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X Demographics
Mendeley readers
Attention Score in Context
| Title |
BMX: a tool for computing bacterial phyletic composition from orthologous maps
|
|---|---|
| Published in |
BMC Research Notes, February 2015
|
| DOI | 10.1186/s13104-015-1017-z |
| Pubmed ID | |
| Authors |
Benard W Kulohoma |
| Abstract |
New sequencing technologies have made it possible to explore genetic diversity at higher resolution in microbial populations. However, our understanding evolutionary relationships, and comparison of closely and distantly related bacterial genomes from these massive datasets remains a formidable challenge. Numerous clustering algorithms that group genomic data based on homology have been developed, but new tools are still required to analyse the resultant orthologous maps to understand functional genetic similarities and their phyletic patterns (patterns of presence of absence of genes). |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 1 | 20% |
| Researcher | 1 | 20% |
| Other | 1 | 20% |
| Student > Master | 1 | 20% |
| Unknown | 1 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 2 | 40% |
| Agricultural and Biological Sciences | 2 | 40% |
| Unknown | 1 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 March 2015.
All research outputs
#17,286,645
of 25,374,917 outputs
Outputs from BMC Research Notes
#2,501
of 4,513 outputs
Outputs of similar age
#163,164
of 269,964 outputs
Outputs of similar age from BMC Research Notes
#37
of 63 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,513 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 32nd percentile – i.e., 32% 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 269,964 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.