| Title |
Prioritizing single-nucleotide polymorphisms and variants associated with clinical mastitis
|
|---|---|
| Published in |
Advances and Applications in Bioinformatics and Chemistry : AABC, June 2017
|
| DOI | 10.2147/aabc.s123604 |
| Pubmed ID | |
| Authors |
Prashanth Suravajhala, Alfredo Benso |
| Abstract |
Next-generation sequencing technology has provided resources to easily explore and identify candidate single-nucleotide polymorphisms (SNPs) and variants. However, there remains a challenge in identifying and inferring the causal SNPs from sequence data. A problem with different methods that predict the effect of mutations is that they produce false positives. In this hypothesis, we provide an overview of methods known for identifying causal variants and discuss the challenges, fallacies, and prospects in discerning candidate SNPs. We then propose a three-point classification strategy, which could be an additional annotation method in identifying causalities. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 67% |
| Taiwan | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 29% |
| Student > Ph. D. Student | 4 | 19% |
| Student > Bachelor | 3 | 14% |
| Other | 1 | 5% |
| Student > Master | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 5 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 29% |
| Agricultural and Biological Sciences | 4 | 19% |
| Computer Science | 2 | 10% |
| Mathematics | 1 | 5% |
| Veterinary Science and Veterinary Medicine | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 5 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 15 July 2017.
All research outputs
#15,349,873
of 25,806,080 outputs
Outputs from Advances and Applications in Bioinformatics and Chemistry : AABC
#20
of 54 outputs
Outputs of similar age
#172,685
of 331,686 outputs
Outputs of similar age from Advances and Applications in Bioinformatics and Chemistry : AABC
#1
of 1 outputs
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So far Altmetric has tracked 54 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 62% of its peers.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them