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Mendeley readers
Attention Score in Context
Chapter title |
nRIP-seq: A Technique to Identify RNA Targets of an RNA Binding Protein on a Genome-Wide Scale.
|
---|---|
Chapter number | 9 |
Book title |
Regulatory Non-Coding RNAs
|
Published in |
Methods in molecular biology, January 2015
|
DOI | 10.1007/978-1-4939-1369-5_9 |
Pubmed ID | |
Book ISBNs |
978-1-4939-1368-8, 978-1-4939-1369-5
|
Authors |
Jing Crystal Zhao |
Abstract |
Native RNA immunoprecipitation (nRIP) coupled with high-throughput sequencing (nRIP-seq) is a powerful technique that allows transcriptome-wide identification of the entire subset of coding and noncoding RNAs associated with a particular protein. Since this technology is carried out in a native condition without cross-linking, nRIP-seq detects RNAs that bind a protein directly or indirectly through a larger RNA-protein complex. Here, we use the interaction between RNA and chromatin modifiers, Polycomb proteins, as an example to describe this method. Using nRIP-seq, we provide a snapshot of Ezh2, a Polycomb component, and RNA interaction in mouse embryonic stem cells. |
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 % |
---|---|---|
France | 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 3% |
Unknown | 31 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 34% |
Researcher | 6 | 19% |
Student > Bachelor | 3 | 9% |
Student > Doctoral Student | 3 | 9% |
Other | 2 | 6% |
Other | 5 | 16% |
Unknown | 2 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 15 | 47% |
Agricultural and Biological Sciences | 9 | 28% |
Neuroscience | 3 | 9% |
Medicine and Dentistry | 1 | 3% |
Immunology and Microbiology | 1 | 3% |
Other | 0 | 0% |
Unknown | 3 | 9% |
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 September 2014.
All research outputs
#20,237,640
of 22,764,165 outputs
Outputs from Methods in molecular biology
#9,865
of 13,089 outputs
Outputs of similar age
#295,617
of 352,894 outputs
Outputs of similar age from Methods in molecular biology
#634
of 995 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 1st percentile – i.e., 1% 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 352,894 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 995 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.