A Deep Clustering-based Novel Approach for Binning of Metagenomics Data.

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.

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 cluster

SUBMITTER: Madival SD 

PROVIDER: S-EPMC9878855 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature