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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
A Highly Efficient Strategy for Overexpressing circRNAs
|
|---|---|
| Chapter number | 8 |
| Book title |
Circular RNAs
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7562-4_8 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7561-7, 978-1-4939-7562-4
|
| Authors |
Dawei Liu, Vanessa Conn, Gregory J. Goodall, Simon J. Conn |
| Abstract |
Circular RNAs (circRNAs) constitute an emerging class of widespread, abundant, and evolutionarily conserved noncoding RNA. They play important and diverse roles in cell development, growth, and tumorigenesis, but functions of the majority of circRNAs remain enigmatic. In order to investigate circRNA function it is necessary to manipulate its expression. While various standard approaches exist for circRNA knockdown, here we present cloning vectors for simplifying the laborious process of cloning circRNAs to achieve high-efficiency overexpression in mammalian cell lines. |
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 % |
|---|---|---|
| Australia | 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 27% |
| Researcher | 7 | 14% |
| Student > Master | 6 | 12% |
| Student > Bachelor | 5 | 10% |
| Student > Doctoral Student | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 9 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 45% |
| Neuroscience | 5 | 10% |
| Medicine and Dentistry | 4 | 8% |
| Agricultural and Biological Sciences | 4 | 8% |
| Chemical Engineering | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 11 | 22% |
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 23 January 2018.
All research outputs
#18,583,054
of 23,016,919 outputs
Outputs from Methods in molecular biology
#7,965
of 13,165 outputs
Outputs of similar age
#330,553
of 442,354 outputs
Outputs of similar age from Methods in molecular biology
#950
of 1,498 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,165 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% 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 442,354 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.