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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Workflow for a Computational Analysis of an sRNA Candidate in Bacteria
|
|---|---|
| Chapter number | 1 |
| Book title |
Bacterial Regulatory RNA
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7634-8_1 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7633-1, 978-1-4939-7634-8
|
| Authors |
Patrick R. Wright, Jens Georg |
| Abstract |
Computational methods can often facilitate the functional characterization of individual sRNAs and furthermore allow high-throughput analysis on large numbers of sRNA candidates. This chapter outlines a potential workflow for computational sRNA analyses and describes in detail methods for homolog detection, target prediction, and functional characterization based on enrichment analysis. The cyanobacterial sRNA IsaR1 is used as a specific example. All methods are available as webservers and easily accessible for nonexpert users. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 18% |
| Student > Bachelor | 2 | 18% |
| Student > Master | 2 | 18% |
| Researcher | 1 | 9% |
| Unknown | 4 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 36% |
| Agricultural and Biological Sciences | 2 | 18% |
| Engineering | 1 | 9% |
| Unknown | 4 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 28 February 2018.
All research outputs
#13,050,328
of 23,577,654 outputs
Outputs from Methods in molecular biology
#3,276
of 13,410 outputs
Outputs of similar age
#202,270
of 445,160 outputs
Outputs of similar age from Methods in molecular biology
#263
of 1,482 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,410 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 75% 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 445,160 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 1,482 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.