| Title |
FANTOM: Functional and taxonomic analysis of metagenomes
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-38 |
| Pubmed ID | |
| Authors |
Kemal Sanli, Fredrik H Karlsson, Intawat Nookaew, Jens Nielsen |
| Abstract |
Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context. |
X Demographics
The data shown below were collected from the profiles of 18 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 | 4 | 22% |
| Japan | 2 | 11% |
| Australia | 2 | 11% |
| Canada | 1 | 6% |
| United Kingdom | 1 | 6% |
| India | 1 | 6% |
| France | 1 | 6% |
| Unknown | 6 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 9 | 50% |
| Members of the public | 9 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 181 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 2% |
| Germany | 3 | 2% |
| Brazil | 3 | 2% |
| Italy | 2 | 1% |
| Estonia | 2 | 1% |
| Sweden | 1 | <1% |
| South Africa | 1 | <1% |
| Belgium | 1 | <1% |
| Netherlands | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 161 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 48 | 27% |
| Researcher | 48 | 27% |
| Student > Master | 28 | 15% |
| Other | 9 | 5% |
| Professor | 8 | 4% |
| Other | 29 | 16% |
| Unknown | 11 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 93 | 51% |
| Biochemistry, Genetics and Molecular Biology | 30 | 17% |
| Computer Science | 18 | 10% |
| Environmental Science | 5 | 3% |
| Immunology and Microbiology | 3 | 2% |
| Other | 15 | 8% |
| Unknown | 17 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 11 December 2013.
All research outputs
#2,829,023
of 22,694,633 outputs
Outputs from BMC Bioinformatics
#976
of 7,254 outputs
Outputs of similar age
#30,958
of 282,530 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 137 outputs
Altmetric has tracked 22,694,633 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,254 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 86% 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 282,530 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 89% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.