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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genome-wide prediction of splice-modifying SNPs in human genes using a new analysis pipeline called AASsites
|
|---|---|
| Published in |
BMC Bioinformatics, July 2011
|
| DOI | 10.1186/1471-2105-12-s4-s2 |
| Pubmed ID | |
| Authors |
Kirsten Faber, Karl-Heinz Glatting, Phillip J Mueller, Angela Risch, Agnes Hotz-Wagenblatt |
| Abstract |
Some single nucleotide polymorphisms (SNPs) are known to modify the risk of developing certain diseases or the reaction to drugs. Due to next generation sequencing methods the number of known human SNPs has grown. Not all SNPs lead to a modified protein, which may be the origin of a disease. Therefore, the recognition of functional SNPs is needed. Because most SNP annotation tools look for SNPs which lead to an amino acid exchange or a premature stop, we designed a new tool called AASsites which searches for SNPs which modify splicing. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Spain | 1 | 2% |
| India | 1 | 2% |
| Unknown | 57 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 25% |
| Researcher | 14 | 23% |
| Student > Master | 7 | 11% |
| Student > Bachelor | 5 | 8% |
| Professor | 5 | 8% |
| Other | 6 | 10% |
| Unknown | 9 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 28 | 46% |
| Biochemistry, Genetics and Molecular Biology | 11 | 18% |
| Medicine and Dentistry | 4 | 7% |
| Computer Science | 2 | 3% |
| Physics and Astronomy | 1 | 2% |
| Other | 3 | 5% |
| Unknown | 12 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 19 July 2016.
All research outputs
#6,410,304
of 23,985,711 outputs
Outputs from BMC Bioinformatics
#2,302
of 7,491 outputs
Outputs of similar age
#34,984
of 118,762 outputs
Outputs of similar age from BMC Bioinformatics
#33
of 98 outputs
Altmetric has tracked 23,985,711 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,491 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 68% 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 118,762 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 98 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.