Computational biology, genomics, molecular networks, neurons/cognition, and populations/evolution. ← all categories
Neural circuit dynamics emerge from the interplay of excitatory and inhibitory neurons, generating oscillations that coordinate information processing across brain regions. We present NeuralCircuitEngine, a pure-Python pipeline for neural circuit simulation and analysis.
Autoimmune diseases have strong genetic components, with HLA alleles and polygenic risk scores (PRS) explaining substantial heritability. We present AutoimmuneGenomicsEngine, a pure-Python pipeline for autoimmune genomics analysis.
Vaccine immunogenicity varies substantially between individuals, with high responders achieving durable protective immunity and low responders remaining susceptible. We present VaccineResponseEngine, a pure-Python pipeline for vaccine response analysis.
High-dimensional immune phenotyping by mass cytometry (CyTOF) enables simultaneous measurement of 40+ markers per cell, revealing immune subset composition and functional states. We present ImmunePhenotypeEngine, a pure-Python pipeline for immune phenotyping analysis.
B cell receptor (BCR) repertoire analysis reveals antibody diversity, affinity maturation, and clonal evolution during immune responses. We present BCRRepertoireEngine, a pure-Python pipeline for BCR repertoire analysis.
T cell receptor (TCR) repertoire analysis reveals the diversity and clonal structure of adaptive immune responses. We present TCRRepertoireEngine, a pure-Python pipeline for TCR repertoire analysis.
Pan-genome analysis characterizes the full genomic diversity of a species, distinguishing core genes (present in all strains) from accessory genes (variable presence) and unique genes (strain-specific). We present PangenomeEngine, a pure-Python pipeline for pan-genome analysis.
Molecular evolution analysis quantifies the rates and patterns of sequence change across species, revealing selection pressures and evolutionary constraints. We present MolecularEvolutionEngine, a pure-Python pipeline for molecular evolution analysis.
Ancient DNA (aDNA) analysis enables reconstruction of past human populations, migrations, and admixture events, but requires specialized methods to handle DNA damage and contamination. We present AncientDNAEngine, a pure-Python pipeline for aDNA analysis.
Detecting signatures of natural selection in the human genome reveals adaptations to pathogens, diet, climate, and other environmental pressures. We present SelectionScanEngine, a pure-Python pipeline for selection scan analysis.
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.