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 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.
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.
Motivation: The vertebrate retina represents an ideal model for evolutionary developmental biology. Single-cell RNA sequencing has revolutionized understanding of retinal cell diversity, but cross-species analyses remain challenging.
We present AbDev, an automated pipeline for in-silico antibody developability profiling. From a single amino acid sequence, AbDev generates a comprehensive developability scorecard covering three assessment layers: chemical liability scanning (deamidation, isomerization, oxidation, glycosylation, unpaired cysteines, RGD motifs), five TAP physicochemical metrics compared against 242 clinical-stage therapeutics, and Thera-SAbDab benchmarking against all approved antibodies.
The rapid emergence of foundation models for single-cell genomics has created an urgent need for standardized, reproducible evaluation frameworks. We present scBenchmark, a comprehensive benchmark system that evaluates single-cell models across 7 core analytical tasks with 24 curated datasets spanning 3.
Microsatellite instability (MSI) is a critical biomarker for colorectal cancer (CRC) prognosis and immunotherapy response prediction. Approximately 15% of non-metastatic and 4–5% of metastatic CRCs exhibit MSI-high (MSI-H) status, defining a molecular subtype with distinct therapeutic implications.
Microsatellite instability (MSI) is a critical biomarker for colorectal cancer (CRC) prognosis and immunotherapy response prediction. While existing computational tools rely on read-count statistics or machine learning classifiers trained on fixed feature sets, they struggle with noisy sequencing data and cross-cohort generalization.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Failure Mode**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Failure Mode**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we characterize as the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we term the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models—a phenomenon we term the **"Harmonization-Dominance" Defect**.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.
Cross-cohort Alzheimer's disease (AD) blood transcriptomic prediction is sensitive to batch effects introduced during dataset harmonization. Standard pipelines treat batch correction and feature selection as independent steps, allowing features that required extreme mathematical rescuing during harmonization to dominate predictive models.