In this paper, we present a new alignment-free technique for DNA barcode classification. Our method is based on the identification of distinctive words, extracted from the spectral representation of DNA sequences. In particular, we performed an unsupervised clustering using neural gas algorithm, for iteratively calculating those fingerprints that are characteristics of DNA sequences at different taxonomic levels. In order to demonstrate the efficacy of the proposed method, we tested it over 10 real barcode datasets belonging to different animalia species, provided by on-line resource Barcode of Life Database (BOLD).
A preliminary study on Spectral Representation Analysis for Classification of DNA Barcode Sequences
Antonino Fiannaca;Massimo La Rosa;Riccardo Rizzo;Alfonso Urso
2013
Abstract
In this paper, we present a new alignment-free technique for DNA barcode classification. Our method is based on the identification of distinctive words, extracted from the spectral representation of DNA sequences. In particular, we performed an unsupervised clustering using neural gas algorithm, for iteratively calculating those fingerprints that are characteristics of DNA sequences at different taxonomic levels. In order to demonstrate the efficacy of the proposed method, we tested it over 10 real barcode datasets belonging to different animalia species, provided by on-line resource Barcode of Life Database (BOLD).File in questo prodotto:
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