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glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data

Overview of attention for article published in Cell Regeneration, January 2014
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

blogs
1 blog
twitter
6 tweeters

Citations

dimensions_citation
38 Dimensions

Readers on

mendeley
25 Mendeley
citeulike
2 CiteULike
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Title
glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data
Published in
Cell Regeneration, January 2014
DOI 10.1186/2045-9769-3-1
Pubmed ID
Authors

Andrew Paul Hutchins, Ralf Jauch, Mateusz Dyla, Diego Miranda-Saavedra

Abstract

Genomic datasets and the tools to analyze them have proliferated at an astonishing rate. However, such tools are often poorly integrated with each other: each program typically produces its own custom output in a variety of non-standard file formats. Here we present glbase, a framework that uses a flexible set of descriptors that can quickly parse non-binary data files. glbase includes many functions to intersect two lists of data, including operations on genomic interval data and support for the efficient random access to huge genomic data files. Many glbase functions can produce graphical outputs, including scatter plots, heatmaps, boxplots and other common analytical displays of high-throughput data such as RNA-seq, ChIP-seq and microarray expression data. glbase is designed to rapidly bring biological data into a Python-based analytical environment to facilitate analysis and data processing. In summary, glbase is a flexible and multifunctional toolkit that allows the combination and analysis of high-throughput data (especially next-generation sequencing and genome-wide data), and which has been instrumental in the analysis of complex data sets. glbase is freely available at http://bitbucket.org/oaxiom/glbase/.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 8%
France 1 4%
Japan 1 4%
Austria 1 4%
Unknown 20 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 36%
Researcher 7 28%
Student > Doctoral Student 2 8%
Student > Master 2 8%
Professor 1 4%
Other 3 12%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 56%
Agricultural and Biological Sciences 4 16%
Computer Science 3 12%
Earth and Planetary Sciences 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 1 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 20 November 2014.
All research outputs
#972,682
of 8,761,118 outputs
Outputs from Cell Regeneration
#5
of 35 outputs
Outputs of similar age
#23,645
of 193,175 outputs
Outputs of similar age from Cell Regeneration
#1
of 6 outputs
Altmetric has tracked 8,761,118 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 35 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one scored the same or higher as 30 of them.
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 193,175 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 87% of its contemporaries.
We're also able to compare this research output to 6 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