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Mendeley readers
Attention Score in Context
| Title |
Construction of a high-density genetic map based on large-scale markers developed by specific length amplified fragment sequencing (SLAF-seq) and its application to QTL analysis for isoflavone content in Glycine max
|
|---|---|
| Published in |
BMC Genomics, December 2014
|
| DOI | 10.1186/1471-2164-15-1086 |
| Pubmed ID | |
| Authors |
Bin Li, Ling Tian, Jingying Zhang, Long Huang, Fenxia Han, Shurong Yan, Lianzheng Wang, Hongkun Zheng, Junming Sun |
| Abstract |
Quantitative trait locus (QTL) mapping is an efficient approach to discover the genetic architecture underlying complex quantitative traits. However, the low density of molecular markers in genetic maps has limited the efficiency and accuracy of QTL mapping. In this study, specific length amplified fragment sequencing (SLAF-seq), a new high-throughput strategy for large-scale SNP discovery and genotyping based on next generation sequencing (NGS), was employed to construct a high-density soybean genetic map using recombinant inbred lines (RILs, Luheidou2 × Nanhuizao, F5:8). With this map, the consistent QTLs for isoflavone content across various environments were identified. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 2% |
| Unknown | 48 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 22% |
| Researcher | 9 | 18% |
| Student > Master | 4 | 8% |
| Lecturer | 3 | 6% |
| Professor | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 13 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 55% |
| Biochemistry, Genetics and Molecular Biology | 5 | 10% |
| Business, Management and Accounting | 1 | 2% |
| Medicine and Dentistry | 1 | 2% |
| Engineering | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 14 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 30 October 2019.
All research outputs
#7,205,293
of 22,774,233 outputs
Outputs from BMC Genomics
#3,420
of 10,642 outputs
Outputs of similar age
#102,591
of 361,216 outputs
Outputs of similar age from BMC Genomics
#79
of 242 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 10,642 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 361,216 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 242 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.