MORA

MORA leverages abundance information to re-assign metagenomic sequencing reads with high precision, even at the strain level, for disentangling similar strains in microbial communities.

Key Features:

• Abundance-Aware Re-Assignment: MORA re-assigns reads by first estimating abundances through an expectation-maximization algorithm and then utilizing abundance information to re-assign query reads.
• Strain-Level Precision: MORA maximizes read re-assignment qualities while simultaneously minimizing the difference from estimated abundance levels, avoiding over-assignment of reads to the same genomes.
• Short and Long Read Support: MORA assigns both short and long metagenomic reads.
• Modular Integration: MORA is modularized for incorporation into broader metagenomics and genomics workflows.

Scientific Applications:

• Strain Disentanglement: MORA accurately infers correct strains for real data, as demonstrated with E. coli reads.
• Similar Strain Differentiation: MORA significantly outperforms other algorithms on very similar reads where taxonomic classification at the strain or sub-species level is challenging.
• Diverse Microbial Community Analysis: MORA achieves F1 scores comparable to other algorithms on simulated diverse reads with less runtime.

Methodology:

MORA estimates abundances through an expectation-maximization algorithm and then utilizes abundance information to re-assign query reads, maximizing read re-assignment qualities while minimizing the difference from estimated abundance levels.