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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improved variant discovery through local re-alignment of short-read next-generation sequencing data using SRMA
|
|---|---|
| Published in |
Genome Biology, October 2010
|
| DOI | 10.1186/gb-2010-11-10-r99 |
| Pubmed ID | |
| Authors |
Nils Homer, Stanley F Nelson |
| Abstract |
A primary component of next-generation sequencing analysis is to align short reads to a reference genome, with each read aligned independently. However, reads that observe the same non-reference DNA sequence are highly correlated and can be used to better model the true variation in the target genome. A novel short-read micro realigner, SRMA, that leverages this correlation to better resolve a consensus of the underlying DNA sequence of the targeted genome is described here. |
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 % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 4% |
| France | 3 | 2% |
| United Kingdom | 3 | 2% |
| Norway | 2 | 1% |
| Spain | 2 | 1% |
| South Africa | 1 | <1% |
| Sweden | 1 | <1% |
| China | 1 | <1% |
| Germany | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 166 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 60 | 32% |
| Student > Ph. D. Student | 55 | 29% |
| Professor > Associate Professor | 14 | 7% |
| Student > Master | 14 | 7% |
| Other | 13 | 7% |
| Other | 22 | 12% |
| Unknown | 12 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 111 | 58% |
| Biochemistry, Genetics and Molecular Biology | 29 | 15% |
| Computer Science | 14 | 7% |
| Medicine and Dentistry | 7 | 4% |
| Mathematics | 4 | 2% |
| Other | 11 | 6% |
| Unknown | 14 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 07 November 2023.
All research outputs
#3,415,350
of 25,374,647 outputs
Outputs from Genome Biology
#2,428
of 4,467 outputs
Outputs of similar age
#13,507
of 108,199 outputs
Outputs of similar age from Genome Biology
#12
of 40 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 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 45th percentile – i.e., 45% of its peers scored the same or lower than it.
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 108,199 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 86% of its contemporaries.
We're also able to compare this research output to 40 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 67% of its contemporaries.