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Additive methods for genomic signatures

Overview of attention for article published in BMC Bioinformatics, August 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

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7 Dimensions

Readers on

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19 Mendeley
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Title
Additive methods for genomic signatures
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1157-8
Pubmed ID
Authors

Rallis Karamichalis, Lila Kari, Stavros Konstantinidis, Steffen Kopecki, Stephen Solis-Reyes

Abstract

Studies exploring the potential of Chaos Game Representations (CGR) of genomic sequences to act as "genomic signatures" (to be species- and genome-specific) showed that CGR patterns of nuclear and organellar DNA sequences of the same organism can be very different. While the hypothesis that CGRs of mitochondrial DNA sequences can act as genomic signatures was validated for a snapshot of all sequenced mitochondrial genomes available in the NCBI GenBank sequence database, to our knowledge no such extensive analysis of CGRs of nuclear DNA sequences exists to date. We analyzed an extensive dataset, totalling 1.45 gigabase pairs, of nuclear/nucleoid genomic sequences (nDNA) from 42 different organisms, spanning all major kingdoms of life. Our computational experiments indicate that CGR signatures of nDNA of two different origins cannot always be differentiated, especially if they originate from closely-related species such as H. sapiens and P. troglodytes or E. coli and E. fergusonii. To address this issue, we propose the general concept of additive DNA signature of a set (collection) of DNA sequences. One particular instance, the composite DNA signature, combines information from nDNA fragments and organellar (mitochondrial, chloroplast, or plasmid) genomes. We demonstrate that, in this dataset, composite DNA signatures originating from two different organisms can be differentiated in all cases, including those where the use of CGR signatures of nDNA failed or was inconclusive. Another instance, the assembled DNA signature, combines information from many short DNA subfragments (e.g., 100 basepairs) of a given DNA fragment, to produce its signature. We show that an assembled DNA signature has the same distinguishing power as a conventionally computed CGR signature, while using shorter contiguous sequences and potentially less sequence information. Our results suggest that, while CGR signatures of nDNA cannot always play the role of genomic signatures, composite and assembled DNA signatures (separately or in combination) could potentially be used instead. Such additive signatures could be used, e.g., with raw unassembled next-generation sequencing (NGS) read data, when high-quality sequencing data is not available, or to complement information obtained by other methods of species identification or classification.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 5%
France 1 5%
Unknown 17 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Student > Bachelor 4 21%
Professor > Associate Professor 2 11%
Researcher 2 11%
Other 1 5%
Other 2 11%
Unknown 3 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 32%
Biochemistry, Genetics and Molecular Biology 5 26%
Computer Science 1 5%
Social Sciences 1 5%
Medicine and Dentistry 1 5%
Other 0 0%
Unknown 5 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 17 December 2017.
All research outputs
#1,790,670
of 12,306,918 outputs
Outputs from BMC Bioinformatics
#779
of 4,482 outputs
Outputs of similar age
#52,540
of 265,128 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 132 outputs
Altmetric has tracked 12,306,918 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,482 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 82% 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 265,128 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 80% of its contemporaries.
We're also able to compare this research output to 132 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.