2014.
All research outputs
#14,197,510
of 22,758,248 outputs
Outputs from Rice
#142
of 383 outputs
Outputs of similar age
#117,592
of 225,827 outputs
Outputs of similar age from Rice
#3
of 4 outputs
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So far Altmetric has tracked 383 research outputs from this source. They receive a mean Attention Score of 3.8. This one has gotten more attention than average, scoring higher than 60% of its peers.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.