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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Analysing 454 amplicon resequencing experiments using the modular and database oriented Variant Identification Pipeline
|
|---|---|
| Published in |
BMC Bioinformatics, May 2010
|
| DOI | 10.1186/1471-2105-11-269 |
| Pubmed ID | |
| Authors |
Joachim M De Schrijver, Kim De Leeneer, Steve Lefever, Nick Sabbe, Filip Pattyn, Filip Van Nieuwerburgh, Paul Coucke, Dieter Deforce, Jo Vandesompele, Sofie Bekaert, Jan Hellemans, Wim Van Criekinge |
| Abstract |
Next-generation amplicon sequencing enables high-throughput genetic diagnostics, sequencing multiple genes in several patients together in one sequencing run. Currently, no open-source out-of-the-box software solution exists that reliably reports detected genetic variations and that can be used to improve future sequencing effectiveness by analyzing the PCR reactions. |
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 % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 6 | 6% |
| Spain | 4 | 4% |
| United States | 3 | 3% |
| United Kingdom | 3 | 3% |
| Australia | 2 | 2% |
| Netherlands | 1 | <1% |
| Chile | 1 | <1% |
| Sweden | 1 | <1% |
| Switzerland | 1 | <1% |
| Other | 0 | 0% |
| Unknown | 82 | 79% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 36 | 35% |
| Student > Ph. D. Student | 25 | 24% |
| Professor > Associate Professor | 9 | 9% |
| Professor | 8 | 8% |
| Other | 8 | 8% |
| Other | 13 | 13% |
| Unknown | 5 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 71 | 68% |
| Biochemistry, Genetics and Molecular Biology | 9 | 9% |
| Medicine and Dentistry | 8 | 8% |
| Computer Science | 3 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | <1% |
| Other | 5 | 5% |
| Unknown | 7 | 7% |
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 26 November 2012.
All research outputs
#7,407,006
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,234 outputs
Outputs of similar age
#33,438
of 94,174 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 74 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,234 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 94,174 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.