Fish Classification Using DNA Barcode Sequences through Deep Learning Method

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.

A deep learning model for fish classification base on DNA barcode

Fish is one of the most extensive distributed organisms in the world, fish taxonomy is an important part of biodiversity and is also the basis of fishery resources management. However, the morphological characters are so subtle to identify and intact specimens are not available sometimes, making the research and application of morphological method laborious and time-consuming. DNA barcoding based on a fragment of the cytochrome c oxidase subunit I (COI) gene is a valuable molecular tool for species identification and biodiversity studies. In this paper, a novel deep learning classification approach that fuses Elastic Net-Stacked Autoencoder (EN-SAE) with Kernel Density Estimation (KDE), named ESK-model, is proposed bases on DNA barcode. In stage one, ESK-model preprocesses the original data from COI fragments. 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 the threshold base on the outgroup scores and classify fish from different families. The effectiveness and superiority of ESK-model have been validated by experiment on three dominant fish families and comparisons with state-of-the-art methods. Those findings confirm that the ESK-model can accurately classify fish from different family base on DNA barcode.

Changes in Fish Taxonomy Affect Freshwater Biogeographical Regionalisations: Insights from Greece

Freshwater fishes are key indicators for delineating biogeographical maps worldwide. However, controversy in regional-scale ichthyogeographic boundaries still persists, especially in areas of high species endemicity, such as in Greece. One problem concerns the taxonomy of the fishes because there have been extensive changes, mainly due to an increased splitting of species in recent years in Europe. Here, we explore why ichthyogeographic boundary disagreements and uncertainties in region-scale biogeographical units persist. We compare cluster analyses of river basin fish fauna in Greece using two taxonomic datasets: the older fish taxonomy (from 1991) and the current taxonomy that now follows the phylogenetic species concept (PSC), which has become widely established in Europe after 2007. Cluster analyses using the older fish taxonomy depicts only two major biogeographical regional divisions, while the current taxonomy defines four major regional divisions in mainland Greece. Interestingly, some older maps from the pre-PSC taxonomy era also similarly show four ichthyogeographic divisions in Greece and we can assume that the older biogeographical work did not solely use numerical taxonomy but followed an expert-guided synthesis; the older regional definitions have persisted quite well despite radical changes in Europe’s fish taxonomy. Through the prism of biodiversity conservation planning, we hope this review may help identify ways to help standardize policy-relevant biogeographical mapping.

Fish Ontology framework for taxonomy-based fish recognition

Life science ontologies play an important role in Semantic Web. Given the diversity in fish species and the associated wealth of information, it is imperative to develop an ontology capable of linking and integrating this information in an automated fashion. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information on unknown fish based on metadata restrictions. It is designed to support knowledge discovery, provide semantic annotation of fish and fisheries resources, data integration, and information retrieval. Automated classification for unknown specimens is a unique feature that currently does not appear to exist in other known ontologies. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1,830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users.