↓ Skip to main content

WiseScaffolder: an algorithm for the semi-automatic scaffolding of Next Generation Sequencing data

Overview of attention for article published in BMC Bioinformatics, September 2015
Altmetric Badge

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 (88th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

blogs
1 blog
twitter
13 X users

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
57 Mendeley
citeulike
4 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
WiseScaffolder: an algorithm for the semi-automatic scaffolding of Next Generation Sequencing data
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0705-y
Pubmed ID
Authors

Gregory K. Farrant, Mark Hoebeke, Frédéric Partensky, Gwendoline Andres, Erwan Corre, Laurence Garczarek

Abstract

The sequencing depth provided by high-throughput sequencing technologies has allowed a rise in the number of de novo sequenced genomes that could potentially be closed without further sequencing. However, genome scaffolding and closure require costly human supervision that often results in genomes being published as drafts. A number of automatic scaffolders were recently released, which improved the global quality of genomes published in the last few years. Yet, none of them reach the efficiency of manual scaffolding. Here, we present an innovative semi-automatic scaffolder that additionally helps with chimerae resolution and generates valuable contig maps and outputs for manual improvement of the automatic scaffolding. This software was tested on the newly sequenced marine cyanobacterium Synechococcus sp. WH8103 as well as two reference datasets used in previous studies, Rhodobacter sphaeroides and Homo sapiens chromosome 14 ( http://gage.cbcb.umd.edu/ ). The quality of resulting scaffolds was compared to that of three other stand-alone scaffolders: SSPACE, SOPRA and SCARPA. For all three model organisms, WiseScaffolder produced better results than other scaffolders in terms of contiguity statistics (number of genome fragments, N50, LG50, etc.) and, in the case of WH8103, the reliability of the scaffolds was confirmed by whole genome alignment against a closely related reference genome. We also propose an efficient computer-assisted strategy for manual improvement of the scaffolding, using outputs generated by WiseScaffolder, as well as for genome finishing that in our hands led to the circularization of the WH8103 genome. Altogether, WiseScaffolder proved more efficient than three other scaffolders for both prokaryotic and eukaryotic genomes and is thus likely applicable to most genome projects. The scaffolding pipeline described here should be of particular interest to biologists wishing to take advantage of the high added value of complete genomes.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 4%
Germany 1 2%
Netherlands 1 2%
Korea, Republic of 1 2%
Sweden 1 2%
United States 1 2%
Unknown 50 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 33%
Student > Ph. D. Student 12 21%
Student > Doctoral Student 4 7%
Student > Bachelor 4 7%
Professor 4 7%
Other 8 14%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 49%
Computer Science 8 14%
Biochemistry, Genetics and Molecular Biology 5 9%
Engineering 3 5%
Social Sciences 2 4%
Other 2 4%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 15 February 2016.
All research outputs
#2,614,845
of 25,706,302 outputs
Outputs from BMC Bioinformatics
#666
of 7,735 outputs
Outputs of similar age
#33,038
of 277,715 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 123 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,735 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 91% 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 277,715 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 88% of its contemporaries.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.