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Data compression for sequencing data

Overview of attention for article published in Algorithms for Molecular Biology, November 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#39 of 209)
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
9 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
63 Dimensions

Readers on

mendeley
103 Mendeley
citeulike
3 CiteULike
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Title
Data compression for sequencing data
Published in
Algorithms for Molecular Biology, November 2013
DOI 10.1186/1748-7188-8-25
Pubmed ID
Authors

Sebastian Deorowicz, Szymon Grabowski

Abstract

: Post-Sanger sequencing methods produce tons of data, and there is a general agreement that the challenge to store and process them must be addressed with data compression. In this review we first answer the question "why compression" in a quantitative manner. Then we also answer the questions "what" and "how", by sketching the fundamental compression ideas, describing the main sequencing data types and formats, and comparing the specialized compression algorithms and tools. Finally, we go back to the question "why compression" and give other, perhaps surprising answers, demonstrating the pervasiveness of data compression techniques in computational biology.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
France 3 3%
Sweden 2 2%
Spain 2 2%
Netherlands 2 2%
Denmark 1 <1%
United Kingdom 1 <1%
Brazil 1 <1%
Germany 1 <1%
Other 3 3%
Unknown 84 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 27%
Student > Ph. D. Student 26 25%
Student > Master 20 19%
Other 5 5%
Student > Bachelor 5 5%
Other 13 13%
Unknown 6 6%
Readers by discipline Count As %
Computer Science 37 36%
Agricultural and Biological Sciences 33 32%
Engineering 12 12%
Biochemistry, Genetics and Molecular Biology 7 7%
Medicine and Dentistry 1 <1%
Other 3 3%
Unknown 10 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 06 August 2017.
All research outputs
#3,125,832
of 13,847,329 outputs
Outputs from Algorithms for Molecular Biology
#39
of 209 outputs
Outputs of similar age
#48,889
of 251,779 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 14 outputs
Altmetric has tracked 13,847,329 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 209 research outputs from this source. They receive a mean Attention Score of 3.0. 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 251,779 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 80% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.