MetaG: a comprehensive visualization tool to explore metagenomes

摘要

Sequencing in metagenomes opens new ways of analyzing genomics in microbial communities in their habitats. Analyzing metagenomes has been a challenge not only because they are acquired from unknown collections without any prior lab-based cultivation, but also the volume. The value of these metagenomic data can be greatly enhanced by integrating with an informative and interactive visualization tool. Need for such tools is growing but still insufficient in number. MetaG, a standalone metagenomic analysis tool which converts higher dimensional sequence data into lower dimensional visualizations through deep learning techniques, is uniquely designed to produce rich lower dimensional representations of oligonucleotide frequency vectors of the population-level genomic diversity of the microbial organisms. Users can opt their preference in dimensionality reduction, between Principal Component Analysis (PCA), t- distributed Stochastic Neighbor Embedding (t-SNE) and autoencoders. Additionally, MetaG provides information on the presence, diversity and abundance of organisms by analyzing alignments against a database that contains taxonomy information. It caters more flexibility with number of visualizations, providing insights of complex microbial communities. MetaG, written in Python is free and open-source with the code publicly accessible.