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Mendeley readers
Attention Score in Context
| Title |
Designing deep sequencing experiments: detecting structural variation and estimating transcript abundance
|
|---|---|
| Published in |
BMC Genomics, June 2010
|
| DOI | 10.1186/1471-2164-11-385 |
| Pubmed ID | |
| Authors |
Ali Bashir, Vikas Bansal, Vineet Bafna |
| Abstract |
Massively parallel DNA sequencing technologies have enabled the sequencing of several individual human genomes. These technologies are also being used in novel ways for mRNA expression profiling, genome-wide discovery of transcription-factor binding sites, small RNA discovery, etc. The multitude of sequencing platforms, each with their unique characteristics, pose a number of design challenges, regarding the technology to be used and the depth of sequencing required for a particular sequencing application. Here we describe a number of analytical and empirical results to address design questions for two applications: detection of structural variations from paired-end sequencing and estimating mRNA transcript abundance. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 197 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 3% |
| United Kingdom | 4 | 2% |
| Belgium | 3 | 2% |
| Brazil | 2 | 1% |
| Malaysia | 1 | <1% |
| Italy | 1 | <1% |
| Hong Kong | 1 | <1% |
| Australia | 1 | <1% |
| Netherlands | 1 | <1% |
| Other | 4 | 2% |
| Unknown | 174 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 73 | 37% |
| Student > Ph. D. Student | 43 | 22% |
| Professor > Associate Professor | 21 | 11% |
| Student > Master | 13 | 7% |
| Other | 11 | 6% |
| Other | 24 | 12% |
| Unknown | 12 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 136 | 69% |
| Biochemistry, Genetics and Molecular Biology | 13 | 7% |
| Medicine and Dentistry | 11 | 6% |
| Computer Science | 4 | 2% |
| Mathematics | 4 | 2% |
| Other | 14 | 7% |
| Unknown | 15 | 8% |
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 18 February 2021.
All research outputs
#4,511,550
of 25,371,288 outputs
Outputs from BMC Genomics
#1,678
of 11,244 outputs
Outputs of similar age
#18,811
of 103,982 outputs
Outputs of similar age from BMC Genomics
#11
of 56 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 85% 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 103,982 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 81% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.