Abstract
BackgroundSpecific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms.MethodsIn this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods.ResultsA software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode classification methods. On empirical data their classification performances are at a comparable level to the other methods.ConclusionsThe classification analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community.
Highlights
Specific fragments, coming from short portions of DNA, have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms
Results of the Weka supervised learning methods tested on empirical datasets show that Support Vector Machines (SVM) and Naïve Bayes outperform the other techniques in term of percentage of the correct species identification
Results of the Weka supervised learning methods tested on synthetic datasets show that SVM and Naïve Bayes outperform the other techniques in term of percentage of the correct species identification
Summary
Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. Specific fragments, coming from short portions of mitochondrial, nuclear and plastid DNA, have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. DNA Barcoding solves this problem, because it is able to distinguish species and identify specimens ( incomplete, damaged or immature ones) using a very short gene sequence, that can be obtained from tiny amounts of tissue. Since 2004 the International Barcode Of Life project (IBOL) and the Consortium for the Barcode Of Life (CBOL) has promoted international initiatives devoted to the development of DNA Barcoding as a global standard for the identification of biological species, aiming to build up an online freely available sequence database (www.barcodinglife.org)
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