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Cpipe: a shared variant detection pipeline designed for diagnostic settings

Overview of attention for article published in Genome Medicine, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)

Mentioned by

1 blog
28 tweeters
1 Facebook page


68 Dimensions

Readers on

83 Mendeley
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Cpipe: a shared variant detection pipeline designed for diagnostic settings
Published in
Genome Medicine, July 2015
DOI 10.1186/s13073-015-0191-x
Pubmed ID

Simon P. Sadedin, Harriet Dashnow, Paul A. James, Melanie Bahlo, Denis C. Bauer, Andrew Lonie, Sebastian Lunke, Ivan Macciocca, Jason P. Ross, Kirby R. Siemering, Zornitza Stark, Susan M. White, Graham Taylor, Clara Gaff, Alicia Oshlack, Natalie P. Thorne


The benefits of implementing high throughput sequencing in the clinic are quickly becoming apparent. However, few freely available bioinformatics pipelines have been built from the ground up with clinical genomics in mind. Here we present Cpipe, a pipeline designed specifically for clinical genetic disease diagnostics. Cpipe was developed by the Melbourne Genomics Health Alliance, an Australian initiative to promote common approaches to genomics across healthcare institutions. As such, Cpipe has been designed to provide fast, effective and reproducible analysis, while also being highly flexible and customisable to meet the individual needs of diverse clinical settings. Cpipe is being shared with the clinical sequencing community as an open source project and is available at http://cpipeline.org.

Twitter Demographics

The data shown below were collected from the profiles of 28 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 1%
Sweden 1 1%
Canada 1 1%
Spain 1 1%
United States 1 1%
Unknown 78 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 25%
Student > Ph. D. Student 15 18%
Student > Master 10 12%
Other 9 11%
Professor 6 7%
Other 11 13%
Unknown 11 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 22%
Agricultural and Biological Sciences 18 22%
Computer Science 15 18%
Medicine and Dentistry 8 10%
Engineering 6 7%
Other 8 10%
Unknown 10 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 12 March 2016.
All research outputs
of 21,500,234 outputs
Outputs from Genome Medicine
of 1,363 outputs
Outputs of similar age
of 245,017 outputs
Outputs of similar age from Genome Medicine
of 3 outputs
Altmetric has tracked 21,500,234 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,363 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.0. This one has done well, scoring higher than 80% 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 245,017 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.