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Mendeley readers
Attention Score in Context
| Title |
Development of a model webserver for breed identification using microsatellite DNA marker
|
|---|---|
| Published in |
BMC Genomic Data, December 2013
|
| DOI | 10.1186/1471-2156-14-118 |
| Pubmed ID | |
| Authors |
Mir Asif Iquebal, Sarika, Sandeep Kumar Dhanda, Vasu Arora, Sat Pal Dixit, Gajendra PS Raghava, Anil Rai, Dinesh Kumar |
| Abstract |
Identification of true to breed type animal for conservation purpose is imperative. Breed dilution is one of the major problems in sustainability except cases of commercial crossbreeding under controlled condition. Breed descriptor has been developed to identify breed but such descriptors cover only "pure breed" or true to the breed type animals excluding undefined or admixture population. Moreover, in case of semen, ova, embryo and breed product, the breed cannot be identified due to lack of visible phenotypic descriptors. Advent of molecular markers like microsatellite and SNP have revolutionized breed identification from even small biological tissue or germplasm. Microsatellite DNA marker based breed assignments has been reported in various domestic animals. Such methods have limitations viz. non availability of allele data in public domain, thus each time all reference breed has to be genotyped which is neither logical nor economical. Even if such data is available but computational methods needs expertise of data analysis and interpretation. |
Mendeley readers
The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 3% |
| Unknown | 32 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 45% |
| Student > Ph. D. Student | 5 | 15% |
| Other | 2 | 6% |
| Professor > Associate Professor | 2 | 6% |
| Lecturer | 1 | 3% |
| Other | 4 | 12% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 42% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Business, Management and Accounting | 1 | 3% |
| Veterinary Science and Veterinary Medicine | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 4 | 12% |
| Unknown | 9 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 01 December 2023.
All research outputs
#4,760,313
of 25,374,647 outputs
Outputs from BMC Genomic Data
#157
of 1,204 outputs
Outputs of similar age
#52,087
of 320,285 outputs
Outputs of similar age from BMC Genomic Data
#4
of 18 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 86% 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 320,285 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.