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Odintifier - A computational method for identifying insertions of organellar origin from modern and ancient high-throughput sequencing data based on haplotype phasing

Overview of attention for article published in BMC Bioinformatics, July 2015
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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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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1 news outlet
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12 X users

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Title
Odintifier - A computational method for identifying insertions of organellar origin from modern and ancient high-throughput sequencing data based on haplotype phasing
Published in
BMC Bioinformatics, July 2015
DOI 10.1186/s12859-015-0682-1
Pubmed ID
Authors

Jose Alfredo Samaniego Castruita, Marie Lisandra Zepeda Mendoza, Ross Barnett, Nathan Wales, M Thomas P. Gilbert

Abstract

Cellular organelles with genomes of their own (e.g. plastids and mitochondria) can pass genetic sequences to other organellar genomes within the cell in many species across the eukaryote phylogeny. The extent of the occurrence of these organellar-derived inserted sequences (odins) is still unknown, but if not accounted for in genomic and phylogenetic studies, they can be a source of error. However, if correctly identified, these inserted sequences can be used for evolutionary and comparative genomic studies. Although such insertions can be detected using various laboratory and bioinformatic strategies, there is currently no straightforward way to apply them as a standard organellar genome assembly on next-generation sequencing data. Furthermore, most current methods for identification of such insertions are unsuitable for use on non-model organisms or ancient DNA datasets. We present a bioinformatic method that uses phasing algorithms to reconstruct both source and inserted organelle sequences. The method was tested in different shotgun and organellar-enriched DNA high-throughput sequencing (HTS) datasets from ancient and modern samples. Specifically, we used datasets from lions (Panthera leo ssp. and Panthera leo leo) to characterize insertions from mitochondrial origin, and from common grapevine (Vitis vinifera) and bugle (Ajuga reptans) to characterize insertions derived from plastid genomes. Comparison of the results against other available organelle genome assembly methods demonstrated that our new method provides an improvement in the sequence assembly. Using datasets from a wide range of species and different levels of complexity we showed that our novel bioinformatic method based on phasing algorithms can be used to achieve the next two goals: i) reference-guided assembly of chloroplast/mitochondrial genomes from HTS data and ii) identification and simultaneous assembly of odins. This method represents the first application of haplotype phasing for automatic detection of odins and reference-based organellar genome assembly.

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X Demographics

The data shown below were collected from the profiles of 12 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
France 1 3%
Unknown 36 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 32%
Student > Bachelor 5 13%
Student > Ph. D. Student 5 13%
Student > Master 4 11%
Other 2 5%
Other 7 18%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 42%
Biochemistry, Genetics and Molecular Biology 8 21%
Computer Science 4 11%
Engineering 3 8%
Nursing and Health Professions 1 3%
Other 3 8%
Unknown 3 8%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 May 2016.
All research outputs
#2,317,153
of 24,862,067 outputs
Outputs from BMC Bioinformatics
#570
of 7,597 outputs
Outputs of similar age
#29,072
of 268,792 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 108 outputs
Altmetric has tracked 24,862,067 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,597 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 268,792 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 108 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 91% of its contemporaries.