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Identifying wrong assemblies in de novo short read primary sequence assembly contigs

Overview of attention for article published in Proceedings: Plant Sciences, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#28 of 975)
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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1 blog
twitter
16 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Readers on

mendeley
32 Mendeley
citeulike
1 CiteULike
Title
Identifying wrong assemblies in de novo short read primary sequence assembly contigs
Published in
Proceedings: Plant Sciences, August 2016
DOI 10.1007/s12038-016-9630-0
Pubmed ID
Authors

Vandna Chawla, Rajnish Kumar, Ravi Shankar

Abstract

With the advent of short-reads-based genome sequencing approaches, large number of organisms are being sequenced all over the world. Most of these assemblies are done using some de novo short read assemblers and other related approaches. However, the contigs produced this way are prone to wrong assembly. So far, there is a conspicuous dearth of reliable tools to identify mis-assembled contigs. Mis-assemblies could result from incorrectly deleted or wrongly arranged genomic sequences. In the present work various factors related to sequence, sequencing and assembling have been assessed for their role in causing mis-assembly by using different genome sequencing data. Finally, some mis-assembly detecting tools have been evaluated for their ability to detect the wrongly assembled primary contigs, suggesting a lot of scope for improvement in this area. The present work also proposes a simple unsupervised learning-based novel approach to identify mis-assemblies in the contigs which was found performing reasonably well when compared to the already existing tools to report mis-assembled contigs. It was observed that the proposed methodology may work as a complementary system to the existing tools to enhance their accuracy.

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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
Norway 1 3%
Unknown 30 94%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 31%
Researcher 7 22%
Lecturer 2 6%
Student > Bachelor 2 6%
Student > Ph. D. Student 2 6%
Other 3 9%
Unknown 6 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 34%
Biochemistry, Genetics and Molecular Biology 9 28%
Immunology and Microbiology 2 6%
Computer Science 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 7 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 03 September 2016.
All research outputs
#2,194,818
of 25,374,647 outputs
Outputs from Proceedings: Plant Sciences
#28
of 975 outputs
Outputs of similar age
#40,047
of 381,577 outputs
Outputs of similar age from Proceedings: Plant Sciences
#1
of 9 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 975 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done particularly well, scoring higher than 97% 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 381,577 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 89% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them