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X Demographics
Mendeley readers
Attention Score in Context
| Title |
PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens
|
|---|---|
| Published in |
Scientific Reports, November 2017
|
| DOI | 10.1038/s41598-017-16193-9 |
| Pubmed ID | |
| Authors |
Philipp N. Spahn, Tyler Bath, Ryan J. Weiss, Jihoon Kim, Jeffrey D. Esko, Nathan E. Lewis, Olivier Harismendy |
| Abstract |
Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets. |
X Demographics
The data shown below were collected from the profiles of 20 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 | 7 | 35% |
| Canada | 2 | 10% |
| Portugal | 1 | 5% |
| France | 1 | 5% |
| Montenegro | 1 | 5% |
| Germany | 1 | 5% |
| Unknown | 7 | 35% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 50% |
| Members of the public | 10 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 145 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 34 | 23% |
| Researcher | 23 | 16% |
| Student > Doctoral Student | 11 | 8% |
| Student > Bachelor | 10 | 7% |
| Student > Master | 7 | 5% |
| Other | 17 | 12% |
| Unknown | 43 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 47 | 32% |
| Agricultural and Biological Sciences | 21 | 14% |
| Computer Science | 8 | 6% |
| Medicine and Dentistry | 7 | 5% |
| Immunology and Microbiology | 6 | 4% |
| Other | 12 | 8% |
| Unknown | 44 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 27 May 2020.
All research outputs
#2,617,496
of 23,008,860 outputs
Outputs from Scientific Reports
#22,355
of 124,271 outputs
Outputs of similar age
#59,553
of 437,479 outputs
Outputs of similar age from Scientific Reports
#687
of 4,070 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 124,271 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one has done well, scoring higher than 81% 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 437,479 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 86% of its contemporaries.
We're also able to compare this research output to 4,070 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.