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Mendeley readers
Attention Score in Context
Title |
Evaluating Characteristics of De Novo Assembly Software on 454 Transcriptome Data: A Simulation Approach
|
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Published in |
PLOS ONE, February 2012
|
DOI | 10.1371/journal.pone.0031410 |
Pubmed ID | |
Authors |
Marvin Mundry, Erich Bornberg-Bauer, Michael Sammeth, Philine G. D. Feulner |
Abstract |
The quantity of transcriptome data is rapidly increasing for non-model organisms. As sequencing technology advances, focus shifts towards solving bioinformatic challenges, of which sequence read assembly is the first task. Recent studies have compared the performance of different software to establish a best practice for transcriptome assembly. Here, we adapted a simulation approach to evaluate specific features of assembly programs on 454 data. The novelty of our study is that the simulation allows us to calculate a model assembly as reference point for comparison. |
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 % |
---|---|---|
Finland | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 5 | 3% |
United States | 4 | 2% |
Brazil | 3 | 2% |
Sweden | 2 | 1% |
France | 1 | <1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
Chile | 1 | <1% |
Israel | 1 | <1% |
Other | 10 | 5% |
Unknown | 167 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 55 | 28% |
Researcher | 49 | 25% |
Professor > Associate Professor | 17 | 9% |
Student > Master | 16 | 8% |
Student > Doctoral Student | 12 | 6% |
Other | 37 | 19% |
Unknown | 10 | 5% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 127 | 65% |
Biochemistry, Genetics and Molecular Biology | 27 | 14% |
Computer Science | 9 | 5% |
Medicine and Dentistry | 4 | 2% |
Engineering | 3 | 2% |
Other | 8 | 4% |
Unknown | 18 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 04 February 2015.
All research outputs
#6,108,824
of 22,663,150 outputs
Outputs from PLOS ONE
#72,827
of 193,502 outputs
Outputs of similar age
#40,118
of 155,414 outputs
Outputs of similar age from PLOS ONE
#1,016
of 3,499 outputs
Altmetric has tracked 22,663,150 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 193,502 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one has gotten more attention than average, scoring higher than 61% 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 155,414 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 73% of its contemporaries.
We're also able to compare this research output to 3,499 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.