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Mendeley readers
Attention Score in Context
| Chapter title |
Detection and Reconstruction of Circular RNAs from Transcriptomic Data
|
|---|---|
| Chapter number | 1 |
| Book title |
Circular RNAs
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7562-4_1 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7561-7, 978-1-4939-7562-4
|
| Authors |
Yi Zheng, Fangqing Zhao |
| Abstract |
Recent studies have shown that circular RNAs (circRNAs) are a novel class of abundant, stable, and ubiquitous noncoding RNA molecules in eukaryotic organisms. Comprehensive detection and reconstruction of circRNAs from high-throughput transcriptome data is an initial step to study their biogenesis and function. Several tools have been developed to deal with this issue, but they require many steps and are difficult to use. To solve this problem, we provide a protocol for researchers to detect and reconstruct circRNA by employing CIRI2, CIRI-AS, and CIRI-full. This protocol can not only simplify the usage of above tools but also integrate their results. |
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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 40% |
| Student > Master | 3 | 20% |
| Researcher | 3 | 20% |
| Student > Bachelor | 1 | 7% |
| Student > Postgraduate | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 53% |
| Agricultural and Biological Sciences | 2 | 13% |
| Computer Science | 1 | 7% |
| Neuroscience | 1 | 7% |
| Unknown | 3 | 20% |
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 13 January 2018.
All research outputs
#15,488,947
of 23,016,919 outputs
Outputs from Methods in molecular biology
#5,388
of 13,165 outputs
Outputs of similar age
#269,777
of 442,344 outputs
Outputs of similar age from Methods in molecular biology
#596
of 1,498 outputs
Altmetric has tracked 23,016,919 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% 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 44th percentile – i.e., 44% 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 442,344 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% 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 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.