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BioNanoAnalyst: a visualisation tool to assess genome assembly quality using BioNano data

Overview of attention for article published in BMC Bioinformatics, June 2017
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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28 X users

Citations

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9 Dimensions

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34 Mendeley
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Title
BioNanoAnalyst: a visualisation tool to assess genome assembly quality using BioNano data
Published in
BMC Bioinformatics, June 2017
DOI 10.1186/s12859-017-1735-4
Pubmed ID
Authors

Yuxuan Yuan, Philipp E. Bayer, Armin Scheben, Chon-Kit Kenneth Chan, David Edwards

Abstract

Reference genome assemblies are valuable, as they provide insights into gene content, genetic evolution and domestication. The higher the quality of a reference genome assembly the more accurate the downstream analysis will be. During the last few years, major efforts have been made towards improving the quality of genome assemblies. However, erroneous and incomplete assemblies are still common. Complementary to DNA sequencing technologies, optical mapping has advanced genomic studies by facilitating the production of genome scaffolds and assessing structural variation. However, there are few tools available to comprehensively examine misassemblies in reference genome sequences using optical map data. We present BioNanoAnalyst, a software package to examine genome assemblies based on restriction endonuclease cut sites and optical map data. A graphical user interface (GUI) allows users to assess reference genome sequences on different computer platforms without the requirement of programming knowledge. The zoom function makes visualisation convenient, while a GFF3 format output file gives an option to directly visualise questionable assembly regions by location and nucleotides following import into a local genome browser. BioNanoAnalyst is a tool to identify misassemblies in a reference genome sequence using optical map data. With the reported information, users can rapidly identify assembly errors and correct them using other software tools, which could facilitate an accurate downstream analysis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 28 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Master 6 18%
Student > Ph. D. Student 6 18%
Student > Bachelor 3 9%
Student > Doctoral Student 3 9%
Other 3 9%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 38%
Biochemistry, Genetics and Molecular Biology 8 24%
Computer Science 7 21%
Engineering 2 6%
Nursing and Health Professions 1 3%
Other 0 0%
Unknown 3 9%
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 24 July 2017.
All research outputs
#2,347,380
of 24,093,053 outputs
Outputs from BMC Bioinformatics
#596
of 7,500 outputs
Outputs of similar age
#44,668
of 318,039 outputs
Outputs of similar age from BMC Bioinformatics
#9
of 111 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,500 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 92% 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 318,039 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 85% of its contemporaries.
We're also able to compare this research output to 111 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 92% of its contemporaries.