A New Insight into Binning of Metagenomics Data Using Unsupervised Deep Learning Approaches

Background: Metagenomics has become a powerful tool for understanding microbial communities in various environments. One key challenge is to accurately identify and group genomic fragments in different clusters, or "bins", especially when the reference genome is not available. Objective: To develop an improved approach for binning of metagenomics databy incorporating extra genomics signatures utilizing advanced deep clustering techniques and evaluating the proposed approach using suitable measures Methods: In this study, we employed advanced deep learning-based unsupervised methods to perform binning of Metagenomics data. Our approach encompassed the analysis of multiple datasets and incorporated several essential features, including Tetra Nucleotide Frequency (TNF), Oligonucleotide Frequency Derived Error Gradient (OFDEG), Coverage, length of contigs, and GC-Content. Further, we incorporated additional techniques for feature extraction and dimensionality reduction such as, Deep Embedded Clustering (DEC), Variational Autoencoder (VAE), and K-means clustering as a part of binning process. Results: The performance of the developed approach was compared to existing methods and tools using benchmarked low-complexity simulated and real metagenomics datasets. The approach was evaluated using appropriate measures, including the Silhouette index, Rand index, and Accuracy. Remarkably, the results demonstrated that our proposed approach achieved a Rand index exceeding 0.80, a Silhouette index above 0.60, and an accuracy surpassing 0.80. These results clearly indicate that our approaches outperformed all previous unsupervised-based methods and were on par with the semi-supervised binning approaches. Conclusion: In conclusion, this study presents a new insight for the binning of metagenomics data using the unsupervised deep learning-based approach, specifically designed to tackle the issue of limited reference data. The developed method exhibits highly promising results across many datasets, underscoring its potential as an innovative solution for metagenomics binning.