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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Annotating genomes with massive-scale RNA sequencing
|
|---|---|
| Published in |
Genome Biology, January 2008
|
| DOI | 10.1186/gb-2008-9-12-r175 |
| Pubmed ID | |
| Authors |
France Denoeud, Jean-Marc Aury, Corinne Da Silva, Benjamin Noel, Odile Rogier, Massimo Delledonne, Michele Morgante, Giorgio Valle, Patrick Wincker, Claude Scarpelli, Olivier Jaillon, François Artiguenave |
| Abstract |
Next generation technologies enable massive-scale cDNA sequencing (so-called RNA-Seq). Mainly because of the difficulty of aligning short reads on exon-exon junctions, no attempts have been made so far to use RNA-Seq for building gene models de novo, that is, in the absence of a set of known genes and/or splicing events. We present G-Mo.R-Se (Gene Modelling using RNA-Seq), an approach aimed at building gene models directly from RNA-Seq and demonstrate its utility on the grapevine genome. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 481 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 25 | 5% |
| Italy | 8 | 2% |
| United Kingdom | 6 | 1% |
| Canada | 4 | <1% |
| Netherlands | 3 | <1% |
| Japan | 3 | <1% |
| China | 3 | <1% |
| Spain | 2 | <1% |
| Chile | 2 | <1% |
| Other | 17 | 4% |
| Unknown | 408 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 141 | 29% |
| Student > Ph. D. Student | 115 | 24% |
| Student > Master | 45 | 9% |
| Professor > Associate Professor | 32 | 7% |
| Professor | 25 | 5% |
| Other | 84 | 17% |
| Unknown | 39 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 306 | 64% |
| Biochemistry, Genetics and Molecular Biology | 59 | 12% |
| Computer Science | 29 | 6% |
| Medicine and Dentistry | 15 | 3% |
| Chemistry | 6 | 1% |
| Other | 24 | 5% |
| Unknown | 42 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 05 February 2019.
All research outputs
#4,862,153
of 24,003,070 outputs
Outputs from Genome Biology
#2,731
of 4,279 outputs
Outputs of similar age
#22,278
of 161,884 outputs
Outputs of similar age from Genome Biology
#35
of 115 outputs
Altmetric has tracked 24,003,070 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,279 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.9. This one is in the 35th percentile – i.e., 35% 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 161,884 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 115 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 69% of its contemporaries.