Software developed by the HEART-GeN group

IsoGraph: multiplex network inference for isoform-switch modules

IsoGraph discovers co-regulated transcript programs from bulk RNA-seq by treating gene abundance and isoform switching as separate channels in a multiplex network. Starting from transcript-level counts, it builds gene-local switch coordinates from compositional transcript usage alongside a standardized abundance signal per gene, infers sparse gene-module structure through interchangeable backends (VAE, latent, graph, baseline, or WGCNA), classifies each module gene by the channel driving its membership (coupled, switch_only, abundance_only, or discordant), and links modules to phenotypic traits.

Modules can be explained with gene driver and transcript polarity tables, publication-ready plots, and encoder/decoder attribution, while switch pairs are annotated with GTF-derived exon, CDS/UTR, biotype, and coding-status changes. All steps are benchmark-validated, seed-controlled, and reproducible.

PyPI: https://pypi.org/project/isograph/

Documentation: https://isograph.readthedocs.io/

Tutorials: https://github.com/heart-gen/IsoGraph/wiki

localQTL

localQTL is a pure-Python library for local-ancestry-aware xQTL mapping that lets researchers run end-to-end analyses on large cohorts without any R/rpy2 dependencies. It preserves the familiar tensorQTL data model with GPU-first execution paths, flexible genotype loaders, and streaming outputs for large-scale workflows, while adding ancestry-aware use cases.

Repository: https://github.com/heart-gen/localQTL

Documentation: https://localqtl.readthedocs.io/

GENBoostGPU: Genomic Elastic Net Boosting on GPU

GENBoostGPU provides a scalable framework for running elastic net regression with boosting across thousands of CpG sites or regions, leveraging GPU acceleration.

It supports SNP preprocessing, cis-window filtering, LD clumping, missing data imputation, and phenotype integration – all optimized for large-scale epigenomics.

PyPI: https://pypi.org/project/genboostgpu/

Documentation: https://genboostgpu.readthedocs.io/

RFMix-reader: Accelerated reading and processing for local ancestry studies

Local ancestry inference is crucial for understanding population history and disease genetics, especially for eQTL studies in admixed populations. While RFMix is widely used, handling its output for large datasets is challenging due to high memory and processing demands. To address this, RFMix-reader efficiently processes large local ancestry datasets, leveraging GPUs for speed and minimizing memory usage, enabling deeper insights into human health and health disparities.

PyPI: https://pypi.org/project/rfmix-reader/

Documentation: http://rfmix-reader.readthedocs.io/

dRFEtools: dynamic recursive feature elimination for omics

Technological advances have generated larger omics datasets with applications in machine learning. Even so, limited sample availability often results in far fewer samples than features. Dynamic recursive feature elimination (RFE) provides a flexible feature elimination framework to tackle this problem. dRFEtools is an interpretable and flexible tool for gaining biological insights from omics data using machine learning.

PyPI: https://pypi.org/project/drfetools/

Documentation: http://drfetools.readthedocs.io/