MarkerMAG

MarkerMAG links metagenome-assembled genomes (MAGs) with 16S rRNA marker genes using paired-end short reads to enable phylogenetic and functional analyses of microbial communities.

Key Features:

• Integration Capability: Links 16S rRNA genes to corresponding MAGs, increasing the number of MAGs that include 16S rRNA marker genes.
• Assignment Accuracy: Demonstrates 100% accuracy in assigning 16S rRNA genes to MAGs across benchmarking datasets of varying complexity.
• Copy Number Estimation: Accurately estimates the copy number of 16S rRNA genes within MAGs.
• Functional Prediction Enhancement: Improves the accuracy of functional predictions derived from 16S rRNA gene amplicon data by associating amplicon sequences with MAGs.
• Scalability on Real Data: Increases the number of MAGs with associated 16S rRNA genes by 1.1- to 14.2-fold in real metagenomic datasets.

Scientific Applications:

• Phylogenetic analysis: Facilitates phylogenetic placement by linking 16S rRNA marker genes with MAGs for improved taxonomic resolution.
• Environmental microbiome surveys: Enables more comprehensive surveys by connecting amplicon-based 16S data to MAG-derived genomic context.
• Functional inference from amplicons: Enhances interpretation of 16S rRNA gene amplicon datasets for functional and ecological inference via MAG associations.

Methodology:

Uses paired-end short reads to establish connections between 16S rRNA genes and MAGs and is implemented in Python3.