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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Next-Generation Sequencing for Binary Protein–Protein Interactions
|
|---|---|
| Published in |
Frontiers in Genetics, December 2015
|
| DOI | 10.3389/fgene.2015.00346 |
| Pubmed ID | |
| Authors |
Bernhard Suter, Xinmin Zhang, C. Gustavo Pesce, Andrew R. Mendelsohn, Savithramma P. Dinesh-Kumar, Jian-Hua Mao |
| Abstract |
The yeast two-hybrid (Y2H) system exploits host cell genetics in order to display binary protein-protein interactions (PPIs) via defined and selectable phenotypes. Numerous improvements have been made to this method, adapting the screening principle for diverse applications, including drug discovery and the scale-up for proteome wide interaction screens in human and other organisms. Here we discuss a systematic workflow and analysis scheme for screening data generated by Y2H and related assays that includes high-throughput selection procedures, readout of comprehensive results via next-generation sequencing (NGS), and the interpretation of interaction data via quantitative statistics. The novel assays and tools will serve the broader scientific community to harness the power of NGS technology to address PPI networks in health and disease. We discuss examples of how this next-generation platform can be applied to address specific questions in diverse fields of biology and medicine. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Scientists | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Bangladesh | 1 | 1% |
| Unknown | 75 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 28% |
| Researcher | 16 | 21% |
| Student > Bachelor | 9 | 12% |
| Student > Master | 5 | 7% |
| Student > Doctoral Student | 4 | 5% |
| Other | 12 | 16% |
| Unknown | 9 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 26 | 34% |
| Biochemistry, Genetics and Molecular Biology | 23 | 30% |
| Computer Science | 6 | 8% |
| Engineering | 3 | 4% |
| Medicine and Dentistry | 2 | 3% |
| Other | 4 | 5% |
| Unknown | 12 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 15 January 2016.
All research outputs
#8,030,998
of 24,826,104 outputs
Outputs from Frontiers in Genetics
#2,553
of 13,366 outputs
Outputs of similar age
#111,755
of 373,741 outputs
Outputs of similar age from Frontiers in Genetics
#13
of 44 outputs
Altmetric has tracked 24,826,104 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 13,366 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done well, scoring higher than 79% of its peers.
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 373,741 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.