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DNA Barcode Sequence Identification Incorporating Taxonomic Hierarchy and within Taxon Variability

Overview of attention for article published in PLOS ONE, August 2011
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Title
DNA Barcode Sequence Identification Incorporating Taxonomic Hierarchy and within Taxon Variability
Published in
PLOS ONE, August 2011
DOI 10.1371/journal.pone.0020552
Pubmed ID
Authors

Damon P. Little

Abstract

For DNA barcoding to succeed as a scientific endeavor an accurate and expeditious query sequence identification method is needed. Although a global multiple-sequence alignment can be generated for some barcoding markers (e.g. COI, rbcL), not all barcoding markers are as structurally conserved (e.g. matK). Thus, algorithms that depend on global multiple-sequence alignments are not universally applicable. Some sequence identification methods that use local pairwise alignments (e.g. BLAST) are unable to accurately differentiate between highly similar sequences and are not designed to cope with hierarchic phylogenetic relationships or within taxon variability. Here, I present a novel alignment-free sequence identification algorithm--BRONX--that accounts for observed within taxon variability and hierarchic relationships among taxa. BRONX identifies short variable segments and corresponding invariant flanking regions in reference sequences. These flanking regions are used to score variable regions in the query sequence without the production of a global multiple-sequence alignment. By incorporating observed within taxon variability into the scoring procedure, misidentifications arising from shared alleles/haplotypes are minimized. An explicit treatment of more inclusive terminals allows for separate identifications to be made for each taxonomic level and/or for user-defined terminals. BRONX performs better than all other methods when there is imperfect overlap between query and reference sequences (e.g. mini-barcode queries against a full-length barcode database). BRONX consistently produced better identifications at the genus-level for all query types.

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Geographical breakdown

Country Count As %
Germany 3 2%
United States 3 2%
India 2 1%
Australia 2 1%
Cuba 1 <1%
Italy 1 <1%
Netherlands 1 <1%
Sweden 1 <1%
France 1 <1%
Other 2 1%
Unknown 165 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 23%
Student > Ph. D. Student 40 22%
Student > Master 23 13%
Student > Bachelor 17 9%
Professor 10 5%
Other 35 19%
Unknown 15 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 117 64%
Biochemistry, Genetics and Molecular Biology 22 12%
Computer Science 7 4%
Environmental Science 6 3%
Medicine and Dentistry 3 2%
Other 9 5%
Unknown 18 10%