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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mapping of Mitochondrial RNA-Protein Interactions by Digital RNase Footprinting
|
|---|---|
| Published in |
Cell Reports, October 2013
|
| DOI | 10.1016/j.celrep.2013.09.036 |
| Pubmed ID | |
| Authors |
Ganqiang Liu, Timothy R. Mercer, Anne-Marie J. Shearwood, Stefan J. Siira, Moira E. Hibbs, John S. Mattick, Oliver Rackham, Aleksandra Filipovska |
| Abstract |
Human mitochondrial DNA is transcribed as long polycistronic transcripts that encompass each strand of the genome and are processed subsequently into mature mRNAs, tRNAs, and rRNAs, necessitating widespread posttranscriptional regulation. Here, we establish methods for massively parallel sequencing and analyses of RNase-accessible regions of human mitochondrial RNA and thereby identify specific regions within mitochondrial transcripts that are bound by proteins. This approach provides a range of insights into the contribution of RNA-binding proteins to the regulation of mitochondrial gene expression. |
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 % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| United Kingdom | 1 | 1% |
| Unknown | 86 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 29% |
| Researcher | 16 | 18% |
| Professor > Associate Professor | 9 | 10% |
| Professor | 7 | 8% |
| Student > Master | 7 | 8% |
| Other | 13 | 14% |
| Unknown | 12 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 38 | 42% |
| Agricultural and Biological Sciences | 25 | 28% |
| Computer Science | 5 | 6% |
| Medicine and Dentistry | 4 | 4% |
| Engineering | 3 | 3% |
| Other | 5 | 6% |
| Unknown | 10 | 11% |
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 01 November 2013.
All research outputs
#22,778,604
of 25,394,764 outputs
Outputs from Cell Reports
#12,703
of 12,973 outputs
Outputs of similar age
#199,781
of 226,001 outputs
Outputs of similar age from Cell Reports
#144
of 157 outputs
Altmetric has tracked 25,394,764 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 12,973 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.3. 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 226,001 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 157 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.