| 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
The data shown below were collected from the profiles of 27 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 Kingdom | 5 | 19% |
| United States | 2 | 7% |
| France | 2 | 7% |
| Canada | 1 | 4% |
| Taiwan | 1 | 4% |
| India | 1 | 4% |
| Spain | 1 | 4% |
| Netherlands | 1 | 4% |
| Italy | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 12 | 44% |
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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 27% |
| Student > Ph. D. Student | 11 | 16% |
| Professor | 7 | 10% |
| Student > Bachelor | 5 | 7% |
| 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 | 14 | 21% |
| 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
#1,604,161
of 25,728,350 outputs
Outputs from Genome Biology
#1,294
of 4,508 outputs
Outputs of similar age
#35,474
of 449,332 outputs
Outputs of similar age from Genome Biology
#18
of 46 outputs
Altmetric has tracked 25,728,350 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 4,508 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.5. This one has gotten more attention than average, scoring higher than 71% of its peers.
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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.