Matapax

Matapax performs genome-wide association analyses on genotype and phenotype datasets to identify single-nucleotide polymorphisms (SNPs) and candidate gene regions underlying trait variation using high-throughput sequencing, genotyping, automated phenotyping, and "omics" profiling.

Key Features:

• High-Throughput Processing: Processes user-supplied trait data in parallel to accelerate large-scale GWAS computations.
• Association Algorithms: Implements established R libraries GAPIT and EMMA for association testing.
• SNP and Gene Identification: Identifies SNP markers and SNP-harboring or neighboring genes from genotype data.
• Annotation Support: Provides annotation information for candidate markers and genes.
• Result Presentation: Outputs annotated candidate markers in tabular format and visualizes them in a genome browser.
• Data Types Leveraged: Integrates high-throughput sequencing, genotyping methods, automated phenotyping platforms, and "omics" profiling as inputs for association analyses.
• Species Scope and Scalability: Supports association studies in Arabidopsis thaliana with genotyping of up to 1,375 accessions and is designed for scalability to additional plant species.

Scientific Applications:

• Candidate Gene Discovery: Identification of candidate gene regions and single genes responsible for trait variation.
• Plant Genetics and Breeding: Elucidation of genetic bases of traits to inform crop improvement and biodiversity conservation.
• Large-Scale GWAS: Analysis of large genotype/phenotype datasets from high-throughput sequencing and automated phenotyping for association studies.

Methodology:

Parallel processing of user-supplied trait data followed by association testing with R libraries GAPIT and EMMA to identify SNP markers and neighboring genes, with results annotated and displayed in a genome browser.