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ReCoil - an algorithm for compression of extremely large datasets of dna data

Overview of attention for article published in Algorithms for Molecular Biology, October 2011
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)

Mentioned by

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1 X user
patent
1 patent

Citations

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29 Dimensions

Readers on

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42 Mendeley
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3 CiteULike
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Title
ReCoil - an algorithm for compression of extremely large datasets of dna data
Published in
Algorithms for Molecular Biology, October 2011
DOI 10.1186/1748-7188-6-23
Pubmed ID
Authors

Vladimir Yanovsky

Abstract

The growing volume of generated DNA sequencing data makes the problem of its long term storage increasingly important. In this work we present ReCoil - an I/O efficient external memory algorithm designed for compression of very large collections of short reads DNA data. Typically each position of DNA sequence is covered by multiple reads of a short read dataset and our algorithm makes use of resulting redundancy to achieve high compression rate.While compression based on encoding mismatches between the dataset and a similar reference can yield high compression rate, good quality reference sequence may be unavailable. Instead, ReCoil's compression is based on encoding the differences between similar or overlapping reads. As such reads may appear at large distances from each other in the dataset and since random access memory is a limited resource, ReCoil is designed to work efficiently in external memory, leveraging high bandwidth of modern hard disk drives.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 10%
Germany 2 5%
France 2 5%
Sweden 1 2%
Portugal 1 2%
Unknown 32 76%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 43%
Student > Ph. D. Student 8 19%
Student > Bachelor 4 10%
Student > Master 4 10%
Professor 2 5%
Other 4 10%
Unknown 2 5%
Readers by discipline Count As %
Computer Science 17 40%
Agricultural and Biological Sciences 16 38%
Biochemistry, Genetics and Molecular Biology 2 5%
Engineering 2 5%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 3 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 01 July 2020.
All research outputs
#6,375,151
of 22,653,392 outputs
Outputs from Algorithms for Molecular Biology
#59
of 264 outputs
Outputs of similar age
#38,256
of 135,951 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 4 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done well, scoring higher than 76% 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 135,951 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.