In Current genomics
BACKGROUND : One major challenge in binning Metagenomics data is the limited availability of reference datasets, as only 1% of the total microbial population is yet cultured. This has given rise to the efficacy of unsupervised methods for binning in the absence of any reference datasets.
OBJECTIVE : To develop a deep clustering-based binning approach for Metagenomics data and to evaluate results with suitable measures.
METHODS : In this study, a deep learning-based approach has been taken for binning the Metagenomics data. The results are validated on different datasets by considering features such as Tetra-nucleotide frequency (TNF), Hexa-nucleotide frequency (HNF) and GC-Content. Convolutional Autoencoder is used for feature extraction and for binning; the K-means clustering method is used.
RESULTS : In most cases, it has been found that evaluation parameters such as the Silhouette index and Rand index are more than 0.5 and 0.8, respectively, which indicates that the proposed approach is giving satisfactory results. The performance of the developed approach is compared with current methods and tools using benchmarked low complexity simulated and real metagenomic datasets. It is found better for unsupervised and at par with semi-supervised methods.
CONCLUSION : An unsupervised advanced learning-based approach for binning has been proposed, and the developed method shows promising results for various datasets. This is a novel approach for solving the lack of reference data problem of binning in metagenomics.
Madival Sharanbasappa D, Mishra Dwijesh Chandra, Sharma Anu, Kumar Sanjeev, Maji Arpan Kumar, Budhlakoti Neeraj, Sinha Dipro, Rai Anil
2022-Nov-18
Binning, K-means, convolutional autoencoder, deep clustering, genomic features, metagenomics
