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De novo assembly of Dekkera bruxellensis: a multi technology approach using short and long-read sequencing and optical mapping

Overview of attention for article published in Giga Science, November 2015
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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 (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

blogs
1 blog
twitter
16 X users
peer_reviews
1 peer review site
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
75 Mendeley
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Title
De novo assembly of Dekkera bruxellensis: a multi technology approach using short and long-read sequencing and optical mapping
Published in
Giga Science, November 2015
DOI 10.1186/s13742-015-0094-1
Pubmed ID
Authors

Remi-Andre Olsen, Ignas Bunikis, Ievgeniia Tiukova, Kicki Holmberg, Britta Lötstedt, Olga Vinnere Pettersson, Volkmar Passoth, Max Käller, Francesco Vezzi

Abstract

It remains a challenge to perform de novo assembly using next-generation sequencing (NGS). Despite the availability of multiple sequencing technologies and tools (e.g., assemblers) it is still difficult to assemble new genomes at chromosome resolution (i.e., one sequence per chromosome). Obtaining high quality draft assemblies is extremely important in the case of yeast genomes to better characterise major events in their evolutionary history. The aim of this work is two-fold: on the one hand we want to show how combining different and somewhat complementary technologies is key to improving assembly quality and correctness, and on the other hand we present a de novo assembly pipeline we believe to be beneficial to core facility bioinformaticians. To demonstrate both the effectiveness of combining technologies and the simplicity of the pipeline, here we present the results obtained using the Dekkera bruxellensis genome. In this work we used short-read Illumina data and long-read PacBio data combined with the extreme long-range information from OpGen optical maps in the task of de novo genome assembly and finishing. Moreover, we developed NouGAT, a semi-automated pipeline for read-preprocessing, de novo assembly and assembly evaluation, which was instrumental for this work. We obtained a high quality draft assembly of a yeast genome, resolved on a chromosomal level. Furthermore, this assembly was corrected for mis-assembly errors as demonstrated by resolving a large collapsed repeat and by receiving higher scores by assembly evaluation tools. With the inclusion of PacBio data we were able to fill about 5 % of the optical mapped genome not covered by the Illumina data.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Spain 2 3%
Brazil 1 1%
Norway 1 1%
Singapore 1 1%
Unknown 68 91%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 19%
Researcher 13 17%
Student > Ph. D. Student 11 15%
Student > Bachelor 9 12%
Student > Doctoral Student 6 8%
Other 15 20%
Unknown 7 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 40%
Biochemistry, Genetics and Molecular Biology 19 25%
Computer Science 6 8%
Engineering 3 4%
Chemistry 3 4%
Other 4 5%
Unknown 10 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 28 January 2016.
All research outputs
#1,928,810
of 25,965,655 outputs
Outputs from Giga Science
#356
of 1,184 outputs
Outputs of similar age
#30,619
of 396,146 outputs
Outputs of similar age from Giga Science
#7
of 19 outputs
Altmetric has tracked 25,965,655 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 1,184 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.5. This one has gotten more attention than average, scoring higher than 69% 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 396,146 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 92% of its contemporaries.
We're also able to compare this research output to 19 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 63% of its contemporaries.