We evaluated MADRe on both real and simulated datasets and observed:
✅ Comparable or improved accuracy over existing tools
✅ Clearer and more realistic abundance profiles
✅ Substantial reductions in runtime and memory usage
7/9

While assembly is often considered computationally expensive, we demonstrate that MADRe, by combining assembly with contig-level mapping, is more efficient than directly mapping large volumes of reads to a full reference database. 6/9

To complete the pipeline, MADRe maps reads to the reduced reference database and applies a second round of probabilistic reassignment.
This enhances classification sensitivity and filters false-positive identifications, enabling precise strain-level profiling. 5/9