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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Computational prediction of viral miRNAs.
|
|---|---|
| Chapter number | 8 |
| Book title |
Antiviral RNAi
|
| Published in |
Methods in molecular biology, January 2011
|
| DOI | 10.1007/978-1-61779-037-9_8 |
| Pubmed ID | |
| Book ISBNs |
978-1-61779-036-2, 978-1-61779-037-9
|
| Authors |
Adam Grundhoff, Grundhoff, Adam |
| Abstract |
While cloning and/or massive parallel sequencing of small RNAs represent powerful tools for the discovery of novel miRNAs, computational miRNA prediction represents a valuable alternative which can be performed with comparably little technical effort. This is especially true for viruses, as the number of predicted candidates generally remains low and thus within a range that may be readily confirmed by experimental means. Here, we provide a detailed protocol for the prediction of putative miRNA genes using VMir, an ab initio prediction program which we have recently designed specifically to identify pre-miRNAs in viral genomes. |
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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Turkey | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 31 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 15% |
| Researcher | 5 | 15% |
| Student > Doctoral Student | 3 | 9% |
| Student > Bachelor | 3 | 9% |
| Student > Master | 3 | 9% |
| Other | 6 | 18% |
| Unknown | 8 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 33% |
| Biochemistry, Genetics and Molecular Biology | 7 | 21% |
| Immunology and Microbiology | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Computer Science | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 8 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 May 2014.
All research outputs
#14,770,397
of 22,738,543 outputs
Outputs from Methods in molecular biology
#4,667
of 13,087 outputs
Outputs of similar age
#138,778
of 180,492 outputs
Outputs of similar age from Methods in molecular biology
#139
of 230 outputs
Altmetric has tracked 22,738,543 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,087 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 59% 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 180,492 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 230 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.