Hieranoid

Hieranoid infers hierarchical ortholog groups across multiple proteomes using a species-tree-guided application of the InParanoid algorithm to identify orthologs while reducing computational complexity.

Key Features:

• Hierarchical aggregation: Aggregates ortholog groups along a species tree to organize multi-species ortholog groups coherently.
• Guide-tree-driven pairwise inference: Performs pairwise orthology analysis at internal nodes of a guide species tree using the InParanoid algorithm propagated from leaves toward the root.
• Scalability improvement: Reduces computational complexity from quadratic to linear with respect to the number of species by hierarchically decomposing the inference problem.
• Improved multiple sequence alignments and consensus sequences: Enhances construction of multiple sequence alignments and consensus sequences to increase coverage and accuracy (Hieranoid v2).
• Parallelized cluster mode: Supports a parallelized cluster mode for rapid processing of large proteomic datasets while maintaining accuracy comparable to InParanoid.
• HieranoiDB database: Provides a database of hierarchical ortholog groups inferred by Hieranoid covering 66 Quest for Orthologs reference proteomes and annotating speciation and duplication nodes.

Scientific Applications:

• Comparative genomics: Infers orthologs across multiple species for genome-wide comparisons of protein families and evolutionary conservation.
• Functional annotation: Transfers functional information across species by identifying orthologous relationships between proteins.
• Evolutionary biology: Resolves speciation and duplication events within ortholog trees to support studies of gene family evolution.

Methodology:

Uses a species guide tree to perform pairwise orthology inference at internal nodes with the InParanoid algorithm, aggregates ortholog groups hierarchically from leaves to root, employs improved multiple sequence alignment and consensus-sequence construction (v2), and offers an optional parallelized cluster mode for large datasets.