Uniclust databases of clustered and deeply annotated protein sequences and alignments

Abstract

We present three clustered protein sequence databases, Uniclust90, Uniclust50, Uniclust30 and three databases of multiple sequence alignments (MSAs), Uniboost10, Uniboost20 and Uniboost30, as a resource for protein sequence analysis, function prediction and sequence searches. The Uniclust databases cluster UniProtKB sequences at the level of 90%, 50% and 30% pairwise sequence identity. Uniclust90 and Uniclust50 clusters showed better consistency of functional annotation than those of UniRef90 and UniRef50, owing to an optimised clustering pipeline that runs with our MMseqs2 software for fast and sensitive protein sequence searching and clustering. Uniclust sequences are annotated with matches to Pfam, SCOP domains, and proteins in the PDB, using our HHblits homology detection tool. Due to its high sensitivity, Uniclust contains 17% more Pfam domain annotations than UniProt. Uniboost MSAs of three diversities are built by enriching the Uniclust30 MSAs with local sequence matches from MMseqs2 profile searches through Uniclust30. All databases can be downloaded from the Uniclust server at uniclust.mmseqs.com. Users can search clusters by keywords and explore their MSAs, taxonomic representation, and annotations. Uniclust is updated every two months with the new UniProt release.

Figure 1.

Visualization of a cluster with the multiple sequence alignment including domain annotations, the taxonomic tree for the species of the cluster's member sequences, domain annotations, summary of sequence annotation keywords, and protein evidence values.

Figure 2.

(A) Sequence identities averaged over all clusters of Uniclust30, Uniclust50, Uniclust90, UniRef50 and UniRef90. We compute the mean and worst sequence identity between all possible pairs of sequences in a cluster. If a cluster contains more than ten sequences we sample ten sequences to compute the sequence identities. (B) Annotation consistency scores averaged over all clusters of Uniclust30, Uniclust50, Uniclust90, UniRef50 and UniRef90. We compute the mean and worst annotation consistency between the representative sequence and all other cluster members for Gene Ontology annotations (top-left), protein names (top-right) keywords (bottom-left), and the average of the former three (bottom-right). (C) Total Pfam annotation count difference between Uniclust and UniProt. (D) Comparison of the fraction of proteins in ten model organisms with Pfam annotations in Uniclust and in UniProt.