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Mendeley readers
Attention Score in Context
| Title |
A novel molecular typing method of Mycobacteria based on DNA barcoding visualization
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, February 2014
|
| DOI | 10.1186/2043-9113-4-4 |
| Pubmed ID | |
| Authors |
Bin Liu, Xiaotian Zhang, Honglan Huang, Ying Zhang, Fengfeng Zhou, Guoqing Wang |
| Abstract |
Different subtypes of Mycobacterium tuberculosis (MTB) may induce diverse severe human infections, and some of their symptoms are similar to other pathogenes, e.g. Nontuberculosis mycobacteria (NTM). So determination of mycobacterium subtypes facilitates the effective control of MTB infection and proliferation. This study exploits a novel DNA barcoding visualization method for molecular typing of 17 mycobacteria genomes published in the NCBI prokaryotic genome database. Three mycobacterium genes (Rv0279c, Rv3508 and Rv3514) from the PE/PPE family of MT Band were detected to best represent the inter-strain pathogenetic variations. An accurate and fast MTB substrain typing method was proposed based on the combination of the aforementioned three biomarker genes and the 16S rRNA gene. The protocol of establishing a bacterial substrain typing system used in this study may also be applied to the other pathogenes. |
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 % |
|---|---|---|
| Brazil | 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | 5% |
| Unknown | 18 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 32% |
| Researcher | 5 | 26% |
| Lecturer > Senior Lecturer | 2 | 11% |
| Student > Ph. D. Student | 1 | 5% |
| Other | 1 | 5% |
| Other | 2 | 11% |
| Unknown | 2 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 6 | 32% |
| Biochemistry, Genetics and Molecular Biology | 5 | 26% |
| Medicine and Dentistry | 3 | 16% |
| Immunology and Microbiology | 2 | 11% |
| Computer Science | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 2 | 11% |
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 07 April 2014.
All research outputs
#20,656,820
of 25,374,647 outputs
Outputs from Journal of Clinical Bioinformatics
#44
of 61 outputs
Outputs of similar age
#177,006
of 238,973 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#3
of 4 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 3rd percentile – i.e., 3% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.