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Use Cases

This page covers the most common analysis scenarios you can run with Hoodini Colab.

Single Protein Analysis

When you want to explore the genomic neighborhood of a specific protein.

Scenario: You found an interesting protein in a paper and want to see what genes surround it across different organisms.

Configuration:

  1. Select Single Input mode
  2. Enter an NCBI protein ID (e.g., WP_012345678.1)
  3. Configure Remote BLAST settings:
    • E-value: 1e-10 (default, adjust for stringency)
    • Max targets: 100 (how many homologs to retrieve)
Input: WP_012345678.1
Mode: Single Input
Remote BLAST E-value: 1e-10
Max targets: 100

Single Input mode uses remote BLAST to automatically find homologous sequences. This only works with NCBI protein IDs.

Custom Homolog List

When you already have a curated list of sequences to compare.

Scenario: You ran your own BLAST search and want to analyze specific sequences rather than letting Hoodini choose.

Configuration:

  1. Select Input List mode
  2. Paste your IDs (one per line):
WP_000000001.1
WP_000000002.1
WP_000000003.1
NZ_CP000001.1

Unlike Single Input mode, you can mix NCBI protein IDs and nucleotide IDs in this mode.

Analyzing Non-Coding Regions

For CRISPR arrays, regulatory elements, or genomic islands.

Scenario: You want to compare CRISPR arrays or other non-coding genomic regions.

Configuration:

  1. Select Input List mode
  2. Enter nucleotide accessions (e.g., NZ_CP000001.1)
  3. In Neighborhood Window:
    • Set appropriate window sizes for your elements
    • Consider larger windows for genomic islands
Input: NZ_CP000001.1, NZ_CP000002.1
Window upstream: 15000
Window downstream: 15000

Defense System Survey

Comprehensive scan for antiphage defense systems.

Scenario: You want to catalog all defense systems in the neighborhoods around your genes of interest.

Configuration:

  1. Set up your input (any mode)
  2. Enable annotation tools:
    • ✅ PADLOC — Antiphage defense systems
    • ✅ DefenseFinder — Defense system detection
    • ✅ CCtyper — CRISPR-Cas systems
    • ✅ geNomad — Mobile genetic elements

First-time setup: Each tool requires downloading its database. Allow extra time (~2-5 min per tool) on the first run.

Phylogenetic Context Analysis

Add evolutionary context with tree construction.

Scenario: You want to visualize how genomic neighborhoods vary across a phylogenetic tree.

Configuration:

  1. Set up your input
  2. In Tree Construction, select a method:

Fastest option — Uses NCBI taxonomy

Tree mode: taxonomy_tree

Custom Coordinates (Input Sheet)

For precise control over which genomic regions to analyze.

Scenario: You have specific coordinates from your own analysis or want to reproduce exact regions.

Configuration:

  1. Select Input Sheet mode
  2. Fill in the table with:
    • protein_id or nucleotide_id
    • assembly_id
    • start and end coordinates
    • strand (+/-)

Example table format:

protein_idassembly_idstartendstrand
WP_000001.1GCF_000001.11000025000+
WP_000002.1GCF_000002.15000065000-

You can also paste TSV data directly into the table.

Comparing Gene Clusters

Visualize conserved operons across species.

Scenario: You’re studying a biosynthetic gene cluster and want to see how it varies across organisms.

Configuration:

  1. Use Input List mode with proteins from the cluster
  2. Enable Protein Links to see sequence similarity
  3. Set Clustering to group similar genes:
    • Identity threshold: 0.3 (30%)
    • Coverage threshold: 0.8 (80%)
  4. Enable a tree method to order genomes

Batch Processing Tips

For large-scale analyses:

  • Start with a subset: Test with 10-20 sequences first
  • Use taxonomy trees: Faster than AAI/ANI for large datasets
  • Limit annotations: Each tool adds processing time
  • Add NCBI API key: Speeds up data downloads significantly
# Set API key before running
import os
os.environ['NCBI_API_KEY'] = 'your_api_key_here'

Next Steps

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