To identify the putative functions and annotations of the genes, MG-RAST follows a multi-step process. Initially, it builds clusters of proteins at a 90% identity level using the UCLUST implementation in QIIME. The longest sequence within each cluster is then selected for further analysis.
For the similarity analysis, MG-RAST employs sBLAT, a parallelized version of the BLAT algorithm using OpenMP. The search is conducted against a protein database derived from the M5nr, which integrates nonredundant sequences from various databases such as GenBank, SEED, IMG, UniProt, KEGG, and eggNOGs.
In the case of reads associated with rRNA sequences, a clustering step is performed at a 97% identity level. The longest sequence from each cluster is chosen as the representative and is used for a BLAT search against the M5rna database. This database integrates sequences from SILVA, Greengenes, and RDP, providing a comprehensive reference for the analysis of ribosomal RNA sequences.