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Bloom Filter Trie: an alignment-free and reference-free data structure for pan-genome storage

Overview of attention for article published in Algorithms for Molecular Biology, April 2016
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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 (#11 of 177)
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
19 tweeters

Citations

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31 Dimensions

Readers on

mendeley
63 Mendeley
citeulike
1 CiteULike
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Title
Bloom Filter Trie: an alignment-free and reference-free data structure for pan-genome storage
Published in
Algorithms for Molecular Biology, April 2016
DOI 10.1186/s13015-016-0066-8
Pubmed ID
Authors

Guillaume Holley, Roland Wittler, Jens Stoye

Abstract

High throughput sequencing technologies have become fast and cheap in the past years. As a result, large-scale projects started to sequence tens to several thousands of genomes per species, producing a high number of sequences sampled from each genome. Such a highly redundant collection of very similar sequences is called a pan-genome. It can be transformed into a set of sequences "colored" by the genomes to which they belong. A colored de Bruijn graph (C-DBG) extracts from the sequences all colored k-mers, strings of length k, and stores them in vertices. In this paper, we present an alignment-free, reference-free and incremental data structure for storing a pan-genome as a C-DBG: the bloom filter trie (BFT). The data structure allows to store and compress a set of colored k-mers, and also to efficiently traverse the graph. Bloom filter trie was used to index and query different pangenome datasets. Compared to another state-of-the-art data structure, BFT was up to two times faster to build while using about the same amount of main memory. For querying k-mers, BFT was about 52-66 times faster while using about 5.5-14.3 times less memory. We present a novel succinct data structure called the Bloom Filter Trie for indexing a pan-genome as a colored de Bruijn graph. The trie stores k-mers and their colors based on a new representation of vertices that compress and index shared substrings. Vertices use basic data structures for lightweight substrings storage as well as Bloom filters for efficient trie and graph traversals. Experimental results prove better performance compared to another state-of-the-art data structure. https://www.github.com/GuillaumeHolley/BloomFilterTrie.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 3 5%
Norway 2 3%
Sweden 1 2%
Unknown 57 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 33%
Researcher 16 25%
Student > Master 8 13%
Student > Bachelor 5 8%
Professor 3 5%
Other 4 6%
Unknown 6 10%
Readers by discipline Count As %
Computer Science 27 43%
Agricultural and Biological Sciences 15 24%
Biochemistry, Genetics and Molecular Biology 9 14%
Engineering 2 3%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 8 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 19 April 2016.
All research outputs
#1,387,766
of 11,293,566 outputs
Outputs from Algorithms for Molecular Biology
#11
of 177 outputs
Outputs of similar age
#48,745
of 278,694 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 10 outputs
Altmetric has tracked 11,293,566 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 177 research outputs from this source. They receive a mean Attention Score of 2.8. 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 278,694 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 82% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.