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Visual programming for next-generation sequencing data analytics

Overview of attention for article published in BioData Mining, April 2016
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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 (85th percentile)

Mentioned by

18 tweeters
1 Facebook page


11 Dimensions

Readers on

100 Mendeley
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Visual programming for next-generation sequencing data analytics
Published in
BioData Mining, April 2016
DOI 10.1186/s13040-016-0095-3
Pubmed ID

Franco Milicchio, Rebecca Rose, Jiang Bian, Jae Min, Mattia Prosperi


High-throughput or next-generation sequencing (NGS) technologies have become an established and affordable experimental framework in biological and medical sciences for all basic and translational research. Processing and analyzing NGS data is challenging. NGS data are big, heterogeneous, sparse, and error prone. Although a plethora of tools for NGS data analysis has emerged in the past decade, (i) software development is still lagging behind data generation capabilities, and (ii) there is a 'cultural' gap between the end user and the developer. Generic software template libraries specifically developed for NGS can help in dealing with the former problem, whilst coupling template libraries with visual programming may help with the latter. Here we scrutinize the state-of-the-art low-level software libraries implemented specifically for NGS and graphical tools for NGS analytics. An ideal developing environment for NGS should be modular (with a native library interface), scalable in computational methods (i.e. serial, multithread, distributed), transparent (platform-independent), interoperable (with external software interface), and usable (via an intuitive graphical user interface). These characteristics should facilitate both the run of standardized NGS pipelines and the development of new workflows based on technological advancements or users' needs. We discuss in detail the potential of a computational framework blending generic template programming and visual programming that addresses all of the current limitations. In the long term, a proper, well-developed (although not necessarily unique) software framework will bridge the current gap between data generation and hypothesis testing. This will eventually facilitate the development of novel diagnostic tools embedded in routine healthcare.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Cuba 1 1%
Pakistan 1 1%
Italy 1 1%
Unknown 95 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 29%
Student > Ph. D. Student 18 18%
Student > Master 13 13%
Student > Doctoral Student 8 8%
Student > Bachelor 7 7%
Other 19 19%
Unknown 6 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 24%
Computer Science 19 19%
Biochemistry, Genetics and Molecular Biology 18 18%
Medicine and Dentistry 8 8%
Engineering 7 7%
Other 14 14%
Unknown 10 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 25 May 2016.
All research outputs
of 15,920,152 outputs
Outputs from BioData Mining
of 252 outputs
Outputs of similar age
of 266,041 outputs
Outputs of similar age from BioData Mining
of 1 outputs
Altmetric has tracked 15,920,152 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 252 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has done well, scoring higher than 78% 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 266,041 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them