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CAMSA: a tool for comparative analysis and merging of scaffold assemblies

Overview of attention for article published in BMC Bioinformatics, December 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
CAMSA: a tool for comparative analysis and merging of scaffold assemblies
Published in
BMC Bioinformatics, December 2017
DOI 10.1186/s12859-017-1919-y
Pubmed ID
Authors

Sergey S. Aganezov, Max A. Alekseyev

Abstract

Despite the recent progress in genome sequencing and assembly, many of the currently available assembled genomes come in a draft form. Such draft genomes consist of a large number of genomic fragments (scaffolds), whose positions and orientations along the genome are unknown. While there exists a number of methods for reconstruction of the genome from its scaffolds, utilizing various computational and wet-lab techniques, they often can produce only partial error-prone scaffold assemblies. It therefore becomes important to compare and merge scaffold assemblies produced by different methods, thus combining their advantages and highlighting present conflicts for further investigation. These tasks may be labor intensive if performed manually. We present CAMSA-a tool for comparative analysis and merging of two or more given scaffold assemblies. The tool (i) creates an extensive report with several comparative quality metrics; (ii) constructs the most confident merged scaffold assembly; and (iii) provides an interactive framework for a visual comparative analysis of the given assemblies. Among the CAMSA features, only scaffold merging can be evaluated in comparison to existing methods. Namely, it resembles the functionality of assembly reconciliation tools, although their primary targets are somewhat different. Our evaluations show that CAMSA produces merged assemblies of comparable or better quality than existing assembly reconciliation tools while being the fastest in terms of the total running time. CAMSA addresses the current deficiency of tools for automated comparison and analysis of multiple assemblies of the same set scaffolds. Since there exist numerous methods and techniques for scaffold assembly, identifying similarities and dissimilarities across assemblies produced by different methods is beneficial both for the developers of scaffold assembly algorithms and for the researchers focused on improving draft assemblies of specific organisms.

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X Demographics

The data shown below were collected from the profiles of 11 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Netherlands 1 1%
Unknown 71 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 26%
Student > Ph. D. Student 15 21%
Student > Bachelor 6 8%
Student > Master 6 8%
Student > Doctoral Student 4 5%
Other 14 19%
Unknown 9 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 40%
Biochemistry, Genetics and Molecular Biology 19 26%
Computer Science 8 11%
Chemical Engineering 2 3%
Immunology and Microbiology 1 1%
Other 1 1%
Unknown 13 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 23 April 2021.
All research outputs
#5,810,655
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#2,125
of 7,387 outputs
Outputs of similar age
#111,894
of 441,850 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 134 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,387 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 gotten more attention than average, scoring higher than 70% 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 441,850 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 74% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.