Marine eDNA Analysis using DL techniques

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Marine eDNA Analysis using DL techniques

Introduction

Environmental DNA (eDNA) is the genetic information or material released by an organism into its surroundings through activities like skin shedding, mucus, urine or other secretions. These materials contain DNA fragments that can be found in soil, water or air samples. The concept of eDNA is based on the fact that organisms constantly leave organic traces behind them. By analysing the eDNA, researchers can detect the presence of any species in that area. This method is a valuable technique for biodiversity monitoring and ecological research.   

Unlike traditional methods that involve direct observation or capturing of organisms, eDNA analysis allows researchers to study biodiversity without directly interacting with the species. This non-invasive approach is particularly useful for studying elusive or endangered species. eDNA analysis has been applied in various ecosystems, including freshwater, marine, and terrestrial environments. It is used to study aquatic organisms, terrestrial fauna, and even microbes. Applications range from monitoring invasive species to assessing the health of ecosystems.  

Challenges

The large volume of data from eDNA and its genetic complexity presents challenges to researchers. The traditional method of analysing eDNA involves time-consuming and labour-intensive processes which makes the task of processing large data difficult. Also, the challenge is accurately identifying a new species and assigning a taxonomical classification to them which is prone to errors. 

The recent development of deep learning algorithms has made the task of pattern recognition and image classification quite easy. Hence DL techniques are now a centre of attraction for researchers. The focus is to harness the capability of DL to improve marine biodiversity study, especially in eDNA analysis. CNN and RNN have a great scope to accurately identify new species or recognise a DNA sequence.

How DL is implemented?

Deep learning models excel in handling data preprocessing tasks in eDNA analysis. They can effectively reduce noise in raw sequencing data, align genetic sequences, and perform quality control measures. By automating these steps, deep learning ensures that subsequent analyses are based on clean and reliable genetic information.   

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are trained on extensive datasets to recognize specific genetic patterns associated with different marine species. The ability of DL to handle complex genetic patterns enhances the precision of taxonomic assignments, contributing to a more accurate representation of biodiversity. These models can accurately identify the presence of particular organisms in the eDNA data, providing a rapid and reliable method for species identification. However, the task of classification is not easy. The imbalanced dataset might decrease the accuracy. A possible solution is data augmentation, a commonly used technique in DL to artificially increase the size of the training dataset.

Another way to implement DL is in metabarcoding. Metabarcoding is a DNA sequencing-based species identification method. DL models are increasingly being employed to analyze metabarcoding data efficiently. The models can recognize and interpret the complex patterns in metabarcoding datasets, enabling researchers to study the relationships between different species and understand community structures. This application is particularly valuable in large-scale biodiversity assessments.

Apart from identifying species, researchers can gain valuable insights into complex ecological patterns that govern marine biodiversity. Such insights will prove helpful not only for biodiversity study but can pave new roads for further climatology studies and oceanography. There are still numerous challenges in this domain to overcome, however constant research will improve the implementation. Combining eDNA analysing and deep learning will have a major impact on the efficiency of marine biodiversity research.

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