Supervised DNA Barcodes species classification: analysis, comparisons and results

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.

DNA barcodes and morphology of P. krempfi, P. mekongensis, and P. elongatus

“…Trong một số gene đặc trưng cho loài, gọi là DNA mã vạch, gene COI, cytochrome c oxidase subunit I, trên DNA ti thể được xem là DNA mã vạch đáng tin cậy trong phân loại nhiều lớp ngành khác nhau (Bucklin et al, 2011;Hebert et al, 2003;Ward, 2009;Weitschek et al, 2014), trong đó có lớp cá (Lara et al, 2010;Mabragaña et al, 2011;Steinke et al, 2009;Ward et al, 2009;Weigt et al, 2012). Đối với các loài thuộc họ cá tra, một số nghiên cứu ứng dụng chỉ thị DNA trong phân loại và nghiên cứu mối quan hệ di truyền đã được công bố, song chưa có nghiên cứu nào sử dụng gene COI (Gustiano and Pouyaud, 2007;Karinthanyakit and Jondeung, 2012; Sriphairoj et al, 2010).…”

Selecting Molecular Markers for a Specific Phylogenetic Problem

“…The rate of sequence submission has recently intensified for three primary reasons: the numerous and successful DNA barcoding projects [2,3], the advent of Next Generation Sequencing [4] and the subsequent decrease in prices for molecular sequencing services [5].As a consequence, genetic data repositories such as GenBank have been doubling in size every 18 months [6], rising from 606 sequences in the first 1982 release to close to 200 million sequences in the 218 th release in February 2017. Molecular data from more than 260 thousand nominal species is now widely accepted as a paramount source of biological information in all life sciences [7].…”

“…The rate of sequence submission has recently intensified for three primary reasons: the numerous and successful DNA barcoding projects [2,3], the advent of Next Generation Sequencing [4] and the subsequent decrease in prices for molecular sequencing services [5].…”

The solution of large-scale Minimum Cost SAT Problem as a tool for data analysis in bioinformatics

“…A long array of methods are used to perform such task, ranging from the more established parametric statistical analysis to non parametric techniques, to classification methods that have been developed in knowledge engineering and artificial intelligence. In this paper, we consider a method for extracting logic formulas from data that relies on a large body of literature in integer and logic optimization, originally presented in [1], that has been largely and successfully applied to different problems in bioinformatics ( [2], [3], [4], [5], [6]). Such method is based on the iterative solution of Minimum Cost SAT Problems and is able to extract logic formulas in DNF form that possess interesting features for their interpretation.…”