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Sealer: a scalable gap-closing application for finishing draft genomes

Overview of attention for article published in BMC Bioinformatics, July 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 (92nd percentile)

Mentioned by

blogs
1 blog
twitter
30 tweeters
facebook
1 Facebook page

Citations

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67 Dimensions

Readers on

mendeley
132 Mendeley
citeulike
1 CiteULike
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Title
Sealer: a scalable gap-closing application for finishing draft genomes
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0663-4
Pubmed ID
Authors

Daniel Paulino, René L. Warren, Benjamin P. Vandervalk, Anthony Raymond, Shaun D. Jackman, Inanç Birol

Abstract

While next-generation sequencing technologies have made sequencing genomes faster and more affordable, deciphering the complete genome sequence of an organism remains a significant bioinformatics challenge, especially for large genomes. Low sequence coverage, repetitive elements and short read length make de novo genome assembly difficult, often resulting in sequence and/or fragment "gaps" - uncharacterized nucleotide (N) stretches of unknown or estimated lengths. Some of these gaps can be closed by re-processing latent information in the raw reads. Even though there are several tools for closing gaps, they do not easily scale up to processing billion base pair genomes. Here we describe Sealer, a tool designed to close gaps within assembly scaffolds by navigating de Bruijn graphs represented by space-efficient Bloom filter data structures. We demonstrate how it scales to successfully close 50.8 % and 13.8 % of gaps in human (3 Gbp) and white spruce (20 Gbp) draft assemblies in under 30 and 27 h, respectively - a feat that is not possible with other leading tools with the breadth of data used in our study. Sealer is an automated finishing application that uses the succinct Bloom filter representation of a de Bruijn graph to close gaps in draft assemblies, including that of very large genomes. We expect Sealer to have broad utility for finishing genomes across the tree of life, from bacterial genomes to large plant genomes and beyond. Sealer is available for download at https://github.com/bcgsc/abyss/tree/sealer-release .

Twitter Demographics

The data shown below were collected from the profiles of 30 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 132 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
Taiwan 2 2%
Brazil 1 <1%
France 1 <1%
Norway 1 <1%
Korea, Republic of 1 <1%
Netherlands 1 <1%
Sweden 1 <1%
Israel 1 <1%
Other 4 3%
Unknown 116 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 41 31%
Student > Ph. D. Student 27 20%
Student > Master 14 11%
Student > Bachelor 11 8%
Other 9 7%
Other 21 16%
Unknown 9 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 65 49%
Biochemistry, Genetics and Molecular Biology 31 23%
Computer Science 9 7%
Immunology and Microbiology 6 5%
Environmental Science 2 2%
Other 5 4%
Unknown 14 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 09 November 2019.
All research outputs
#874,861
of 14,983,162 outputs
Outputs from BMC Bioinformatics
#193
of 5,534 outputs
Outputs of similar age
#17,405
of 235,502 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 2 outputs
Altmetric has tracked 14,983,162 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,534 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done particularly well, scoring higher than 96% 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 235,502 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 2 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