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bioSyntax: syntax highlighting for computational biology

Overview of attention for article published in BMC Bioinformatics, August 2018
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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 (95th percentile)

Mentioned by

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28 X users
reddit
1 Redditor

Citations

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3 Dimensions

Readers on

mendeley
50 Mendeley
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Title
bioSyntax: syntax highlighting for computational biology
Published in
BMC Bioinformatics, August 2018
DOI 10.1186/s12859-018-2315-y
Pubmed ID
Authors

Artem Babaian, Anicet Ebou, Alyssa Fegen, Ho Yin Kam, German E. Novakovsky, Jasper Wong, Dylan Aïssi, Li Yao

Abstract

Computational biology requires the reading and comprehension of biological data files. Plain-text formats such as SAM, VCF, GTF, PDB and FASTA, often contain critical information which is obfuscated by the data structure complexity. bioSyntax ( https://biosyntax.org/ ) is a freely available suite of biological syntax highlighting packages for vim, gedit, Sublime, VSCode, and less. bioSyntax improves the legibility of low-level biological data in the bioinformatics workspace. bioSyntax supports computational scientists in parsing and comprehending their data efficiently and thus can accelerate research output.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 24%
Student > Ph. D. Student 9 18%
Student > Bachelor 6 12%
Student > Master 6 12%
Other 4 8%
Other 5 10%
Unknown 8 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 26%
Agricultural and Biological Sciences 12 24%
Computer Science 8 16%
Linguistics 2 4%
Nursing and Health Professions 1 2%
Other 3 6%
Unknown 11 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 08 November 2022.
All research outputs
#2,084,142
of 25,457,297 outputs
Outputs from BMC Bioinformatics
#464
of 7,705 outputs
Outputs of similar age
#41,851
of 342,594 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 91 outputs
Altmetric has tracked 25,457,297 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,705 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 93% 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 342,594 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 91 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 95% of its contemporaries.