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Attention Score in Context
| Title |
Transcriptomic analysis revealed the mechanism of oil dynamic accumulation during developing Siberian apricot (Prunus sibirica L.) seed kernels for the development of woody biodiesel
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|---|---|
| Published in |
Biotechnology for Biofuels and Bioproducts, February 2015
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| DOI | 10.1186/s13068-015-0213-3 |
| Pubmed ID | |
| Authors |
Jun Niu, Jiyong An, Libing Wang, Chengliang Fang, Denglong Ha, Chengyu Fu, Lin Qiu, Haiyan Yu, Haiyan Zhao, Xinyu Hou, Zheng Xiang, Sufan Zhou, Zhixiang Zhang, Xinyi Feng, Shanzhi Lin |
| Abstract |
Siberian apricot (Prunus sibirica L.) has emerged as a novel potential source of biodiesel in China, but the molecular regulatory mechanism of oil accumulation in Siberian apricot seed kernels (SASK) is still unknown at present. To better develop SASK oil as woody biodiesel, it is essential to profile transcriptome and to identify the full repertoire of potential unigenes involved in the formation and accumulation of oil SASK during the different developing stages. We firstly detected the temporal patterns for oil content and fatty acid (FA) compositions of SASK in 7 different developing stages. The best time for obtaining the high quality and quantity of SASK oil was characterized at 60 days after flowering (DAF), and the representative periods (10, 30, 50, 60, and 70 DAF) were selected for transcriptomic analysis. By Illumina/Solexa sequencings, approximately 65 million short reads (average length = 96 bp) were obtained, and then assembled into 124,070 unigenes by Trinity strategy (mean size = 829.62 bp). A total of 3,000, 2,781, 2,620, and 2,675 differentially expressed unigenes were identified at 30, 50, 60, and 70 DAF (10 DAF as the control) by DESeq method, respectively. The relationship between the unigene transcriptional profiles and the oil dynamic patterns in developing SASK was comparatively analyzed, and the specific unigenes encoding some known enzymes and transcription factors involved in acetyl-coenzyme A (acetyl-CoA) formation and oil accumulation were determined. Additionally, 5 key metabolic genes implicated in SASK oil accumulation were experimentally validated by quantitative real-time PCR (qRT-PCR). Our findings could help to construction of oil accumulated pathway and to elucidate the molecular regulatory mechanism of increased oil production in developing SASK. This is the first study of oil temporal patterns, transcriptome sequencings, and differential profiles in developing SASK. All our results will serve as the important foundation to further deeply explore the regulatory mechanism of SASK high-quality oil accumulation, and may also provide some reference for researching the woody biodiesel plants. |
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 % |
|---|---|---|
| Unknown | 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | 3% |
| Unknown | 33 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 18% |
| Researcher | 5 | 15% |
| Student > Master | 4 | 12% |
| Student > Bachelor | 3 | 9% |
| Other | 3 | 9% |
| Other | 8 | 24% |
| Unknown | 5 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 41% |
| Biochemistry, Genetics and Molecular Biology | 5 | 15% |
| Engineering | 4 | 12% |
| Chemistry | 2 | 6% |
| Immunology and Microbiology | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 6 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 05 April 2015.
All research outputs
#17,285,036
of 25,371,288 outputs
Outputs from Biotechnology for Biofuels and Bioproducts
#997
of 1,578 outputs
Outputs of similar age
#162,924
of 269,623 outputs
Outputs of similar age from Biotechnology for Biofuels and Bioproducts
#28
of 41 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,578 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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