opentargets/locus_to_gene_platform-2609

Scalable genomic analysis.

martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork.

JAMMA (Highly-Accelerated Multi-method Mixed-Model Association) is an open-source Python and C implementation of GEMMA's core linear mixed-model workflows for genome-wide association studies (GWAS). It reads PLINK binary genotypes and supports kinship estimation, Wald, likelihood-ratio and score association tests, covariates, multiple phenotypes, and leave-one-chromosome-out (LOCO) analysis. JAMMA provides a command-line interface with familiar GEMMA flags, GEMMA-compatible association output, and a Python API. Native C kernels, parallel computation and reusable eigendecompositions support large analyses. Pre-flight memory checks and streamed output help manage memory use. Numerical validation against GEMMA is documented. JAMMA runs on Linux, macOS and Windows; large-cohort analyses require sufficient RAM and a suitable 64-bit BLAS configuration. Released under GPL-3.0-or-later.

pyLocusZoom is an open-source Python library for visualizing genome-wide association study (GWAS) results. It creates LocusZoom-style regional association plots with linkage disequilibrium (LD) coloring, gene and exon tracks, and recombination overlays. Additional plots include Manhattan, QQ, Miami, eQTL, fine-mapping credible sets, PheWAS, forest plots, LD heatmaps and colocalization comparisons. Matplotlib provides static figures; Plotly and Bokeh provide interactive views. The library accepts pandas DataFrames and includes loaders for PLINK, GEMMA, REGENIE, BOLT-LMM, SAIGE, GTEx, SuSiE and FINEMAP outputs. It supports canine and feline reference data, automatic Ensembl gene annotations, and custom reference data for other species. LD can be supplied or calculated with PLINK. Requires Python 3.10 or later and is released under GPL-3.0-or-later.

A diffusion language model for genome-scale perturbation prediction across diverse cellular contexts.

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