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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis
|
|---|---|
| Published in |
BMC Bioinformatics, March 2016
|
| DOI | 10.1186/s12859-016-0992-y |
| Pubmed ID | |
| Authors |
Bo Yang, Yong Wang, Pei-Yuan Qian |
| Abstract |
Prokaryotic 16S ribosomal RNA (rRNA) sequences are widely used in environmental microbiology and molecular evolution as reliable markers for the taxonomic classification and phylogenetic analysis of microbes. Restricted by current sequencing techniques, the massive sequencing of 16S rRNA gene amplicons encompassing the full length of genes is not yet feasible. Thus, the selection of the most efficient hypervariable regions for phylogenetic analysis and taxonomic classification is still debated. In the present study, several bioinformatics tools were integrated to build an in silico pipeline to evaluate the phylogenetic sensitivity of the hypervariable regions compared with the corresponding full-length sequences. The correlation of seven sub-regions was inferred from the geodesic distance, a parameter that is applied to quantitatively compare the topology of different phylogenetic trees constructed using the sequences from different sub-regions. The relationship between different sub-regions based on the geodesic distance indicated that V4-V6 were the most reliable regions for representing the full-length 16S rRNA sequences in the phylogenetic analysis of most bacterial phyla, while V2 and V8 were the least reliable regions. Our results suggest that V4-V6 might be optimal sub-regions for the design of universal primers with superior phylogenetic resolution for bacterial phyla. A potential relationship between function and the evolution of 16S rRNA is also discussed. |
X Demographics
The data shown below were collected from the profiles of 50 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 | 8 | 16% |
| France | 3 | 6% |
| Germany | 3 | 6% |
| United Kingdom | 2 | 4% |
| Australia | 2 | 4% |
| Canada | 2 | 4% |
| India | 1 | 2% |
| Nigeria | 1 | 2% |
| Myanmar | 1 | 2% |
| Other | 6 | 12% |
| Unknown | 21 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 32 | 64% |
| Members of the public | 16 | 32% |
| Science communicators (journalists, bloggers, editors) | 1 | 2% |
| Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,392 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | <1% |
| United Kingdom | 2 | <1% |
| France | 1 | <1% |
| Italy | 1 | <1% |
| Germany | 1 | <1% |
| Mexico | 1 | <1% |
| Argentina | 1 | <1% |
| India | 1 | <1% |
| Estonia | 1 | <1% |
| Other | 3 | <1% |
| Unknown | 1378 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 237 | 17% |
| Student > Master | 227 | 16% |
| Student > Bachelor | 217 | 16% |
| Researcher | 164 | 12% |
| Student > Doctoral Student | 84 | 6% |
| Other | 162 | 12% |
| Unknown | 301 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 350 | 25% |
| Agricultural and Biological Sciences | 317 | 23% |
| Immunology and Microbiology | 112 | 8% |
| Environmental Science | 76 | 5% |
| Medicine and Dentistry | 46 | 3% |
| Other | 135 | 10% |
| Unknown | 356 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 38. 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 27 October 2020.
All research outputs
#1,071,295
of 25,452,734 outputs
Outputs from BMC Bioinformatics
#84
of 7,705 outputs
Outputs of similar age
#18,458
of 314,570 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 126 outputs
Altmetric has tracked 25,452,734 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,705 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 98% 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 314,570 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 94% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.