Species Identification using DNA Barcode Sequences through Supervised Learning Methods

Emu, Mahzabeen; Sakib, Sadman

doi:10.1109/ecace.2019.8679166

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Following the ABGD and ASAP methods, species delimitation through characterbased supervised machine learning methods was utilized to understand better the confirmation of the initial identification [84]. So far, several studies have performed the character-based barcoding approach, which has proved its usefulness in identifying plant species better than the conventional unsupervised methods [52][53][54]85]. In our analysis, the unsupervised ASAP method tended to provide a better resolution potential for the rbcL dataset than its neighboring ABGD method (Table 1).…”

Section: Discussionmentioning

confidence: 90%

“…The most conventional DNA barcode analysis method is the pairwise distance-based unsupervised Operational Taxonomic Unit (OTU) picking method, where Automated Barcode Gap Discovery (ABGD) is the most widely used tool, followed by the recently developed Assemble Species by Automatic Partitioning (ASAP) [50,51]. Comparatively, some studies have shown a higher rate of species discrimination using the supervised learning approach [21,[52][53][54][55]. Thus, in this study, we used a comparative approach of implementing both unsupervised and supervised methods for species delimitation.…”

Section: Introductionmentioning

confidence: 99%

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DNA Barcodes for Accurate Identification of Selected Medicinal Plants (Caryophyllales): Toward Barcoding Flowering Plants of the United Arab Emirates

et al. 2022

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The need for herbal medicinal plants is steadily increasing. Hence, the accurate identification of plant material has become vital for safe usage, avoiding adulteration, and medicinal plant trading. DNA barcoding has shown to be a valuable molecular identification tool for medicinal plants, ensuring the safety and efficacy of plant materials of therapeutic significance. Using morphological characters in genera with closely related species, species delimitation is often difficult. Here, we evaluated the capability of the nuclear barcode ITS2 and plastid DNA barcodes rbcL and matK to identify 20 medicinally important plant species of Caryophyllales. In our analysis, we applied an integrative approach for species discrimination using pairwise distance-based unsupervised operational taxonomic unit “OTU picking” methods, viz., ABGD (Automated Barcode Gap Analysis) and ASAP (Assemble Species by Automatic Partitioning). Along with the unsupervised OTU picking methods, Supervised Machine Learning methods (SML) were also implemented to recognize divergent taxa. Our results indicated that ITS2 was more successful in distinguishing between examined species, implying that it could be used to detect the contamination and adulteration of these medicinally important plants. Moreover, this study suggests that the combination of more than one method could assist in the resolution of morphologically similar or closely related taxa.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

DNA Barcodes for Accurate Identification of Selected Medicinal Plants (Caryophyllales): Toward Barcoding Flowering Plants of the United Arab Emirates

et al. 2022

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“…Methods based on DAN barcode can be categorized into four types: tree-based methods [18], similarity-based methods [27], character-based methods [28], and machine learning-based methods [29][30][31]. In biology, DNA barcode-based methods typically analyze DNA sequences, then calculate genetic distances and construct phylogenetic trees to classify organisms [32].…”

Section: Introductionmentioning

confidence: 99%

Fish Classification Using DNA Barcode Sequences through Deep Learning Method

Jin

Yuan

et al. 2021

Symmetry

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Fish is one of the most extensive distributed organisms in the world. Fish taxonomy is an important component of biodiversity and the basis of fishery resources management. The DNA barcode based on a short sequence fragment is a valuable molecular tool for fish classification. However, the high dimensionality of DNA barcode sequences and the limitation of the number of fish species make it difficult to reasonably analyze the DNA sequences and correctly classify fish from different families. In this paper, we propose a novel deep learning method that fuses Elastic Net-Stacked Autoencoder (EN-SAE) with Kernel Density Estimation (KDE), named ESK model. In stage one, the ESK preprocesses original data from DNA barcode sequences. In stage two, EN-SAE is used to learn the deep features and obtain the outgroup score of each fish. In stage three, KDE is used to select a threshold based on the outgroup scores and classify fish from different families. The effectiveness and superiority of ESK have been validated by experiments on three datasets, with the accuracy, recall, F1-Score reaching 97.57%, 97.43%, and 98.96% on average. Those findings confirm that ESK can accurately classify fish from different families based on DNA barcode sequences.

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“…For animal groups, cytochrome c oxidase 1 gene (COI) is used as a barcode, while matK and rbcL are used for identifying land plants, and ITS is used for fungi [7]. The problem then translates into classifying barcodes to a known species in a fast and efficient way [8].…”

Section: Introductionmentioning

confidence: 99%

Scalable classification of organisms into a taxonomy using hierarchical supervised learners

Sohsah

Ibrahimzada

Ayaz

et al. 2020

J. Bioinform. Comput. Biol.

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Accurately identifying organisms based on their partially available genetic material is an important task to explore the phylogenetic diversity in an environment. Specific fragments in the DNA sequence of a living organism have been defined as DNA barcodes and can be used as markers to identify species efficiently and effectively. The existing DNA barcode-based classification approaches suffer from three major issues: (i) most of them assume that the classification is done within a given taxonomic class and/or input sequences are pre-aligned, (ii) highly performing classifiers, such as SVM, cannot scale to large taxonomies due to high memory requirements, (iii) mutations and noise in input DNA sequences greatly reduce the taxonomic classification score. In order to address these issues, we propose a multi-level hierarchical classifier framework to automatically assign taxonomy labels to DNA sequences. We utilize an alignment-free approach called spectrum kernel method for feature extraction. We build a proof-of-concept hierarchical classifier with two levels, and evaluated it on real DNA sequence data from barcode of life data systems. We demonstrate that the proposed framework provides higher f1-score than regular classifiers. Besides, hierarchical framework scales better to large datasets enabling researchers to employ classifiers with high classification performance and high memory requirement on large datasets. Furthermore, we show that the proposed framework is more robust to mutations and noise in sequence data than the non-hierarchical classifiers.

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Species Identification using DNA Barcode Sequences through Supervised Learning Methods

Cited by 9 publications

References 8 publications

DNA Barcodes for Accurate Identification of Selected Medicinal Plants (Caryophyllales): Toward Barcoding Flowering Plants of the United Arab Emirates

DNA Barcodes for Accurate Identification of Selected Medicinal Plants (Caryophyllales): Toward Barcoding Flowering Plants of the United Arab Emirates

Fish Classification Using DNA Barcode Sequences through Deep Learning Method

Scalable classification of organisms into a taxonomy using hierarchical supervised learners

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