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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Watchdog – a workflow management system for the distributed analysis of large-scale experimental data
|
|---|---|
| Published in |
BMC Bioinformatics, March 2018
|
| DOI | 10.1186/s12859-018-2107-4 |
| Pubmed ID | |
| Authors |
Michael Kluge, Caroline C. Friedel |
| Abstract |
The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components. In this article, we present Watchdog, a WMS for the automated analysis of large-scale experimental data. Main features include straightforward processing of replicate data, support for distributed computer systems, customizable error detection and manual intervention into workflow execution. Watchdog is implemented in Java and thus platform-independent and allows easy sharing of workflows and corresponding program modules. It provides a graphical user interface (GUI) for workflow construction using pre-defined modules as well as a helper script for creating new module definitions. Execution of workflows is possible using either the GUI or a command-line interface and a web-interface is provided for monitoring the execution status and intervening in case of errors. To illustrate its potentials on a real-life example, a comprehensive workflow and modules for the analysis of RNA-seq experiments were implemented and are provided with the software in addition to simple test examples. Watchdog is a powerful and flexible WMS for the analysis of large-scale high-throughput experiments. We believe it will greatly benefit both users with and without programming skills who want to develop and apply bioinformatical workflows with reasonable overhead. The software, example workflows and a comprehensive documentation are freely available at www.bio.ifi.lmu.de/watchdog. |
X Demographics
The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 13% |
| United States | 2 | 13% |
| Latvia | 1 | 6% |
| Luxembourg | 1 | 6% |
| Netherlands | 1 | 6% |
| Malaysia | 1 | 6% |
| Australia | 1 | 6% |
| United Kingdom | 1 | 6% |
| Unknown | 6 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 10 | 63% |
| Members of the public | 6 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 24% |
| Student > Ph. D. Student | 12 | 18% |
| Student > Master | 11 | 16% |
| Student > Bachelor | 5 | 7% |
| Professor | 3 | 4% |
| Other | 8 | 12% |
| Unknown | 12 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 16 | 24% |
| Biochemistry, Genetics and Molecular Biology | 13 | 19% |
| Agricultural and Biological Sciences | 11 | 16% |
| Immunology and Microbiology | 4 | 6% |
| Engineering | 3 | 4% |
| Other | 5 | 7% |
| Unknown | 15 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 28 September 2018.
All research outputs
#2,205,608
of 24,482,039 outputs
Outputs from BMC Bioinformatics
#542
of 7,543 outputs
Outputs of similar age
#47,339
of 338,091 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 109 outputs
Altmetric has tracked 24,482,039 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,543 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 particularly well, scoring higher than 92% 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 338,091 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 85% of its contemporaries.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.