KofamKOALA

KofamKOALA assigns KEGG Orthologs (KOs) to protein sequences using KOfam profile hidden Markov models (HMMs) and pre-computed adaptive score thresholds to enable accurate functional annotation and pathway mapping.

Key Features:

• Profile HMM-Based Assignment: Uses KOfam profile HMMs to compare protein sequences against KO models, typically using HMMER/HMMSEARCH.
• Adaptive Score Thresholding: Applies KO-dependent pre-computed adaptive score thresholds to determine assignments and marks assignments exceeding thresholds with an asterisk '*' to indicate high confidence.
• Efficiency and Accuracy: Optimized for computational speed while achieving accuracy comparable to top-performing KO assignment methods.
• Integration with KEGG Resources: Assigns K numbers that enable linking genes to KEGG pathway maps and support molecular network reconstruction.

Scientific Applications:

• Functional Annotation: Assigns KOs to proteins to infer gene functions based on orthology.
• Pathway Mapping: Connects assigned K numbers to KEGG pathway maps for analysis of biological processes.
• Network Reconstruction: Facilitates construction of molecular networks by linking genes and enzymes via KEGG identifiers.

Methodology:

Performs homology searches of protein sequences against KOfam profile HMMs (e.g., using HMMER/HMMSEARCH), then applies KO-dependent pre-computed adaptive score thresholds to assign K numbers and flag high-confidence annotations.