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seq-seq-pan: building a computational pan-genome data structure on whole genome alignment

Overview of attention for article published in BMC Genomics, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

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27 X users

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124 Mendeley
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Title
seq-seq-pan: building a computational pan-genome data structure on whole genome alignment
Published in
BMC Genomics, January 2018
DOI 10.1186/s12864-017-4401-3
Pubmed ID
Authors

Christine Jandrasits, Piotr W. Dabrowski, Stephan Fuchs, Bernhard Y. Renard

Abstract

The increasing application of next generation sequencing technologies has led to the availability of thousands of reference genomes, often providing multiple genomes for the same or closely related species. The current approach to represent a species or a population with a single reference sequence and a set of variations cannot represent their full diversity and introduces bias towards the chosen reference. There is a need for the representation of multiple sequences in a composite way that is compatible with existing data sources for annotation and suitable for established sequence analysis methods. At the same time, this representation needs to be easily accessible and extendable to account for the constant change of available genomes. We introduce seq-seq-pan, a framework that provides methods for adding or removing new genomes from a set of aligned genomes and uses these to construct a whole genome alignment. Throughout the sequential workflow the alignment is optimized for generating a representative linear presentation of the aligned set of genomes, that enables its usage for annotation and in downstream analyses. By providing dynamic updates and optimized processing, our approach enables the usage of whole genome alignment in the field of pan-genomics. In addition, the sequential workflow can be used as a fast alternative to existing whole genome aligners for aligning closely related genomes. seq-seq-pan is freely available at https://gitlab.com/rki_bioinformatics.

X Demographics

X Demographics

The data shown below were collected from the profiles of 27 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 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 124 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 18%
Student > Ph. D. Student 21 17%
Student > Master 19 15%
Student > Bachelor 13 10%
Student > Doctoral Student 8 6%
Other 17 14%
Unknown 24 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 33%
Biochemistry, Genetics and Molecular Biology 29 23%
Computer Science 14 11%
Engineering 4 3%
Chemical Engineering 3 2%
Other 7 6%
Unknown 26 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 19 September 2018.
All research outputs
#2,455,701
of 23,577,654 outputs
Outputs from BMC Genomics
#753
of 10,777 outputs
Outputs of similar age
#62,697
of 476,553 outputs
Outputs of similar age from BMC Genomics
#21
of 217 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,777 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 92% 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 476,553 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 86% 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 particularly well, scoring higher than 90% of its contemporaries.