BAR
BAR annotates protein sequences with structural and functional information by clustering UniProtKB sequences and transferring statistically validated annotations including Gene Ontology (GO), Pfam domains, and PDB-derived structural models.
Key Features:
- Graph-Based Clustering: Implements a graph-based, non-hierarchical clustering that groups UniProtKB sequences using pairwise similarity thresholds of sequence identity ≥40% and alignment coverage ≥90%.
- Comprehensive Annotation Database: Aggregates annotations from UniProtKB, Gene Ontology (GO), Pfam domains, and Protein Data Bank (PDB) structures within clusters.
- Large-Scale Sequence Analysis: Processes 28,869,663 sequences organized into 1,361,773 clusters; 22.2% of sequences have at least one validated GO term, 47.4% have at least one Pfam domain, and 1.4% of clusters include PDB structures.
- Cluster-HMM for Structural Modeling: Associates a hidden Markov model (Cluster-HMM) to clusters containing PDB structures to enable template-target alignments and direct computation of 3D models from sequence data.
- Statistical Validation of Annotations: Applies statistical validation to annotation transfer within clusters to ensure reliable assignment of GO terms and Pfam domains even at low homology.
Scientific Applications:
- Functional Annotation: Assigns validated Gene Ontology (GO) terms and Pfam annotations to infer protein biological roles.
- Structural Modeling: Uses Cluster-HMMs and PDB-derived templates to compute 3D structural models for cluster members.
- Comparative Genomics: Enables large-scale cross-comparison of UniProtKB sequences to investigate evolutionary relationships among proteins.
Methodology:
Performs graph-based, non-hierarchical clustering of UniProtKB sequences using pairwise thresholds of ≥40% sequence identity and ≥90% alignment coverage; computes profile Hidden Markov Models from sequence-to-structure alignments; associates Cluster-HMMs to clusters with PDB structures for template-target alignment and 3D model computation; and applies statistical validation for annotation transfer.
Topics
Collections
Details
- Maturity:
- Mature
- Cost:
- Free of charge
- Tool Type:
- web application
- Operating Systems:
- Linux, Windows, Mac
- Added:
- 1/22/2015
- Last Updated:
- 11/24/2024
Operations
- 3D profile generation
- Functional clustering
- Pathway or network prediction
- Protein function prediction
- Protein interaction prediction
- Protein subcellular localisation prediction
- 3D profile generation
- Functional clustering
- Pathway or network prediction
- Protein function prediction
- Protein interaction prediction
- Protein subcellular localisation prediction
- Database search
- Database search
- Database search
- Database search
- Database search
- Database search
Publications
Piovesan D, Martelli PL, Fariselli P, Profiti G, Zauli A, Rossi I, Casadio R. How to inherit statistically validated annotation within BAR+ protein clusters. BMC Bioinformatics. 2013;14(S3). doi:10.1186/1471-2105-14-s3-s4. PMID:23514411. PMCID:PMC3584929.
Profiti G, Martelli PL, Casadio R. The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation. Nucleic Acids Research. 2017;45(W1):W285-W290. doi:10.1093/nar/gkx330. PMID:28453653. PMCID:PMC5570247.
Piovesan D, Luigi Martelli P, Fariselli P, Zauli A, Rossi I, Casadio R. BAR-PLUS: the Bologna Annotation Resource Plus for functional and structural annotation of protein sequences. Nucleic Acids Research. 2011;39(suppl):W197-W202. doi:10.1093/nar/gkr292. PMID:21622657. PMCID:PMC3125743.