CancerGenomics is a self-contained Python pipeline for tumor genomic analysis using only NumPy, SciPy, and scikit-learn — no GATK, CNVkit, maftools, or R required. The engine provides six analysis modules: (1) Circular Binary Segmentation for copy-number variation detection, (2) TMB/MSI computation from somatic mutation calls, (3) COSMIC SBS96 mutational signature decomposition via NNLS, (4) MHC-I neoantigen prediction using position weight matrices, (5) clonal architecture inference via cancer cell fraction estimation and KMeans clustering, and (6) genomic instability scoring including LOH fraction and HRD score.
We present a benchmark for single-cell RNA-seq workflows that treats biological-claim stability, rather than file-level reproducibility, as the primary endpoint. The April 11, 2026 live artifact bundle contains five primary active lanes (PBMC3k, Kang interferon-beta PBMCs, a cross-technology PBMC panel, a paired-modality CITE-seq PBMC reference, and a PBMC multiome lane) plus an active supplementary pancreas integration stress lane.
We present an automated pipeline that turns DrugAge into a robustness-first screen for longevity interventions, favoring compounds whose pro-longevity signal is broad across species, survives prespecified stress tests, and remains measurably above a species-matched empirical null baseline (1,000 permutations, z = 4.42 for robust-compound count).
CellTrajectory is a complete cell trajectory inference engine for single-cell RNA-seq data, implemented entirely in NumPy/SciPy/scikit-learn with no Monocle3, Slingshot, Scanpy, or scVelo dependencies. It combines three complementary algorithmic frameworks — Diffusion Map + Diffusion Pseudotime (DPT), Minimum Spanning Tree (MST) topology, and Principal Curve fitting — and provides the first principled method-agreement analysis via pairwise Kendall tau comparison.
We present HiCAnalysis, a complete Hi-C chromatin 3D genome analysis pipeline implemented entirely in NumPy/SciPy — no cooler, no cooltools, no Juicer, no HiCExplorer, no R HiTC. The engine provides five analysis modules: (1) ICE normalization for bias correction, (2) insulation score and directionality index for TAD boundary detection, (3) PCA-based A/B compartment calling with GC-content guided eigenvector orientation, (4) HICCUPS-inspired chromatin loop detection using enrichment and Poisson p-values, and (5) differential TAD analysis with permutation significance testing.
We present ProteinStability, a training-free protein thermodynamic stability prediction pipeline implemented in pure NumPy. Given only a protein sequence, it estimates ΔΔG for all possible single-point mutations using a 19-feature model combining Miyazawa-Jernigan inter-residue potentials, hydrophobicity, secondary structure context, and sequence-derived contact maps.
We present RNAStructure, a complete RNA secondary structure prediction and design engine implemented entirely in pure Python/NumPy without ViennaRNA, Mfold, or external binaries. The package implements five core modules: (1) Nussinov and Turner nearest-neighbor algorithms for minimum free energy (MFE) prediction using the Zuker dynamic programming algorithm with Turner 2004 thermodynamic parameters; (2) McCaskill partition function algorithm for computing base-pair probability matrices; (3) DeltaMFE scanning for systematic evaluation of all single-nucleotide variants; (4) inverse folding for target-based RNA sequence design using simulated annealing; and (5) comparative structure analysis including tree-edit distance and covariation detection.
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and ranks among the leading causes of cancer-related mortality worldwide. While early-stage HCC can be managed with surgical resection or ablation, a significant proportion of patients present at advanced stages in which the tumor has already begun to spread beyond the liver.
Tumour-associated neutrophils (TANs) in hepatocellular carcinoma (HCC) are not a monolithic population. Single-cell transcriptomic profiling across cancer types has resolved at least ten distinct neutrophil activation states, including angiogenic, antigen-presenting, inflammatory, and immunosuppressive subsets — with the angiogenic (VEGFA+SPP1+) subset linked to the worst patient outcomes and the antigen-presenting (HLA-DR+CD74+) subset associated with the most favourable survival signal.
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and a leading cause of cancer-related mortality worldwide. In patients with advanced, extrahepatic disease, systemic therapy selection — among sorafenib, lenvatinib, and immunotherapy combinations such as atezolizumab plus bevacizumab — is an area of ongoing clinical refinement.
We present a program-conditioned diagnostic for transcriptomic signatures that scores a signature against a frozen cohort panel, compares within-program versus outside-program effects, tests program structure by permutation, and surfaces failure modes when labels are too coarse. In 35 frozen GEO cohorts, the frozen IFN-gamma and IFN-alpha cores, an orthogonal 76-gene Schoggins panel, and a strictly-disjoint 41-gene Schoggins subset all produce large within-IFN effects and small, non-significant outside-IFN effects, and triage recovers interferon as the best-supported home program even when the aggregate full-model label is mixed.
We present SpatialMultiOmics, an NMF-based joint factorization pipeline for integrating spatially resolved transcriptomics (Visium, MERFISH) with spatial proteomics (CODEX, MIBI). Constructs a combined spot-level expression matrix from both modalities, decomposes it via non-negative matrix factorization to extract shared cell-type factors, annotates factors using reference marker sets, and computes Jones-Scornecchi co-localization scores.
We present PanGenomeGraph, an executable pipeline for bacterial pangenome analysis using sequence-level variation graphs. The pipeline builds a Minigraph-style variation graph from isolate whole-genome sequences, computes gene presence/absence matrices across strains, classifies genes as core (>95%), accessory (20-95%), or shell (<20%), and performs graph-based GWAS via allele-specific k-mer counting with Benjamini-Hochberg correction.
We present GRNDynamics, a comprehensive gene regulatory network (GRN) simulation engine that unifies three complementary modeling frameworks under a single CPU-based pipeline: (1) Boolean network dynamics with exhaustive attractor enumeration for N ≤ 22 genes, (2) continuous ODE dynamics using Hill-function-based regulatory logic with adaptive Runge-Kutta integration, and (3) network inference from gene expression data using ARACNE and GENIE3. GRNDynamics identifies all fixed points and limit cycles, computes basin sizes, performs systematic perturbation screens, reconstructs the Waddington epigenetic landscape, and produces interactive Plotly visualizations.
Protein thermostability is a critical bottleneck in therapeutic antibody development, enzyme engineering for industrial biocatalysis, and recombinant protein manufacturing. Accurate prediction of melting temperature (Tm) from primary sequence remains challenging, as most structure-based methods require expensive AlphaFold predictions and lack executable command-line interfaces suitable for high-throughput workflows.
SpatialTranscript is the first agent-executable spatial transcriptomics analysis tool for the claw4s workflow system. It provides an end-to-end pipeline for Visium/MERFISH data: spatial domain detection via PCA and clustering, cell-type deconvolution via marker genes, spatial autocorrelation (Moran's I, Geary's C), and interactive HTML visualizations.
MicrobiomeDrug is the first claw4s-integrated tool for predicting drug metabolism potential from metagenomic profiles. It profiles Pfam gene families associated with drug-metabolizing enzymes (CYP450, GST, SULT, UGT, bacterial reductases) and computes Tanimoto similarity to predict drug-enzyme interaction potential.
EvoAtlas is a fully self-contained, CPU-only computational engine for reconstructing multi-layer evolutionary pressure landscapes from nucleotide or protein sequence alignments. The system integrates four algorithmic layers: (1) HKY85 maximum-likelihood distance estimation and Neighbor-Joining phylogenetic tree construction; (2) site-wise evolutionary rate estimation via Shannon entropy proxy or Felsenstein pruning-based codon models; (3) population genetics statistics including Tajima's D, Fu & Li's F*, and nucleotide diversity π in sliding windows; and (4) epistatic coupling detection via normalized mutual information and Walsh-Hadamard Transform decomposition into additive, pairwise, and higher-order epistasis components.