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.