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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SUSHI: an exquisite recipe for fully documented, reproducible and reusable NGS data analysis
|
|---|---|
| Published in |
BMC Bioinformatics, June 2016
|
| DOI | 10.1186/s12859-016-1104-8 |
| Pubmed ID | |
| Authors |
Masaomi Hatakeyama, Lennart Opitz, Giancarlo Russo, Weihong Qi, Ralph Schlapbach, Hubert Rehrauer |
| Abstract |
Next generation sequencing (NGS) produces massive datasets consisting of billions of reads and up to thousands of samples. Subsequent bioinformatic analysis is typically done with the help of open source tools, where each application performs a single step towards the final result. This situation leaves the bioinformaticians with the tasks to combine the tools, manage the data files and meta-information, document the analysis, and ensure reproducibility. We present SUSHI, an agile data analysis framework that relieves bioinformaticians from the administrative challenges of their data analysis. SUSHI lets users build reproducible data analysis workflows from individual applications and manages the input data, the parameters, meta-information with user-driven semantics, and the job scripts. As distinguishing features, SUSHI provides an expert command line interface as well as a convenient web interface to run bioinformatics tools. SUSHI datasets are self-contained and self-documented on the file system. This makes them fully reproducible and ready to be shared. With the associated meta-information being formatted as plain text tables, the datasets can be readily further analyzed and interpreted outside SUSHI. SUSHI provides an exquisite recipe for analysing NGS data. By following the SUSHI recipe, SUSHI makes data analysis straightforward and takes care of documentation and administration tasks. Thus, the user can fully dedicate his time to the analysis itself. SUSHI is suitable for use by bioinformaticians as well as life science researchers. It is targeted for, but by no means constrained to, NGS data analysis. Our SUSHI instance is in productive use and has served as data analysis interface for more than 1000 data analysis projects. SUSHI source code as well as a demo server are freely available. |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Luxembourg | 1 | 5% |
| Japan | 1 | 5% |
| Germany | 1 | 5% |
| Greece | 1 | 5% |
| Australia | 1 | 5% |
| United States | 1 | 5% |
| Canada | 1 | 5% |
| Mexico | 1 | 5% |
| Norway | 1 | 5% |
| Other | 3 | 16% |
| Unknown | 7 | 37% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 12 | 63% |
| Members of the public | 4 | 21% |
| Science communicators (journalists, bloggers, editors) | 2 | 11% |
| Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 152 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | <1% |
| Chile | 1 | <1% |
| Brazil | 1 | <1% |
| Czechia | 1 | <1% |
| China | 1 | <1% |
| Unknown | 147 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 39 | 26% |
| Student > Ph. D. Student | 27 | 18% |
| Student > Master | 24 | 16% |
| Student > Bachelor | 12 | 8% |
| Student > Doctoral Student | 9 | 6% |
| Other | 17 | 11% |
| Unknown | 24 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 52 | 34% |
| Biochemistry, Genetics and Molecular Biology | 24 | 16% |
| Computer Science | 10 | 7% |
| Engineering | 9 | 6% |
| Medicine and Dentistry | 7 | 5% |
| Other | 21 | 14% |
| Unknown | 29 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 24 July 2017.
All research outputs
#3,476,483
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#1,257
of 7,454 outputs
Outputs of similar age
#62,046
of 342,727 outputs
Outputs of similar age from BMC Bioinformatics
#15
of 89 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 82% 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 342,727 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 89 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.