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SMRT sequencing only de novo assembly of the sugar beet (Beta vulgaris) chloroplast genome

Overview of attention for article published in BMC Bioinformatics, September 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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Title
SMRT sequencing only de novo assembly of the sugar beet (Beta vulgaris) chloroplast genome
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0726-6
Pubmed ID
Authors

Kai Bernd Stadermann, Bernd Weisshaar, Daniela Holtgräwe

Abstract

Third generation sequencing methods, like SMRT (Single Molecule, Real-Time) sequencing developed by Pacific Biosciences, offer much longer read length in comparison to Next Generation Sequencing (NGS) methods. Hence, they are well suited for de novo- or re-sequencing projects. Sequences generated for these purposes will not only contain reads originating from the nuclear genome, but also a significant amount of reads originating from the organelles of the target organism. These reads are usually discarded but they can also be used for an assembly of organellar replicons. The long read length supports resolution of repetitive regions and repeats within the organelles genome which might be problematic when just using short read data. Additionally, SMRT sequencing is less influenced by GC rich areas and by long stretches of the same base. We describe a workflow for a de novo assembly of the sugar beet (Beta vulgaris ssp. vulgaris) chloroplast genome sequence only based on data originating from a SMRT sequencing dataset targeted on its nuclear genome. We show that the data obtained from such an experiment are sufficient to create a high quality assembly with a higher reliability than assemblies derived from e.g. Illumina reads only. The chloroplast genome is especially challenging for de novo assembling as it contains two large inverted repeat (IR) regions. We also describe some limitations that still apply even though long reads are used for the assembly. SMRT sequencing reads extracted from a dataset created for nuclear genome (re)sequencing can be used to obtain a high quality de novo assembly of the chloroplast of the sequenced organism. Even with a relatively small overall coverage for the nuclear genome it is possible to collect more than enough reads to generate a high quality assembly that outperforms short read based assemblies. However, even with long reads it is not always possible to clarify the order of elements of a chloroplast genome sequence reliantly which we could demonstrate with Fosmid End Sequences (FES) generated with Sanger technology. Nevertheless, this limitation also applies to short read sequencing data but is reached in this case at a much earlier stage during finishing.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Japan 1 2%
Austria 1 2%
Unknown 37 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Student > Bachelor 6 15%
Student > Ph. D. Student 5 12%
Student > Master 4 10%
Student > Doctoral Student 3 7%
Other 11 27%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 51%
Biochemistry, Genetics and Molecular Biology 6 15%
Computer Science 5 12%
Engineering 2 5%
Chemistry 2 5%
Other 2 5%
Unknown 3 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 22 September 2015.
All research outputs
#4,582,476
of 22,828,180 outputs
Outputs from BMC Bioinformatics
#1,744
of 7,287 outputs
Outputs of similar age
#54,757
of 245,084 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 128 outputs
Altmetric has tracked 22,828,180 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,287 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 done well, scoring higher than 75% 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 245,084 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 77% of its contemporaries.
We're also able to compare this research output to 128 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.