EnTAP

EnTAP annotates de novo assembled transcriptomes from non-model eukaryotes by filtering and functionally characterizing translated proteins to improve functional annotation accuracy.

Key Features:

• Expression and Frame Selection Filtering: Applies filters based on true expression levels and frame selection to reduce the set of translated proteins for annotation.
• Fast Similarity Search: Conducts rapid similarity searches across five distinct repositories to identify potential matches.
• Protein Domain Assignment: Assigns protein domains to annotated sequences to provide functional insights.
• Orthologous Gene Family Assessment: Evaluates orthologous relationships among gene families to inform evolutionary and functional context.
• Gene Ontology (GO) Term Assignment: Assigns GO terms to gene products for structured functional description.
• Multi-database Integration and Weighted Assignment: Integrates data from multiple databases and selects optimal annotation assignments using weighted metrics that consider similarity search scores, taxonomic relationships, and annotation informativeness.
• Contaminant Filtering: Identifies and removes contaminant sequences from annotation results.
• Pathway Association and Enrichment Preparation: Associates pathways with transcripts and prepares annotations for enrichment analysis.

Scientific Applications:

• Functional annotation of non-model eukaryote transcriptomes: Provides comprehensive functional assignments for de novo assembled transcriptomes from organisms with limited genomic resources.
• Reduction of assembly artifacts: Mitigates issues from fragmentation and assembly errors that lead to inflated transcript estimates through expression and frame filtering.
• Evolutionary and functional inference: Supports evolutionary and functional analyses via orthologous gene family assessment and protein domain assignment.
• Gene function categorization and enrichment analysis: Enables GO-based categorization, pathway association, and downstream enrichment analyses.

Methodology:

Applies expression-level and frame-selection filters to reduce translated proteins, leverages open-source tools to perform similarity searches across five repositories, assigns protein domains, evaluates orthologous gene families, assigns GO terms, integrates multi-database results using weighted metrics based on similarity scores, taxonomic relationships, and annotation informativeness, and includes contaminant filtering plus pathway association for enrichment preparation.