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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Impact of pre-imputation SNP-filtering on genotype imputation results
|
|---|---|
| Published in |
BMC Genomic Data, August 2014
|
| DOI | 10.1186/s12863-014-0088-5 |
| Pubmed ID | |
| Authors |
Nab Raj Roshyara, Holger Kirsten, Katrin Horn, Peter Ahnert, Markus Scholz |
| Abstract |
Imputation of partially missing or unobserved genotypes is an indispensable tool for SNP data analyses. However, research and understanding of the impact of initial SNP-data quality control on imputation results is still limited. In this paper, we aim to evaluate the effect of different strategies of pre-imputation quality filtering on the performance of the widely used imputation algorithms MaCH and IMPUTE. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 1 | 25% |
| Germany | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 2% |
| Italy | 1 | <1% |
| Israel | 1 | <1% |
| Finland | 1 | <1% |
| Germany | 1 | <1% |
| Canada | 1 | <1% |
| United Kingdom | 1 | <1% |
| Denmark | 1 | <1% |
| United States | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 121 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 31 | 24% |
| Researcher | 30 | 23% |
| Student > Master | 22 | 17% |
| Other | 9 | 7% |
| Student > Doctoral Student | 8 | 6% |
| Other | 14 | 11% |
| Unknown | 17 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 56 | 43% |
| Biochemistry, Genetics and Molecular Biology | 20 | 15% |
| Medicine and Dentistry | 8 | 6% |
| Computer Science | 5 | 4% |
| Psychology | 5 | 4% |
| Other | 15 | 11% |
| Unknown | 22 | 17% |
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 20 October 2014.
All research outputs
#14,278,325
of 25,374,917 outputs
Outputs from BMC Genomic Data
#413
of 1,204 outputs
Outputs of similar age
#112,594
of 243,237 outputs
Outputs of similar age from BMC Genomic Data
#5
of 17 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 65% 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 243,237 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 53% of its contemporaries.
We're also able to compare this research output to 17 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 70% of its contemporaries.