Flex-sweep documentation

Second version of Flex-sweep, a versatile tool for detecting selective sweeps. The software simulates, estimates summary statistics, and trains a convolutional neural network (CNN) to classify genomic regions as sweeps or neutrals.

Contents:

• Introduction
  • Pipeline overview
  • Recommended requirements
• Installation
  • Conda
  • Pip
  • Source
  • Testing the Installation
• Basic usage
  • Command line
  • Python interface
• Outlier scan
  • How it works
  • Available statistics
  • Normalization
  • Multi-contig usage
  • CLI reference
  • Python API
  • Output format
  • Scan results visualization
  • References
• CNN prediction
  • Prediction visualization
  • Post-processing predictions
  • Training diagnostics
• Enrichment analysis
  • Biological background
  • How the pipeline works
  • Quick start
  • Input file formats
  • Parameters
  • Understanding the output
  • Notes and constraints
• Ancestral state polarization
  • How it works
  • Polarization methods
  • Input files
  • CLI reference
  • Python API
  • Using polarized VCFs with scan and feature vectors
• Advanced usage
  • Available statistics
  • Additional statistics (Python API)
  • Selecting statistics
  • diploS/HIC-like feature vectors
  • Locus and window settings
  • Recombination-rate stratified normalisation
  • Haplotype sorting
• API Reference
  • Simulation
  • Data I/O
  • Feature vectors
  • Outlier scan
  • CNN / DANN
  • Enrichment
  • Ancestral polarization
  • Visualization
  • Population genetics statistics

Citing Flex-sweep

If you use flexsweep in your research, please cite:

Lauterbur, M.E., Munch, K. and Enard, D. (2023) Versatile detection of diverse selective sweeps with flex-sweep, Molecular Biology and Evolution, 40(6).

Kern, A.D. and Schrider, D.R. (2016). discoal: flexible coalescent simulations with selection. Bioinformatics, 32(24), 3839-3841.

Support

• Report issues on GitHub

• Contact: murgamoreno@arizona.edu, denard@arizona.edu