MiGA's analyses can accurately classify genomes providing a statistical classification support, identify taxonomic novelty, and annotate and evaluate sequencing reads and assembly quality, among others. MiGA can take in a variety of inputs such as raw, unassembled reads, assembled isolate genomes, metagenome-assembled genomes (MAGs), and single-cell amplified genomes (SAGs). MiGA uses a combination of the genome-aggregate average nucleotide identity concept or ANI and the average amino-acid identity, AAI, to taxonomically classify a query genomic sequence against the genome sequences in its reference database. Part of MiGA’s strength lies in the 10,000+ reference genomes that make up its database and an efficient heuristic algorithm to search the query genome against all this database. The reference database is regularly updated and improved with minimal downtime, ensuring consistently improved classification accuracy.