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X Demographics
Mendeley readers
Attention Score in Context
| Title |
GapFiller: a de novo assembly approach to fill the gap within paired reads
|
|---|---|
| Published in |
BMC Bioinformatics, September 2012
|
| DOI | 10.1186/1471-2105-13-s14-s8 |
| Pubmed ID | |
| Authors |
Francesca Nadalin, Francesco Vezzi, Alberto Policriti |
| Abstract |
Next Generation Sequencing technologies are able to provide high genome coverages at a relatively low cost. However, due to limited reads' length (from 30 bp up to 200 bp), specific bioinformatics problems have become even more difficult to solve. De novo assembly with short reads, for example, is more complicated at least for two reasons: first, the overall amount of "noisy" data to cope with increased and, second, as the reads' length decreases the number of unsolvable repeats grows. Our work's aim is to go at the root of the problem by providing a pre-processing tool capable to produce (in-silico) longer and highly accurate sequences from a collection of Next Generation Sequencing reads. |
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 % |
|---|---|---|
| Canada | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 252 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 3 | 1% |
| United States | 3 | 1% |
| Brazil | 3 | 1% |
| France | 2 | <1% |
| Netherlands | 2 | <1% |
| Norway | 1 | <1% |
| Switzerland | 1 | <1% |
| Australia | 1 | <1% |
| United Kingdom | 1 | <1% |
| Other | 5 | 2% |
| Unknown | 230 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 62 | 25% |
| Researcher | 51 | 20% |
| Student > Master | 47 | 19% |
| Student > Bachelor | 14 | 6% |
| Other | 11 | 4% |
| Other | 29 | 12% |
| Unknown | 38 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 105 | 42% |
| Biochemistry, Genetics and Molecular Biology | 54 | 21% |
| Computer Science | 14 | 6% |
| Immunology and Microbiology | 8 | 3% |
| Environmental Science | 3 | 1% |
| Other | 15 | 6% |
| Unknown | 53 | 21% |
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 20 July 2018.
All research outputs
#3,972,436
of 22,753,345 outputs
Outputs from BMC Bioinformatics
#1,530
of 7,269 outputs
Outputs of similar age
#28,585
of 169,080 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 96 outputs
Altmetric has tracked 22,753,345 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 7,269 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 done well, scoring higher than 78% 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 169,080 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 83% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.