HOGENOM

HOGENOM performs large-scale comparative genomics analyses of completely sequenced microbial genomes to produce annotated homologous gene families, multiple sequence alignments, and phylogenetic trees for gene annotation, detection of functional genomic regions, and studies of molecular evolution such as speciation and genome duplications.

Key Features:

• Automated sequence analysis pipeline: Integrates massive all-against-all similarity searches, gene clustering, multiple sequence alignment computation, and phylogenetic tree construction.
• Parallel computing optimization: Computations are optimized through parallel computing on a large computer cluster to process extensive sequence datasets.
• Database architecture: Shares a common architecture with HOVERGEN and HOMOLENS while being specifically tailored for microbial genomes.
• Output products: Produces high-quality homologous gene families, multiple sequence alignments, and phylogenetic trees based on advanced algorithms.
• Phylogenetic reconciliation: Implements phylogenetic tree construction and reconciliation using advanced algorithms to infer evolutionary relationships.

Scientific Applications:

• Comparative sequence analysis: Enables cross-species comparison of microbial sequences to identify gene families and assess sequence conservation.
• Phylogenetics and molecular evolution: Supports analyses of speciation, genome duplications, and gene family evolution using reconstructed phylogenies.
• Gene annotation and functional region detection: Facilitates gene annotation and the identification of novel functional genomic regions through homolog identification and alignments.
• Ortholog identification: Identifies orthologous genes across microbial genomes to support comparative and evolutionary studies.

Methodology:

Massive all-against-all similarity searches are followed by gene clustering algorithms, multiple sequence alignment computation, phylogenetic tree construction and reconciliation using advanced algorithms, with computations parallelized on a large computer cluster.