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Attention Score in Context
| Title |
Genome-wide analysis of porcine backfat and intramuscular fat fatty acid composition using high-density genotyping and expression data
|
|---|---|
| Published in |
BMC Genomics, December 2013
|
| DOI | 10.1186/1471-2164-14-845 |
| Pubmed ID | |
| Authors |
María Muñoz, M Carmen Rodríguez, Estefânia Alves, Josep María Folch, Noelia Ibañez-Escriche, Luis Silió, Ana Isabel Fernández |
| Abstract |
Porcine fatty acid composition is a key factor for quality and nutritive value of pork. Several QTLs for fatty acid composition have been reported in diverse fat tissues. The results obtained so far seem to point out different genetic control of fatty acid composition conditional on the fat deposits. Those studies have been conducted using simple approaches and most of them focused on one single tissue. The first objective of the present study was to identify tissue-specific and tissue-consistent QTLs for fatty acid composition in backfat and intramuscular fat, combining linkage mapping and GWAS approaches and conducted under single and multitrait models. A second aim was to identify powerful candidate genes for these tissue-consistent QTLs, using microarray gene expression data and following a targeted genetical genomics approach. |
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 % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 87 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 35 | 39% |
| Student > Ph. D. Student | 17 | 19% |
| Researcher | 8 | 9% |
| Student > Bachelor | 7 | 8% |
| Student > Doctoral Student | 2 | 2% |
| Other | 8 | 9% |
| Unknown | 12 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 41 | 46% |
| Environmental Science | 18 | 20% |
| Veterinary Science and Veterinary Medicine | 5 | 6% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Engineering | 3 | 3% |
| Other | 7 | 8% |
| Unknown | 12 | 13% |
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 02 December 2013.
All research outputs
#20,656,820
of 25,374,647 outputs
Outputs from BMC Genomics
#8,709
of 11,244 outputs
Outputs of similar age
#245,748
of 320,941 outputs
Outputs of similar age from BMC Genomics
#137
of 194 outputs
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So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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