| Title |
Predicting the functional repertoire of an organism from unassembled RNA–seq data
|
|---|---|
| Published in |
BMC Genomics, November 2014
|
| DOI | 10.1186/1471-2164-15-1003 |
| Pubmed ID | |
| Authors |
Manuel Landesfeind, Peter Meinicke |
| Abstract |
The annotation of biomolecular functions is an essential step in the analysis of newly sequenced organisms. Usually, the functions are inferred from predicted genes on the genome using homology search techniques. A high quality genomic sequence is an important prerequisite which, however, is difficult to achieve for certain organisms, such as hybrids or organisms with a large genome. For functional analysis it is also possible to use a de novo transcriptome assembly but the computational requirements can be demanding. Up to now, it is unclear how much of the functional repertoire of an organism can be reliably predicted from unassembled RNA-seq short reads alone. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 33% |
| France | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 83% |
| Members of the public | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Argentina | 1 | 3% |
| Switzerland | 1 | 3% |
| Canada | 1 | 3% |
| Unknown | 34 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 26% |
| Researcher | 8 | 21% |
| Student > Ph. D. Student | 7 | 18% |
| Lecturer | 2 | 5% |
| Professor > Associate Professor | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 5 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 45% |
| Biochemistry, Genetics and Molecular Biology | 7 | 18% |
| Computer Science | 4 | 11% |
| Environmental Science | 2 | 5% |
| Medicine and Dentistry | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 6 | 16% |
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 27 November 2014.
All research outputs
#14,599,159
of 25,371,288 outputs
Outputs from BMC Genomics
#4,932
of 11,244 outputs
Outputs of similar age
#184,273
of 369,134 outputs
Outputs of similar age from BMC Genomics
#141
of 354 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 54% of its peers.
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We're also able to compare this research output to 354 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 58% of its contemporaries.