MiGA's analyses can illuminate community composition, identify taxonomic novelty, hypothesize gene prediction, and evaluate assembly quality. MiGA can take in a variety of inputs such as raw, unassembled reads, assembled isolate genomes, metagenome-assembled genome (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 these genomes. The reference database is regularly updated and improved with minimal downtime. This ensures consistently improved accuracy of classification without a large impact to its users.