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MaGuS: a tool for quality assessment and scaffolding of genome assemblies with Whole Genome Profiling™ Data

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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Title
MaGuS: a tool for quality assessment and scaffolding of genome assemblies with Whole Genome Profiling™ Data
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0969-x
Pubmed ID
Authors

Mohammed-Amin Madoui, Carole Dossat, Léo d’Agata, Jan van Oeveren, Edwin van der Vossen, Jean-Marc Aury

Abstract

Scaffolding is an essential step in the genome assembly process. Current methods based on large fragment paired-end reads or long reads allow an increase in contiguity but often lack consistency in repetitive regions, resulting in fragmented assemblies. Here, we describe a novel tool to link assemblies to a genome map to aid complex genome reconstruction by detecting assembly errors and allowing scaffold ordering and anchoring. We present MaGuS (map-guided scaffolding), a modular tool that uses a draft genome assembly, a Whole Genome Profiling™ (WGP) map, and high-throughput paired-end sequencing data to estimate the quality and to enhance the contiguity of an assembly. We generated several assemblies of the Arabidopsis genome using different scaffolding programs and applied MaGuS to select the best assembly using quality metrics. Then, we used MaGuS to perform map-guided scaffolding to increase contiguity by creating new scaffold links in low-covered and highly repetitive regions where other commonly used scaffolding methods lack consistency. MaGuS is a powerful reference-free evaluator of assembly quality and a WGP map-guided scaffolder that is freely available at https://github.com/institut-de-genomique/MaGuS . Its use can be extended to other high-throughput sequencing data (e.g., long-read data) and also to other map data (e.g., genetic maps) to improve the quality and the contiguity of large and complex genome assemblies.

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X Demographics

The data shown below were collected from the profiles of 22 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Korea, Republic of 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 42%
Student > Ph. D. Student 8 22%
Student > Bachelor 2 6%
Student > Postgraduate 2 6%
Professor > Associate Professor 2 6%
Other 5 14%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 42%
Biochemistry, Genetics and Molecular Biology 13 36%
Computer Science 3 8%
Immunology and Microbiology 1 3%
Earth and Planetary Sciences 1 3%
Other 0 0%
Unknown 3 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 10 August 2016.
All research outputs
#1,686,228
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#354
of 7,418 outputs
Outputs of similar age
#28,609
of 300,317 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 128 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 95% 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 300,317 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 90% of its contemporaries.
We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.