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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MICRA: an automatic pipeline for fast characterization of microbial genomes from high-throughput sequencing data
|
|---|---|
| Published in |
Genome Biology, December 2017
|
| DOI | 10.1186/s13059-017-1367-z |
| Pubmed ID | |
| Authors |
Ségolène Caboche, Gaël Even, Alexandre Loywick, Christophe Audebert, David Hot |
| Abstract |
The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 11% |
| Italy | 1 | 4% |
| Spain | 1 | 4% |
| France | 1 | 4% |
| Netherlands | 1 | 4% |
| United States | 1 | 4% |
| Unknown | 19 | 70% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 17 | 63% |
| Members of the public | 8 | 30% |
| Science communicators (journalists, bloggers, editors) | 2 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 68 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 26% |
| Student > Ph. D. Student | 11 | 16% |
| Professor | 7 | 10% |
| Student > Bachelor | 6 | 9% |
| Student > Master | 5 | 7% |
| Other | 6 | 9% |
| Unknown | 15 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 28% |
| Biochemistry, Genetics and Molecular Biology | 15 | 22% |
| Computer Science | 6 | 9% |
| Immunology and Microbiology | 4 | 6% |
| Veterinary Science and Veterinary Medicine | 2 | 3% |
| Other | 5 | 7% |
| Unknown | 17 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 24. 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 15 January 2018.
All research outputs
#1,663,183
of 26,393,590 outputs
Outputs from Genome Biology
#1,315
of 4,605 outputs
Outputs of similar age
#35,871
of 453,316 outputs
Outputs of similar age from Genome Biology
#18
of 46 outputs
Altmetric has tracked 26,393,590 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,605 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.9. This one has gotten more attention than average, scoring higher than 71% 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 453,316 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 46 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 60% of its contemporaries.