Affiliation:
1. Division of Agriculture Bioinformatics, ICAR-IASRI, New Delhi- 110012, India
2. Division of Computer Applications, ICAR-IASRI, New Delhi- 110012, India
Abstract
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.
Method:
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.
Publisher
Bentham Science Publishers Ltd.
Subject
Genetics (clinical),Genetics