Building and Using Phage Genome Sequence Databases

As Earth's most abundant biological entities, bacteriophages (viruses infecting bacteria) play pivotal roles in microbial ecology, pathogenesis research, and therapeutic applications. High-throughput Phage Genome Sequencing has rapidly generated vast datasets of phage genetic material. To make sense of this wealth of information, sequence databases—such as PhageScope and PhagesDB—are indispensable for storing, annotating, and visualizing genomic data. Conversely, as these databases continue to integrate new high-quality sequences, they further enhance the power and precision of sequencing-based analyses, forming a virtuous cycle that underpins modern phage genomics.

Overview of PhageScope database (Wang RH wt al., 2024)

Constructing the Bacteriophage Genome Database

1. The Core Challenge: Why We need tough standards

Imagine trying to assemble a global library where every book is in a different language and format. This was the state of phage data. Building a reliable database requires a rigorous, multi-layered approach to standardize information from diverse sources:

2. Diverse Data Acquisition Sources

2. Source-Specific Processing Standards

Public Databases

• Screening: Automated taxonomic filters extract phage entries
• Compliance Tiering:
  • Complete: Circularized + annotated
  • High-Quality Draft: N50 > 50 kb
  • Draft: N50 > 10 kb
  • Fragment: Unassembled segments
• Deduplication: CD-HIT-EST clustering (99% identity threshold)

Metagenomic vMAGs

• Identification Pipeline:
  • VirSorter2 (marker-based)
  • DeepVirFinder (k-mer/AI prediction)
  • vRhyme (cross-sample binning)
• QC Thresholds: CheckV-verified ≥50% completeness + ≤10% contamination

Laboratory Isolates

• Submission Requirements:
  • Host strain documentation (e.g., ATCC no.)
  • EM structural verification
  • Experimental validation (plaques/growth curves)
• Priority Handling: Expedited review and featured display

Draft Submissions

• Provisional Status: Temporary ID with 1-year completion deadline
• Assembly Incentives: Preference for hybrid Nanopore+Illumina submissions
• Non-Compliance: Automatic downgrade to "Deprecated" status

Each source undergoes specific processing and strict quality checks to ensure it meets a defined grade—from "Complete" to "Draft"—before entering the database.

The Engine Room: How Data is Processed and Annotated

Once collected, the data must be cleaned, standardized, and interpreted.

1. Rigorous Quality Control:

Every genome is put through a multi-step validation process to check its completeness and remove any contaminating DNA from the host bacteria or other organisms.

• Assessment Tools:
  • CheckV: Quantifies completeness (%) and contamination (%)
  • BUSCO (viral gene set): Evaluates core gene preservation
• Certification Criteria for "Complete" Status:
  • Terminal overlap ≥10 bp
  • ≥90% CheckV completeness
  • Presence of ≥4 core viral genes (e.g., terminase large subunit, capsid protein)

Note: Draft/MAG entries must display integrity/contamination metrics prominently

2. Standardized "Labeling":

Each sequence is tagged with consistent, rich metadata—like the host bacterium, GPS coordinates of where it was found, and sampling date. This turns a raw sequence into a meaningful biological story.

• FASTA Header Requirements:
  • >DatabaseID|Host_Genus|IsolationSource|Date[YYYY-MM-DD]
  • Example: >PhageDB_KT003|Pseudomonas|Marine_sediment|2023-05-17

3. Minimum Metadata Standards

4. Contamination Control Protocol

• Three-Stage Filtration:
  • Primary Screening: Remove host-derived sequences (e.g., 16S rRNA) via NT database alignment
  • Deep Classification: Discard contigs with >5% non-viral matches using Centrifuge
  • Targeted Purification: Eliminate residual host fragments with k-mer databases (HostCleanse)

Comparison of Mainstream Phage Databases

From raw data to trusted information: a brief description of quality control and standardization pipelines.

1. Core Functional Element Prediction

• tRNA Identification: tRNAscan-SE v2.0 (-B -O parameters)
• rRNA Detection: Barrnap v0.9 (virus mode: --vir)
• Non-coding RNA: Infernal + Rfam 14.0
• ORF Prediction:
  • Prodigal v2.6 (prokaryotic mode: -p meta)
  • MetaGeneMark v4.0 (cross-validated)
  • Retention threshold: CDS ≥30 amino acids with valid start codon (ATG/GTG)

2. Functional Annotation Protocol

Tiered Assignment System

• Primary Annotation: Diamond BLASTP vs. PHROGS (e-value ≤1e⁻⁵, coverage ≥70%)
• Secondary Annotation: InterProScan domain analysis (Pfam/SUPERFAMILY)
• Tertiary Annotation:
  • DeepFri structure-based GO term assignment
  • Conflict resolution: PHROGs > InterPro > UniProt hierarchy

3. Integrated Genomic Feature Prediction

4. CRISPR System Analysis

• Spacer Identification: CRISPRCasTyper v2.4.1
• Target Prediction: CRISPRTargetDB alignment
• Anti-CRISPR Genes: AcrFinder + custom HMM profiles

5. Quality Assurance Workflow

Workflow Implementation

• Automated Phase: Standardized processing via pipeline managers
• Manual Intervention: Required for evolutionarily significant features:
  • First-reported phage-host pairs
  • Unannotated gene clusters (≥3 consecutive unknown CDS)
  • Novel anti-CRISPR or toxin candidates

Multi-Tiered Metadata Architecture for Phage Genomes

1. Core Metadata Specifications

• Storage Implementation:
  • Embedded in sequence files: INSDC-compliant FASTA headers (## comment lines)
  • Structured database storage:

2. Ecological & Phenotypic Extensions

Dynamic Field System (Optional but Recommended)

• Host Range: Comma-delimited verified hosts
  • Example: Salmonella enterica, Escherichia coli
• Lytic Profile: Quantitative parameters
  • Format: latent_period=20min; burst_size=150PFU
• Morphology: Dual-component documentation
  • EM image repository links
  • ICTV classification codes (e.g., Caudoviricetes; Siphoviridae)
• Stability Data: Temperature/pH tolerance ranges

3. Ontology-Driven Standardization

• Controlled Vocabulary Enforcement:

Database Architecture & Implementation Framework

1. Backend Storage System

• Hybrid Data Management:

2. Scalable File Management

• Sequence Storage: FASTA files in HDF5 block compression (70% space reduction)
• Search Optimization:
  • Weekly automated BLAST index updates
  • Distributed Diamond index sharding

3. Web Interface & API

• Advanced Search Capabilities:
  • Sequence similarity: BLAST/BLAT + MASH prescreening
  • Combinatorial queries (e.g., "Terminases AND marine hosts")
  • Taxonomic tree filtering

4. Integrated Visualization Suite

Summary of key innovation points

• Hierarchical management of dynamic data
  • Automatic ranking based on data quality (Complete/Draft/Deprecated)
  • Establish a "Gold Standard Dataset" for laboratory strains
• Guarantee of whole process reproducibility
  • All annotation tool versions are solidified in the Docker container
  • Provide workflow. YML for user local re-annotation
• Multi-dimensional relevance retrieval capability
  • Support "Environment → host → phage → gene function" penetrating query
  • Integrating phylogenetic trees with geographic information system data
• The "Dark matter" strategy
  • Initiate automatic structure prediction (AlphaFold2) for unknown gene clusters
  • Establishment of the "Phage Orphan Gene Repository"

For a more detailed approach to phage sequencing, please refer to "Phage Genome Sequencing: Methods, Challenges, and Applications".

To see how the Illumina platform can deep sequence phage libraries, see "Deep Sequencing of Phage Libraries Using Illumina Platforms".

Understanding the role of NGS data in quality control of phage display libraries can be referenced "Quality Control for Phage Display Libraries with NGS Data".

Powering Discovery: Key Applications for Researchers

• Precision Taxonomy & Phylogeny: Moving beyond morphology, databases use whole-genome analysis for accurate classification and large-scale evolutionary mapping.
• Novel Phage Discovery: They facilitate the discovery of new phages and unique genes, including those for host-range determination and novel anti-CRISPR (Acr) systems, directly feeding into therapeutic discovery programs.
• Phage Therapy Development: They serve as a primary screen for identifying pathogen-targeted phage candidates, predicting host range, and conducting critical safety assessments for lysogeny and virulence genes.
• Metagenomic Analysis: Acting as a essential reference, they enable researchers to identify and classify viral sequences within complex mixtures of DNA from the human gut, oceans, or soil.

Case in Point: Discovering Hidden Diversity

1. Taxonomy & Phylogeny

• Precision Classification
  • Whole-genome similarity analysis using VIRIDIC (ICTV-standard metrics)
  • Core gene phylogenies overcoming morphological limitations
• Evolutionary Mapping
  • Large-scale phylogenetic reconstructions
  • Divergence pattern analysis across phage taxa

2. Novel Phage Discovery

• Known functions: Lyases, polymerases, terminases, host-range determinants
• Unknown functions: PHROGs clustering + conserved domain identification
• CRISPR Systems
  • Host spacer matching (historical infections)
  • Novel anti-CRISPR (ACR) gene discovery

3. Phage Therapy Development

• Candidate Screening: Pathogen-targeted phage identification
• Host Range Prediction: Receptor-binding protein analysis + infection data correlation
• Safety Assessment: Lysogeny/virulence/antibiotic resistance gene screening
• Biocatalyst Discovery
  • Therapeutic enzymes (endolysins)
  • Specialized DNA polymerases

4. Metagenomic Analysis Foundation

• Reference Framework
  • Binning and taxonomic assignment
  • Functional annotation benchmark
• Virome Exploration
  • Ecosystem studies:
    • Gut microbiomes
    • Marine environments
    • Soil communities

5. Comparative Genomics Insights

• Modular Evolution
  • Conservation/recombination analysis of functional units:
    • DNA replication modules
    • Structural protein clusters
    • Packaging systems
    • Host lysis machinery

Multifunctional Validation of Phage Database Capabilities

1. Novel Phage Discovery & Classification

• High-Sensitivity Detection: Identified novel Rhizobium RR1-like phages with <30% sequence similarity to known entries via BLAST/BLAT
• Taxonomic Expansion: Cataloged 733 phages across 51 families using core gene clustering and ICTV standards

2. Non-Model Host Applications

• Case Study: Wild Banana (M. balbisiana) & Rare Genotypes (M. sikkimensis)
  • Detected unprecedented phage diversity
  • Validated database capacity for atypical host-phage systems

3. Host-Virus Interaction Analysis

4. Functional Gene Mining

• Virulence Factor Detection: Identified holin (lysis) and Shiga-toxin gene fragments via VFDB/PHROGs integration
• Therapeutic Potential: Discovered Klebsiella phages with Fusarium wilt suppression markers through antiSMASH metabolite analysis

5. Niche Adaptability Profiling

• Tissue-Specific Distribution
  • Leaf niche: Higher phage abundance (ENVO:00005784 "phyllosphere")
  • Root niche: Greater diversity (Shannon Index >4.2)
• Endogenous vs. Transient Virus Discrimination
  • Classified 56 Badnavirus strains using "Plant endophyte" vs. "Environmental parasite" tags

6. Knowledge Gaps as Discovery Catalysts

• Annotation Deficits
  • 1,038 uncharacterized protein domains reveal novel viral lineages (Aghdam SA et al., 2023)

Putative phage community overlap at the lowest taxonomic-levels (species or isolates) within the endosphere microbiomes of 6 Musa genotypes (Aghdam SA et al., 2023)

Challenges and Future Directions

Persisting Challenges

• Despite substantial advances, key limitations remain:
  • Data Quality Gaps: Inconsistent standardization across datasets
  • Host Representation Bias: Scarce genomes from non-model hosts (e.g., unculturable environmental bacteria)
  • Metadata Integration Barriers:Limited interoperability of ecological/experimental context data
  • Functional Knowledge Deficits: Viral "dark matter" (uncharacterized genes) representing >70% of predicted ORFs

Strategic Development Priorities

• Intelligent Annotation Systems
  • AI-assisted platforms combining:
    • Automated prediction pipelines
    • Expert curation interfaces
• Phage-Host Interaction Atlas: Experimentally validated host range databases
• Multi-Omics Resource Integration

Unified access to:

• Predictive AI Implementation
  • Deep learning models for:
    • Gene function elucidation
    • Host range projection

Conclusion

Phage genome databases have fundamentally transformed viral research by:

• Solving Data Management Challenges
  • Enabling efficient organization of exponentially growing sequence data
• Accelerating Discovery Applications
  • Serving as critical infrastructure for:
    • Novel antibacterial therapies
    • Synthetic biology tools
    • Ecological modeling

As sequencing technologies advance and global datasets expand, these repositories will remain indispensable for unlocking the full biotechnological potential of phages. Their continued evolution promises unprecedented insights into viral diversity, host adaptation mechanisms, and therapeutic engineering pathways.

Related Database Access:

PhageScope: https://phagescope.deepomics.org
PhagesDB: https://phagesdb.org
MGV: https://img.jgi.doe.gov/mgv
PhageScope:https://phagescope.deepomics.org

People Also Ask

What is the database for bacteriophages?

Welcome to PhageScope! PhageScope is an online bacteriophage database that offers comprehensive annotations, including completeness assessment, phenotype annotation, taxonomic annotation, structural annotation, functional annotation, and genome comparison.

What is a pham in phages?

Mycobacteriophage genes related to each other can be grouped into phamilies (phams) and that mosaic relationships can be analyzed and represented using pham-annotated genomes maps and phamily circles that show the patterns of which phages contain members of particular phams.

What is the ICTVdB the universal virus database?

The International Committee on Taxonomy of Viruses database is a universally available taxonomic research tool for understanding relationships among all viruses.

References:

1. Fujimoto K. Metagenome data-based phage therapy for intestinal bacteria-mediated diseases. Biosci Microbiota Food Health. 2023;42(1):8-12.
2. Wang RH, Yang S, Liu Z, Zhang Y, Wang X, Xu Z, Wang J, Li SC. PhageScope: a well-annotated bacteriophage database with automatic analyses and visualizations. Nucleic Acids Res. 2024 Jan 5;52(D1):D756-D761.
3. Aghdam SA, Lahowetz RM, Brown AMV. Divergent endophytic viromes and phage genome repertoires among banana (Musa) species. Front Microbiol. 2023 Jun 9;14:1127606.
4. Gauthier CH, Cresawn SG, Hatfull GF. PhaMMseqs: a new pipeline for constructing phage gene phamilies using MMseqs2. G3 (Bethesda). 2022 Nov 4;12(11):jkac233.
5. Wang RH, Yang S, Liu Z, Zhang Y, Wang X, Xu Z, Wang J, Li SC. PhageScope: a well-annotated bacteriophage database with automatic analyses and visualizations. Nucleic Acids Res. 2024 Jan 5;52(D1):D756-D761.