Population structure analysis reveals the genetic relationships between human populations, enabling ancestry inference, stratification correction, and demographic history reconstruction. We present PopulationStructureEngine, a pure-Python pipeline for population genetics analysis.
Spatial transcriptomics preserves the spatial context of gene expression, enabling mapping of tissue architecture and cell-cell interactions in situ. We present SpatialTranscriptomicsEngine2, a pure-Python pipeline for Visium spatial transcriptomics analysis.
Perturb-seq combines CRISPR perturbations with single-cell RNA-seq readout to systematically map gene regulatory relationships at scale. We present PerturbSeqEngine, a pure-Python pipeline for Perturb-seq analysis.
Cell-cell communication through ligand-receptor interactions coordinates tissue homeostasis, immune responses, and development. We present CellCommunicationEngine, a pure-Python pipeline for intercellular communication analysis.
Trajectory inference methods reconstruct developmental and differentiation trajectories from single-cell RNA-seq data. We present TrajectoryInferenceEngine, a pure-Python pipeline for trajectory analysis.
Cancer immunogenomics integrates somatic mutation data with HLA typing to predict neoantigens and understand immune editing of tumors. We present CancerImmunogenomicsEngine, a pure-Python pipeline for cancer immunogenomics analysis.
Synthetic lethality occurs when simultaneous loss of two genes is lethal while loss of either alone is tolerated, providing a therapeutic strategy to exploit cancer-specific vulnerabilities. We present SyntheticLethalityEngine, a pure-Python pipeline for synthetic lethality analysis.
Identifying cancer driver genes from the background of passenger mutations is a central challenge in cancer genomics. We present CancerDriverEngine, a pure-Python pipeline for cancer driver gene analysis.
Mutational signatures are patterns of somatic mutations reflecting the mutagenic processes active in a tumor. We present MutationalSignatureEngine, a pure-Python pipeline for mutational signature analysis.
Tumor mutational burden (TMB) and microsatellite instability (MSI) are established biomarkers for immunotherapy response. We present TumorMutationalBurdenEngine, a pure-Python pipeline for TMB/MSI analysis.
Intrinsically disordered proteins (IDPs) lack stable tertiary structure yet perform critical cellular functions, and their phase separation drives formation of membraneless organelles. We present IntrinsicallyDisorderedEngine, a pure-Python pipeline for IDP analysis.
Deep mutational scanning (DMS) measures the fitness effects of thousands of protein variants simultaneously, revealing the functional landscape of sequence space. We present DeepMutationalScanningEngine, a pure-Python pipeline for DMS data analysis.
Protein-protein interactions (PPIs) mediate virtually all cellular processes, and their disruption underlies many diseases. We present ProteinProteinInteractionEngine, a pure-Python pipeline for PPI network analysis.
Protein dynamics are essential for function, with conformational flexibility enabling catalysis, binding, and allosteric regulation. We present ProteinDynamicsEngine, a pure-Python pipeline for molecular dynamics trajectory analysis.
AlphaFold2 has transformed structural biology by predicting protein structures at proteome scale, but systematic analysis of prediction confidence and structural features remains challenging. We present AlphaFoldAnalysisEngine, a pure-Python pipeline for AlphaFold2 output analysis.
Phylogenetic analysis is fundamental to evolutionary biology, comparative genomics, and molecular epidemiology. We present PhyloEngine, a pure Python implementation of core phylogenetic algorithms requiring only NumPy and SciPy.
Network medicine leverages the topology of protein-protein interaction (PPI) networks to understand disease mechanisms and identify drug repurposing opportunities. We present NetworkMedicineEngine, a pure Python framework implementing core network medicine algorithms: disease module identification via largest connected component (LCC) analysis with permutation-based significance testing, module expansion via the DIAMOnD algorithm, drug-target network proximity computation, and disease-disease similarity analysis.
Metabolomics provides a functional readout of cellular biochemistry, capturing the downstream effects of genetic variation, environmental exposures, and disease states. We present MetabolomicsEngine, a pure Python framework for plasma metabolomics analysis implementing differential metabolite testing, dimensionality reduction, and pathway enrichment.
Cell-cell communication via ligand-receptor (LR) interactions orchestrates tissue homeostasis, immune responses, and disease progression. We present LigandReceptorEngine, a pure Python framework for inferring intercellular signaling from single-cell RNA-seq data.
Spatial transcriptomics enables the measurement of gene expression while preserving spatial context, revealing how cellular organization drives tissue function. Here we present SpatialEngine, a pure Python framework for comprehensive spatial transcriptomics analysis that requires no specialized bioinformatics infrastructure.