glactools

glactools provides command-line utilities to manage, manipulate, and analyze per-individual genotype likelihoods and population-level allele counts for population genomics research.


Key Features:

  • Data import and intermediate representation: Import genotypic data or population-wide allele counts into a flexible intermediate representation with two forms that store per-individual genotype likelihoods and aggregated allele counts across individuals.
  • Data operations and manipulation: Perform dataset intersections, merge individual samples into populations, and create subsets of genomic data.
  • Querying capabilities: Identify genetic sites where specific populations do not share alleles with other populations.
  • Export capabilities: Export data to multiple file formats compatible with a range of population genetics software.
  • Summary statistics computation: Compute summary statistics relevant to population genomics analyses.

Scientific Applications:

  • Population genomics data management: Standardize and store genotype likelihoods and allele-count data for population-level analyses.
  • Comparative population genetics: Detect population-specific alleles and compare genetic variation across populations.
  • Preparation for downstream analyses: Generate exports in formats required by other population genetics tools and workflows.
  • Reproducible analyses: Provide standardized intermediate representations to reduce ad hoc scripting and improve reproducibility of population-genomic workflows.

Methodology:

Implemented as C++ command-line utilities that link against the htslib library and operate on intermediate representations of genotype likelihoods and allele counts.

Topics

Details

License:
GPL-3.0
Maturity:
Mature
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Linux
Programming Languages:
C++
Added:
8/15/2019
Last Updated:
11/24/2024

Operations

Publications

Renaud G. glactools: a command-line toolset for the management of genotype likelihoods and allele counts. Bioinformatics. 2017;34(8):1398-1400. doi:10.1093/bioinformatics/btx749. PMID:29186325.

PMID: 29186325
Funding: - NSERC: 752657