Use Cases

Real-world applications of AlleleFlux for metagenomic evolution studies.

Antibiotic Resistance Evolution

Question: How do bacterial communities evolve under antibiotic treatment?

Design: Longitudinal fecal samples from antibiotic-treated vs. control mice (pre, during, post-treatment)

AlleleFlux Analysis: - High parallelism scores in treatment group → deterministic resistance evolution - Divergence scores identify differentially selected genes between groups - Outlier genes highlight novel resistance mechanisms

Expected Findings: Antibiotic resistance genes with high scores; correlation with treatment outcomes

Diet-Microbiome Adaptation

Question: How do microbiomes adapt to dietary changes?

Design: High-fat vs. standard diet groups sampled at baseline, week 1, week 4

AlleleFlux Analysis: - Identify MAGs with diet-specific selection signatures - Focus on metabolic gene evolution - Track temporal adaptation patterns

Expected Findings: Nutrient acquisition genes under selection; taxonomic differences in adaptation rate

Host-Microbe Co-evolution

Question: How do host genotypes shape microbial evolution?

Design: Multiple timepoints from different host genotypes (e.g., WT vs. knockout)

AlleleFlux Analysis: - Compare allele changes between host types - Identify host-specific selective pressures - Focus on host-interaction genes

Expected Findings: Host genotype-specific microbial adaptations; immune evasion genes

Environmental Adaptation

Question: How do communities adapt to pollution?

Design: Contaminated vs. pristine sites sampled over time

AlleleFlux Analysis: - Parallel evolution of degradation pathways - Contaminant-specific gene selection - Biomarker discovery

Expected Findings: Degradation genes with high scores; potential bioremediation candidates

Analysis Tips

Data Quality - Aim for ≥10x average coverage per MAG - ≥4 biological replicates per group - Match sequencing depth across samples

Parameter Tuning - Start with default QC thresholds (breadth_threshold: 0.1) - Use LMM for complex experimental designs - Enable CMH for detecting parallel changes

Result Interpretation - Compare multiple statistical approaches for robustness - Focus on genes with consistent signals across tests - Validate high-scoring genes with functional data

Configuration Examples

See Input Preparation and Configuration Reference for detailed configuration guides.

Longitudinal study (antibiotic treatment):

data_type: longitudinal
timepoints_combinations:
  - timepoint: [pre, during, post]
    focus: post
groups_combinations:
  - [antibiotic, control]
quality_control:
  breadth_threshold: 0.2
  min_sample_num: 6

Single timepoint (disease vs. healthy):

data_type: single
timepoints_combinations:
  - timepoint: [baseline]
groups_combinations:
  - [disease, healthy]
quality_control:
  breadth_threshold: 0.1
  min_sample_num: 4

For complete worked examples, see Tutorial and Interpreting Results.