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A graph extension of the positional Burrows–Wheeler transform and its applications

Overview of attention for article published in Algorithms for Molecular Biology, July 2017
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About this Attention Score

  • Among the highest-scoring outputs from this source (#40 of 180)
  • Good Attention Score compared to outputs of the same age (67th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

twitter
9 tweeters

Citations

dimensions_citation
19 Dimensions

Readers on

mendeley
47 Mendeley
citeulike
2 CiteULike
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Title
A graph extension of the positional Burrows–Wheeler transform and its applications
Published in
Algorithms for Molecular Biology, July 2017
DOI 10.1186/s13015-017-0109-9
Pubmed ID
Authors

Adam M. Novak, Erik Garrison, Benedict Paten

Abstract

We present a generalization of the positional Burrows-Wheeler transform, or PBWT, to genome graphs, which we call the gPBWT. A genome graph is a collapsed representation of a set of genomes described as a graph. In a genome graph, a haplotype corresponds to a restricted form of walk. The gPBWT is a compressible representation of a set of these graph-encoded haplotypes that allows for efficient subhaplotype match queries. We give efficient algorithms for gPBWT construction and query operations. As a demonstration, we use the gPBWT to quickly count the number of haplotypes consistent with random walks in a genome graph, and with the paths taken by mapped reads; results suggest that haplotype consistency information can be practically incorporated into graph-based read mappers. We estimate that with the gPBWT of the order of 100,000 diploid genomes, including all forms structural variation, could be stored and made searchable for haplotype queries using a single large compute node.

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 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Sweden 1 2%
Norway 1 2%
Unknown 44 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 30%
Researcher 12 26%
Student > Master 5 11%
Student > Bachelor 5 11%
Student > Doctoral Student 3 6%
Other 5 11%
Unknown 3 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 34%
Computer Science 15 32%
Biochemistry, Genetics and Molecular Biology 10 21%
Mathematics 1 2%
Environmental Science 1 2%
Other 1 2%
Unknown 3 6%

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 21 September 2017.
All research outputs
#3,123,560
of 11,805,285 outputs
Outputs from Algorithms for Molecular Biology
#40
of 180 outputs
Outputs of similar age
#86,318
of 264,903 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 6 outputs
Altmetric has tracked 11,805,285 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 180 research outputs from this source. They receive a mean Attention Score of 2.7. 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 264,903 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 67% of its contemporaries.
We're also able to compare this research output to 6 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