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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Variations in genome-wide RNAi screens: lessons from influenza research
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, March 2015
|
| DOI | 10.1186/s13336-015-0017-5 |
| Pubmed ID | |
| Authors |
Yu-Chi Chou, Michael MC Lai, Yi-Chen Wu, Nai-Chi Hsu, King-Song Jeng, Wen-Chi Su |
| Abstract |
Genome-wide RNA interference (RNAi) screening is an emerging and powerful technique for genetic screens, which can be divided into arrayed RNAi screen and pooled RNAi screen/selection based on different screening strategies. To date, several genome-wide RNAi screens have been successfully performed to identify host factors essential for influenza virus replication. However, the host factors identified by different research groups are not always consistent. Taking influenza virus screens as an example, we found that a number of screening parameters may directly or indirectly influence the primary hits identified by the screens. This review highlights the differences among the published genome-wide screening approaches and offers recommendations for performing a good pooled shRNA screen/selection. |
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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Turkey | 1 | 2% |
| Austria | 1 | 2% |
| United Kingdom | 1 | 2% |
| Canada | 1 | 2% |
| Denmark | 1 | 2% |
| Unknown | 55 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 28% |
| Researcher | 12 | 20% |
| Student > Bachelor | 6 | 10% |
| Student > Postgraduate | 5 | 8% |
| Student > Master | 5 | 8% |
| Other | 8 | 13% |
| Unknown | 7 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 35% |
| Biochemistry, Genetics and Molecular Biology | 17 | 28% |
| Medicine and Dentistry | 5 | 8% |
| Immunology and Microbiology | 4 | 7% |
| Engineering | 2 | 3% |
| Other | 3 | 5% |
| Unknown | 8 | 13% |
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 07 March 2015.
All research outputs
#17,285,668
of 25,373,627 outputs
Outputs from Journal of Clinical Bioinformatics
#33
of 61 outputs
Outputs of similar age
#164,491
of 271,801 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#4
of 4 outputs
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So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.