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Improvement of the banana “Musa acuminata” reference sequence using NGS data and semi-automated bioinformatics methods

Overview of attention for article published in BMC Genomics, March 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
10 tweeters

Citations

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111 Dimensions

Readers on

mendeley
126 Mendeley
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3 CiteULike
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Title
Improvement of the banana “Musa acuminata” reference sequence using NGS data and semi-automated bioinformatics methods
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2579-4
Pubmed ID
Authors

Guillaume Martin, Franc-Christophe Baurens, Gaëtan Droc, Mathieu Rouard, Alberto Cenci, Andrzej Kilian, Alex Hastie, Jaroslav Doležel, Jean-Marc Aury, Adriana Alberti, Françoise Carreel, Angélique D’Hont

Abstract

Recent advances in genomics indicate functional significance of a majority of genome sequences and their long range interactions. As a detailed examination of genome organization and function requires very high quality genome sequence, the objective of this study was to improve reference genome assembly of banana (Musa acuminata). We have developed a modular bioinformatics pipeline to improve genome sequence assemblies, which can handle various types of data. The pipeline comprises several semi-automated tools. However, unlike classical automated tools that are based on global parameters, the semi-automated tools proposed an expert mode for a user who can decide on suggested improvements through local compromises. The pipeline was used to improve the draft genome sequence of Musa acuminata. Genotyping by sequencing (GBS) of a segregating population and paired-end sequencing were used to detect and correct scaffold misassemblies. Long insert size paired-end reads identified scaffold junctions and fusions missed by automated assembly methods. GBS markers were used to anchor scaffolds to pseudo-molecules with a new bioinformatics approach that avoids the tedious step of marker ordering during genetic map construction. Furthermore, a genome map was constructed and used to assemble scaffolds into super scaffolds. Finally, a consensus gene annotation was projected on the new assembly from two pre-existing annotations. This approach reduced the total Musa scaffold number from 7513 to 1532 (i.e. by 80 %), with an N50 that increased from 1.3 Mb (65 scaffolds) to 3.0 Mb (26 scaffolds). 89.5 % of the assembly was anchored to the 11 Musa chromosomes compared to the previous 70 %. Unknown sites (N) were reduced from 17.3 to 10.0 %. The release of the Musa acuminata reference genome version 2 provides a platform for detailed analysis of banana genome variation, function and evolution. Bioinformatics tools developed in this work can be used to improve genome sequence assemblies in other species.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Chile 1 <1%
Switzerland 1 <1%
Unknown 124 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 21%
Researcher 24 19%
Student > Master 17 13%
Student > Bachelor 15 12%
Student > Doctoral Student 8 6%
Other 19 15%
Unknown 16 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 46%
Biochemistry, Genetics and Molecular Biology 30 24%
Chemistry 3 2%
Environmental Science 2 2%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 9 7%
Unknown 22 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 15 November 2016.
All research outputs
#5,592,267
of 22,856,968 outputs
Outputs from BMC Genomics
#2,269
of 10,661 outputs
Outputs of similar age
#78,780
of 300,005 outputs
Outputs of similar age from BMC Genomics
#48
of 217 outputs
Altmetric has tracked 22,856,968 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,661 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 78% 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 300,005 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 73% of its contemporaries.
We're also able to compare this research output to 217 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.