Antonino Fiannaca1, Massimo La Rosa2, Riccardo Rizzo2, Alfonso Urso2. 1. Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy. Electronic address: fiannaca@pa.icar.cnr.it. 2. Institute of High-Performance Computing and Networking, National Research Council of Italy, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy.
Abstract
OBJECTIVES: In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. METHODS: In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". RESULTS: The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. CONCLUSIONS: Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments.
OBJECTIVES: In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. METHODS: In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". RESULTS: The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. CONCLUSIONS: Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments.
Authors: Antonino Fiannaca; Laura La Paglia; Massimo La Rosa; Giosue' Lo Bosco; Giovanni Renda; Riccardo Rizzo; Salvatore Gaglio; Alfonso Urso Journal: BMC Bioinformatics Date: 2018-07-09 Impact factor: 3.169
Authors: Rahul Jamdade; Maulik Upadhyay; Khawla Al Shaer; Eman Al Harthi; Mariam Al Sallani; Mariam Al Jasmi; Asma Al Ketbi Journal: Plants (Basel) Date: 2021-12-13