Evidence map›Paper›PMID 27549194›Full record

ArticleBMC bioinformatics2016

Additive methods for genomic signatures.

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

Abstract readEvaluation Study
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
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  6. Microbial characterization based on multifractal analysis of metagenomes.Frontiers in cellular and infection microbiology · 2023
    Article
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  11. Differential Expression of theFrontiers in cell and developmental biology · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Rallis KaramichalisDepartment of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.
Lila KariSchool of Computing Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada. lila.kari@uwaterloo.ca.
Stavros KonstantinidisDepartment of Mathematics and Computing Science, Saint Mary's University, Halifax NS, Canada.
Steffen KopeckiDepartment of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.
Stephen Solis-ReyesDepartment of Computer Science, University of Western Ontario, London ON, N6A 5B7, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStudies 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.

resultsWe 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.

conclusionsOur 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.

Indexed as

Genome, BacterialAnimalsBacteriaDNA, BacterialDNA, MitochondrialGenomicsHigh-Throughput Nucleotide SequencingHumansDNA, BacterialDNA, MitochondrialAdditive DNA signatureAlignment-freeAssembled DNA signatureChaos Game RepresentationComparative genomicsComposite DNA signatureGenomic signatureInformation distance

Identifiers

PMID27549194
PMCPMC4994249

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.