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Minimus: a fast, lightweight genome assembler

Overview of attention for article published in BMC Bioinformatics, February 2007
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

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3 X users
wikipedia
1 Wikipedia page

Citations

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407 Dimensions

Readers on

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374 Mendeley
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12 CiteULike
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6 Connotea
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Title
Minimus: a fast, lightweight genome assembler
Published in
BMC Bioinformatics, February 2007
DOI 10.1186/1471-2105-8-64
Pubmed ID
Authors

Daniel D Sommer, Arthur L Delcher, Steven L Salzberg, Mihai Pop

Abstract

Genome assemblers have grown very large and complex in response to the need for algorithms to handle the challenges of large whole-genome sequencing projects. Many of the most common uses of assemblers, however, are best served by a simpler type of assembler that requires fewer software components, uses less memory, and is far easier to install and run. We have developed the Minimus assembler to address these issues, and tested it on a range of assembly problems. We show that Minimus performs well on several small assembly tasks, including the assembly of viral genomes, individual genes, and BAC clones. In addition, we evaluate Minimus' performance in assembling bacterial genomes in order to assess its suitability as a component of a larger assembly pipeline. We show that, unlike other software currently used for these tasks, Minimus produces significantly fewer assembly errors, at the cost of generating a more fragmented assembly. We find that for small genomes and other small assembly tasks, Minimus is faster and far more flexible than existing tools. Due to its small size and modular design Minimus is perfectly suited to be a component of complex assembly pipelines. Minimus is released as an open-source software project and the code is available as part of the AMOS project at Sourceforge.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 374 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 5%
Brazil 8 2%
Germany 6 2%
Sweden 2 <1%
Mexico 2 <1%
United Kingdom 2 <1%
Canada 2 <1%
Spain 2 <1%
New Zealand 1 <1%
Other 4 1%
Unknown 328 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 102 27%
Student > Ph. D. Student 90 24%
Student > Master 33 9%
Professor > Associate Professor 29 8%
Student > Bachelor 26 7%
Other 65 17%
Unknown 29 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 214 57%
Biochemistry, Genetics and Molecular Biology 41 11%
Computer Science 34 9%
Environmental Science 13 3%
Immunology and Microbiology 8 2%
Other 21 6%
Unknown 43 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 April 2013.
All research outputs
#6,121,314
of 22,708,120 outputs
Outputs from BMC Bioinformatics
#2,323
of 7,256 outputs
Outputs of similar age
#23,770
of 75,969 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 37 outputs
Altmetric has tracked 22,708,120 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,256 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 gotten more attention than average, scoring higher than 67% 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 75,969 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 68% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.