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Gerbil: a fast and memory-efficient k-mer counter with GPU-support

Overview of attention for article published in Algorithms for Molecular Biology, March 2017
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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 (#44 of 205)
  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
5 tweeters
wikipedia
1 Wikipedia page

Citations

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15 Dimensions

Readers on

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1 Mendeley
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Title
Gerbil: a fast and memory-efficient k-mer counter with GPU-support
Published in
Algorithms for Molecular Biology, March 2017
DOI 10.1186/s13015-017-0097-9
Pubmed ID
Authors

Marius Erbert, Steffen Rechner, Matthias Müller-Hannemann

Abstract

A basic task in bioinformatics is the counting of k-mers in genome sequences. Existing k-mer counting tools are most often optimized for small k < 32 and suffer from excessive memory resource consumption or degrading performance for large k. However, given the technology trend towards long reads of next-generation sequencers, support for large k becomes increasingly important. We present the open source k-mer counting software Gerbil that has been designed for the efficient counting of k-mers for k ≥ 32. Our software is the result of an intensive process of algorithm engineering. It implements a two-step approach. In the first step, genome reads are loaded from disk and redistributed to temporary files. In a second step, the k-mers of each temporary file are counted via a hash table approach. In addition to its basic functionality, Gerbil can optionally use GPUs to accelerate the counting step. In a set of experiments with real-world genome data sets, we show that Gerbil is able to efficiently support both small and large k. While Gerbil's performance is comparable to existing state-of-the-art open source k-mer counting tools for small k < 32, it vastly outperforms its competitors for large k, thereby enabling new applications which require large values of k.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 1000%
Researcher 4 400%
Student > Master 4 400%
Student > Bachelor 2 200%
Other 2 200%
Other 1 100%
Readers by discipline Count As %
Computer Science 11 1100%
Biochemistry, Genetics and Molecular Biology 5 500%
Engineering 3 300%
Agricultural and Biological Sciences 3 300%
Unspecified 1 100%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 28 June 2019.
All research outputs
#3,255,699
of 13,560,201 outputs
Outputs from Algorithms for Molecular Biology
#44
of 205 outputs
Outputs of similar age
#76,203
of 262,025 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 5 outputs
Altmetric has tracked 13,560,201 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 205 research outputs from this source. They receive a mean Attention Score of 3.0. This one has done well, scoring higher than 78% 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 262,025 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 70% of its contemporaries.
We're also able to compare this research output to 5 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