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X Demographics
Mendeley readers
Attention Score in Context
| Title |
QsRNA-seq: a method for high-throughput profiling and quantifying small RNAs
|
|---|---|
| Published in |
Genome Biology, August 2018
|
| DOI | 10.1186/s13059-018-1495-0 |
| Pubmed ID | |
| Authors |
Alla Fishman, Dean Light, Ayelet T. Lamm |
| Abstract |
The ability to profile and quantify small non-coding RNAs (sRNAs), specifically microRNAs (miRNAs), using high-throughput sequencing is challenging because of their small size. We developed QsRNA-seq, a method for preparation of sRNA libraries for high-throughput sequencing that overcomes this difficulty by enabling a gel-free separation of fragments shorter than 100 nt that differ only by 20 nt in length. The method allows the use of unique molecular identifiers for quantification and is more amenable to automation than gel-based methods. We show that QsRNA-seq gives very accurate, comprehensive, and reproducible results by looking at miRNAs in Caenorhabditis elegans embryos and larvae. |
X Demographics
The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 22% |
| Israel | 2 | 11% |
| France | 1 | 6% |
| United Kingdom | 1 | 6% |
| Unknown | 10 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 50% |
| Scientists | 8 | 44% |
| Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 73 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 22% |
| Researcher | 16 | 22% |
| Student > Bachelor | 6 | 8% |
| Student > Master | 6 | 8% |
| Student > Doctoral Student | 4 | 5% |
| Other | 4 | 5% |
| Unknown | 21 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 30% |
| Agricultural and Biological Sciences | 19 | 26% |
| Immunology and Microbiology | 2 | 3% |
| Neuroscience | 2 | 3% |
| Linguistics | 1 | 1% |
| Other | 4 | 5% |
| Unknown | 23 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 22 October 2021.
All research outputs
#3,344,831
of 25,385,509 outputs
Outputs from Genome Biology
#2,395
of 4,468 outputs
Outputs of similar age
#63,795
of 341,562 outputs
Outputs of similar age from Genome Biology
#56
of 67 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 46th percentile – i.e., 46% 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 341,562 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.