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Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph

Overview of attention for article published in BMC Bioinformatics, September 2015
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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 (89th percentile)

Mentioned by

blogs
1 blog
twitter
20 tweeters

Citations

dimensions_citation
64 Dimensions

Readers on

mendeley
68 Mendeley
citeulike
1 CiteULike
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Title
Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0709-7
Pubmed ID
Authors

Gaëtan Benoit, Claire Lemaitre, Dominique Lavenier, Erwan Drezen, Thibault Dayris, Raluca Uricaru, Guillaume Rizk

Abstract

Data volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method. We present a novel reference-free method meant to compress data issued from high throughput sequencing technologies. Our approach, implemented in the software LEON, employs techniques derived from existing assembly principles. The method is based on a reference probabilistic de Bruijn Graph, built de novo from the set of reads and stored in a Bloom filter. Each read is encoded as a path in this graph, by memorizing an anchoring kmer and a list of bifurcations. The same probabilistic de Bruijn Graph is used to perform a lossy transformation of the quality scores, which allows to obtain higher compression rates without losing pertinent information for downstream analyses. LEON was run on various real sequencing datasets (whole genome, exome, RNA-seq or metagenomics). In all cases, LEON showed higher overall compression ratios than state-of-the-art compression software. On a C. elegans whole genome sequencing dataset, LEON divided the original file size by more than 20. LEON is an open source software, distributed under GNU affero GPL License, available for download at http://gatb.inria.fr/software/leon/ .

Twitter Demographics

The data shown below were collected from the profiles of 20 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Czechia 1 1%
Israel 1 1%
Colombia 1 1%
Canada 1 1%
Russia 1 1%
Spain 1 1%
Unknown 60 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 25%
Researcher 16 24%
Student > Master 11 16%
Student > Bachelor 5 7%
Student > Doctoral Student 3 4%
Other 10 15%
Unknown 6 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 32%
Computer Science 18 26%
Biochemistry, Genetics and Molecular Biology 12 18%
Engineering 5 7%
Earth and Planetary Sciences 1 1%
Other 1 1%
Unknown 9 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 22 May 2020.
All research outputs
#1,343,679
of 16,638,522 outputs
Outputs from BMC Bioinformatics
#372
of 5,984 outputs
Outputs of similar age
#24,657
of 244,218 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 16,638,522 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 5,984 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 244,218 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 89% 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