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Reference-guided de novo assembly approach improves genome reconstruction for related species

Overview of attention for article published in BMC Bioinformatics, November 2017
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  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

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8 tweeters

Citations

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27 Dimensions

Readers on

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292 Mendeley
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Title
Reference-guided de novo assembly approach improves genome reconstruction for related species
Published in
BMC Bioinformatics, November 2017
DOI 10.1186/s12859-017-1911-6
Pubmed ID
Authors

Heidi E. L. Lischer, Kentaro K. Shimizu

Abstract

The development of next-generation sequencing has made it possible to sequence whole genomes at a relatively low cost. However, de novo genome assemblies remain challenging due to short read length, missing data, repetitive regions, polymorphisms and sequencing errors. As more and more genomes are sequenced, reference-guided assembly approaches can be used to assist the assembly process. However, previous methods mostly focused on the assembly of other genotypes within the same species. We adapted and extended a reference-guided de novo assembly approach, which enables the usage of a related reference sequence to guide the genome assembly. In order to compare and evaluate de novo and our reference-guided de novo assembly approaches, we used a simulated data set of a repetitive and heterozygotic plant genome. The extended reference-guided de novo assembly approach almost always outperforms the corresponding de novo assembly program even when a reference of a different species is used. Similar improvements can be observed in high and low coverage situations. In addition, we show that a single evaluation metric, like the widely used N50 length, is not enough to properly rate assemblies as it not always points to the best assembly evaluated with other criteria. Therefore, we used the summed z-scores of 36 different statistics to evaluate the assemblies. The combination of reference mapping and de novo assembly provides a powerful tool to improve genome reconstruction by integrating information of a related genome. Our extension of the reference-guided de novo assembly approach enables the application of this strategy not only within but also between related species. Finally, the evaluation of genome assemblies is often not straight forward, as the truth is not known. Thus one should always use a combination of evaluation metrics, which not only try to assess the continuity but also the accuracy of an assembly.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 292 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 292 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 67 23%
Researcher 50 17%
Student > Bachelor 47 16%
Student > Master 38 13%
Student > Doctoral Student 20 7%
Other 28 10%
Unknown 42 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 105 36%
Biochemistry, Genetics and Molecular Biology 101 35%
Computer Science 10 3%
Immunology and Microbiology 6 2%
Environmental Science 4 1%
Other 17 6%
Unknown 49 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 10 February 2018.
All research outputs
#5,289,916
of 16,575,518 outputs
Outputs from BMC Bioinformatics
#2,334
of 5,983 outputs
Outputs of similar age
#120,251
of 325,119 outputs
Outputs of similar age from BMC Bioinformatics
#166
of 461 outputs
Altmetric has tracked 16,575,518 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,983 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 52% 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 325,119 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 60% of its contemporaries.
We're also able to compare this research output to 461 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.