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Additive methods for genomic signatures
by
Kopecki, Steffen
, Karamichalis, Rallis
, Solis-Reyes, Stephen
, Kari, Lila
, Konstantinidis, Stavros
in
Algorithms
/ Animals
/ Bacteria - classification
/ Bacteria - genetics
/ Bioinformatics
/ Biomedical and Life Sciences
/ Comparative genomics
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ DNA, Bacterial - genetics
/ DNA, Mitochondrial - genetics
/ Genome, Bacterial
/ Genomics
/ Genomics - methods
/ High-Throughput Nucleotide Sequencing
/ Humans
/ Information management
/ Life Sciences
/ Microarrays
/ Research Article
2016
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Additive methods for genomic signatures
by
Kopecki, Steffen
, Karamichalis, Rallis
, Solis-Reyes, Stephen
, Kari, Lila
, Konstantinidis, Stavros
in
Algorithms
/ Animals
/ Bacteria - classification
/ Bacteria - genetics
/ Bioinformatics
/ Biomedical and Life Sciences
/ Comparative genomics
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ DNA, Bacterial - genetics
/ DNA, Mitochondrial - genetics
/ Genome, Bacterial
/ Genomics
/ Genomics - methods
/ High-Throughput Nucleotide Sequencing
/ Humans
/ Information management
/ Life Sciences
/ Microarrays
/ Research Article
2016
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Additive methods for genomic signatures
by
Kopecki, Steffen
, Karamichalis, Rallis
, Solis-Reyes, Stephen
, Kari, Lila
, Konstantinidis, Stavros
in
Algorithms
/ Animals
/ Bacteria - classification
/ Bacteria - genetics
/ Bioinformatics
/ Biomedical and Life Sciences
/ Comparative genomics
/ Computational Biology/Bioinformatics
/ Computer Appl. in Life Sciences
/ DNA, Bacterial - genetics
/ DNA, Mitochondrial - genetics
/ Genome, Bacterial
/ Genomics
/ Genomics - methods
/ High-Throughput Nucleotide Sequencing
/ Humans
/ Information management
/ Life Sciences
/ Microarrays
/ Research Article
2016
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Journal Article
Additive methods for genomic signatures
2016
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Overview
Background
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.
Results
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.
Conclusions
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.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V
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