Biology subjects

Shi, Y.

Publications and source records attributed to Shi, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

MutCleaner: Cleaning and Standardizing Biological Mutation Datasets for Variant Effect Prediction

Summary: Protein mutation datasets are widely used in variant effect prediction and protein engineering, but datasets from different sources often lack consistent conventions for mutation representation, sequence representation, data organization, and experimental labels, making these resources difficult to integrate directly and limiting their use in downstream analyses and modeling. MutCleaner is an extensible Python framework that cleans, validates, and standardizes protein- and codon-level mutation datasets through composable cleaning pipelines, unified sequence and mutation data structures, and dataset-specific cleaners. It provides standardized resources covering 16 protein and codon mutation datasets with more than 12.14 million mutation records. Availability and implementation: MutCleaner is an open-source Python package released under the Apache License 2.0. The source code is available on https://github.com/xulab-research/MutCleaner, the package is distributed through https://pypi.org/project/mutcleaner/, and the documentation is available https://xulab-research.github.io/MutCleaner/.

bioinformatics↗

bioRxiv2026

Computer vision-aided locomotor behavioral analysis identifies therapeutic motor signatures in a mouse model of Huntington disease

Huntington disease (HD) is a neurodegenerative disorder characterized by progressive motor dysfunction. Traditional open-field tests quantify spontaneous locomotor parameters; however, fine mouse motor signatures, particularly disease stage-specific changes in HD motor symptoms and pharmacodynamic responses to therapeutic treatments. Here, we employed a computer vision-aided behavioral flow analysis designed to quantify fine, HD-relevant motor dysfunction in the zQ175DN HD mouse model, ranging from early HD-like motor signatures to well-defined motor deficits. Markerless pose estimation and Keypoint-MoSeq segmented standard top-view open-field recordings into recurrent behavioral syllables, which were then organized into higher-order clusters and transition networks. Disease stage-dependent changes in syllable occurrence, syllable duration, behavioral-state composition, and transition structure were identified. These analyses are not possible with traditional open-field assays. Syllable-duration features provided the strongest genotype discrimination, and HD-like motor features were also characterized by hub remodeling and transition-network disorganization. These features were integrated into an HD motor dysfunction (HDMD) score based on age- or HD progress-matched wild-type (WT) -standardized absolute deviations. The HDMD score distinguished HD mice from WT across multiple symptomatic stages and correlated with HD pathology and disease severity. Effect-size and power analyses suggested improved efficiency for detecting potential therapeutic effects. This framework requires only standard top-view recordings and may also support retrospective analysis of existing open-field video datasets. Overall, the HDMD framework provides a practical strategy for identifying fine motor changes in HD mice, aiding study design and preclinical efficacy assessment in HD drug development.

neuroscience↗

bioRxiv2026

OmniSyn unifies target-aware molecular generation and optimization within a synthesis-native LLM framework across the human proteome

Designing target-specific bioactive molecules with actionable synthesis routes for the human proteome holds enormous potential for expanding therapeutic discovery, but remains a challenge. Existing target-aware generative models often depend on protein structures and generate molecules before assessing synthetic feasibility. Here we present OmniSyn, a protein-sequence-conditioned Mixture-of-Experts (MoE) language model that couples a task-conditioned interaction module with a synthesis-action decoder to generate molecules with explicit synthesis traces, thereby unifying de novo ligand generation, synthesizability projection and hit-to-lead (H2L) optimization within synthesis-traceable chemical space. OmniSyn is pre-trained with self-distillation and post-trained with task-specific reinforcement learning (RL) to adapt expert routing and optimize molecular properties across design modes. On unseen protein targets from MolGenBench, a real-world drug-discovery benchmark, OmniSyn achieves state-of-the-art performance across de novo design and H2L optimization, including target-awareness and hit-rediscovery metrics, despite relying only on protein sequences rather than three-dimensional (3D) pocket structures. By embedding synthesis planning into the design process, OmniSyn shifts molecular generation from a generate-then-filter paradigm toward design-with-synthesis paradigm, transforming virtual predictions into experimentally actionable candidates. Independent AiZynthFinder evaluation yielded retrosynthetic success rates of 68.47% for de novo generation and 71.92% for H2L optimization, improving over the strongest baselines by 61.3% and 184.2%, respectively, and supporting the synthetic feasibility of OmniSyn-generated molecules. In synthesizability projection, OmniSyn further converts outputs from external generative models into close analogues with improved retrosynthetic feasibility while preserving molecular similarity. Having established strong benchmark performance and external retrosynthetic feasibility, we next applied OmniSyn at human-proteome scale, spanning more than 21,000 targets. Rapid sequence-conditioned sampling enabled the construction of, to our knowledge, the largest human-proteome-scale generative virtual library, comprising 2.7 billion target-specific molecules, each accompanied by model-derived synthesis traces and target-specific prioritization scores. By enabling scalable target-specific molecular design with synthesis-aware generation across the human proteome, OmniSyn opens new opportunities for exploring previously inaccessible therapeutic targets, including those lacking experimentally resolved structures.

bioinformatics↗

bioRxiv2026

A Hymenoptera-restricted gene mediating ant castes co-opts deeply conserved machinery to control organ size

Lineage-specific genes are widespread and have been implicated as phenotypic innovation inducers, but how they acquire complex developmental functions remains poorly understood. Ant queens and workers develop dramatically different organ sizes from identical genomes under juvenile hormone (JH) control, yet the molecular effectors translating JH signalling into caste-specific organ growth remain unknown. Here we identify torch, a Hymenoptera-restricted gene, as the most consistently gyne-biased and JH-responsive gene across 68 ant species. Knockdown of torch in virgin queens of Monomorium pharaonis produces a worker-like, multi-organ growth-restricted phenotype. Mechanistically, torch harbours an E-box-like motif activated by the JH receptor Gce-Tai and acts as a GA-repeat-binding transcription factor that regulates Hippo signalling, the deeply conserved organ-size control pathway in animals. Expressing torch heterologously in mice and a growth-restricted Drosophila background shows that the gene retained its general growth-promoting activity across more than 700 million years of animal evolution in lineages that lack the gene, establishing that its function is mediated through conserved rather than ant-specific machinery. A lineage-specific gene can therefore acquire complex morphogenetic function by co-opting ancient organ-size circuitry, providing a general route by which novel genes can drive phenotypic innovation.

evolutionary biology↗

bioRxiv2026

Multiplexed Quantification of Variant Abundance in the Globin Gene Family: Integrating Saturation Mutagenesis with Cross-Paralog Prediction

Widespread genetic testing has expanded variant identification, yet functional characterization remains a bottleneck in genome guided medicine. Here, we present a modified Variant Abundance by Massively Parallel Sequencing (VAMP-seq) platform integrating experimental and computational approaches for high-resolution abundance profiling of protein variants. Utilizing a lentiviral integration system, we systematically assessed the stability effects of 2,696 amino acid substitutions in {zeta}-globin (HBZ) via saturation mutagenesis in human cells, achieving complete variant coverage with high reproducibility. Representative variants showed strong concordance with orthogonal low-throughput validation assays. We further developed a deep learning framework leveraging VAMP-seq derived HBZ data to predict variant abundance across thalassemia-associated globin paralogs (HBA, HBB, and HBG1) not experimentally tractable. Our hybrid framework demonstrates how targeted experimental profiling combined with AI-driven extrapolation can accelerate variant interpretation across protein family members.

genetics↗

bioRxiv2026

Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder

The human immune system excels at generating highly effective antibodies through natural selection and somatic hypermutation, but adapting these antibodies for therapeutic use, referred to as "antibody medicine-likeness", requires careful consideration of biochemical and physiological properties. Traditional redesign methods are often slow and limited in scope. In this study, we introduce a machine learning-based approach to evolve new anti-PD-1 antibodies within a chemically informed latent space using a conditional kernel-elastic autoencoder (CKEA) between nivolumab and pembrolizumab, both of which bind the FG-loop "hotspot" of PD-1 in the most distantly related orientations, differing by 174{degrees}. This generative framework is designed to preserve favorable therapeutic features while exploring variants with different potency, ultimately for improved potency. To evaluate structural and functional viability, we performed molecular dynamics (MD) simulations of the generated antibody - PD-1 complexes and described their MD properties. These simulations reveal detailed free-energy landscapes and identify stable binding conformations, providing a strong basis for experimental validation. To validate our designs, we expressed and experimentally tested the antibodies for binding affinity to PD-1. Upon expression and purification, three out of six designed antibodies exhibited some binding to PD-1, whose properties could likely be improved using other computational saturation mutagenesis or laboratory evolution. Our results demonstrate the potential of artificial intelligence (AI)-guided interpolation methods to generate novel, high-affinity antibodies with therapeutic promise, offering a powerful strategy for next-generation antibody development. SYNOPSIS TOCCombination of MD simulations with machine learning algorithms could revolutionize the antibody-breeding sciences to lead to new antibody discovery that is compatible with or better than naturally occurring antibodies. Topics of Content (TOC)Fingerprints of R86 finger of PD-1 are recognized by our designed P2N_2 anti-PD-1 antibody according to our MD simulations O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/742579v1_ufig1.gif" ALT="Figure 1000"> View larger version (49K): org.highwire.dtl.DTLVardef@130fd5aorg.highwire.dtl.DTLVardef@1495cd8org.highwire.dtl.DTLVardef@16e822forg.highwire.dtl.DTLVardef@25004c_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Uncovering key genes related to potato tuber starch concentration from the tuber bulking, maturation stage to storage

Potato is the worlds fourth major staple crop, and tuber starch is a critical dietary energy source and a food processing raw material. In this study, hybrid progenies of the high-starch cultivar Huasheng No.7 and China main commercial varieties were used as materials, high-throughput sequencing was performed on tuber samples at tuber bulking, maturation and storage stages. Combined with bioinformatics analysis, parent-progeny resequencing, SNP screening, the regulatory mechanism of tuber starch metabolism was explored. The results showed significant stage-specific transcriptional reprogramming in potato tubers: few differentially expressed genes (DEGs) were identified at tuber bulking and maturation stages, but massive transcriptional changes occurred during storage. XET family genes regulated cell wall remodeling and carbon translocation across all stages, and WRKY transcription factors specifically controlled starch homeostasis in stored tubers. The DEGs at different stage were induced by the SNPs from their parent, and the allele from the high-starch parent, Huasheng 7 may contribute the high-starch allele to the offsprings. One key gene, Soltu.DM.02G019910, which encoded {beta}-glucosidase, have a negatively relationship with tuber starch concentration. The low-starch potato breeding tubers shown higher significantly {beta}-glucosidase activity than high-starch breeding population at maturation stage. This study clarifies the molecular regulatory network of potato tuber starch metabolism and screens core regulatory genes from the tuber bulking stage to tuber storage, thereby providing theoretical support and genetic resources for molecular breeding of high-starch and storage-resistant potato varieties.

plant biology↗

bioRxiv2026

Deciphering complete archaic introgression sequences in modern human genomes

Genetic introgression from archaic hominins has profoundly reshaped the genetic diversity and adaptive potential of modern humans, yet the full catalog of introgressed sequences, particularly those residing in structurally complex regions has remained elusive. Here, we present ASMaid (ASseMbly-based archaic introgression detector), a Hidden Markov Model-based framework that leverages haplotype-resolved pangenome assemblies to identify archaic-derived sequences with unprecedented completeness. By integrating both single-nucleotide genotype and structural variation (SV) signals, ASMaid captures significantly more intact archaic segments than conventional reference-based approaches. Applying ASMaid to a global panel of 610 phased human genome assemblies, we show that non-African individuals carry approximately 79.8 Mbp of Neanderthal and 8.3 Mbp of Denisovan sequences, representing substantial increases over previous estimates, respectively. Notably, we detected several centromere-spanning archaic segments, including EAS-specific calls on chromosomes 5 and 7. Our assembly-based approach uncovered 1,701 archaic-derived SVs, revealing a previously overlooked layer of archaic functional legacy. High-frequency introgressed loci are enriched in pathways associated with metabolism, immunity, and nervous system (e.g. CTNNA2 linked to early-onset schizophrenia risk), underscoring the fundamental role of introgression in modulating modern human traits. Notably, we identified dozens of loci potentially facilitating local adaptation, such as PRDM16 involved in adipocyte differentiation and cold tolerance, and CSGALNACT2 associated with chondroitin sulfate synthesis. Furthermore, our analysis delineates three distinct Denisovan introgression pulses in Eastern Eurasian genomes, in which the first two pulses are shared across East Eurasian and Oceanian populations, while the third remain primarily exclusive in East Asians. Reflecting these complex introgression events, 31 Denisovan-derived segments, including the TBX15-WARS2 locus, are inferred to have been introduced via at least two events. This comprehensive map of archaic introgression provides a fundamental resource for understanding how ancient gene flow continuously shapes human phenotypic diversity and adaptation.

evolutionary biology↗

bioRxiv2026

Uhrf1 loss disrupts Ctcf-associated chromatin organization during early mouse embryogenesis

UHRF1 is a chromatin-binding protein essential for maintaining DNA methylation and histone modification states, yet its integrated role in vivo remains incompletely understood. To define its function, we generated conditional Uhrf1 knockout embryonic stem cells (ESCs) and embryos. Uhrf1-/- ESCs exhibited near-complete loss of 5mC and 5hmC but maintained pluripotency, whereas Uhrf1-null embryos developed normally until E8.5 and then failed to develop further by E9.5, phenocopying Dnmt1 loss. Single-cell multi-omic (ME-seq) profiling of E8.5 embryos revealed impaired lineage stabilization, widespread hypomethylation, and disrupted chromatin architecture. Uhrf1 loss was associated with altered CTCF-associated chromatin signal, broad remodeling of chromatin contacts, altered cis-regulatory relationships, and reduced predicted BMP-related ligand-receptor communication, particularly within neural crest populations. These findings identify Uhrf1 as a central regulator that tightly couples DNA methylation maintenance to 3D genome organization during gastrulation, thereby, directing early lineage specification and positioning Uhrf1 as a pivotal mediator of epigenetic information transfer during early embryogenesis. HighlightsO_LIUhrf1 knockout ESCs show global loss of 5mC/5hmC but maintain pluripotency. C_LIO_LIUhrf1-null embryos develop normally until E8.5 but die by E9.5 with severe defects. C_LIO_LISingle-cell multi-omics revealed disrupted chromatin, transcription, and lineage stability upon knockout. C_LIO_LILoss of Uhrf1 alters CTCF-associated chromatin signal, predicted BMP-related communication, and cis-regulatory relationships. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/738670v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@15bf31dorg.highwire.dtl.DTLVardef@1b601ecorg.highwire.dtl.DTLVardef@386018org.highwire.dtl.DTLVardef@12ce99f_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2026

Adipose GPD2 Couples Glycolytic Signals to Epigenetic Control of Metabolic Homeostasis

The glycerol-3-phosphate shuttle (G3PS) is a classic reducing equivalent transferring system, yet its role in adipose metabolism remains poorly understood. Here, we identify mitochondrial glycerol-3-phosphate dehydrogenase (GPD2), the core enzyme of G3PS, as a critical orchestrator of adipose thermogenesis and systemic metabolic homeostasis. Adipocyte-specific GPD2 knockout mice exhibited impaired thermogenic capacity and energy expenditure, rendering them susceptible to obesity and metabolic dysfunction. Mechanistically, GPD2 deficiency elevated cytosolic NADH, which suppressed glycolysis and decreased the levels of key metabolites, such as acetyl-CoA, thereby diminishing H3K27ac at thermogenic gene loci and silencing these genes. NADH depletion with an NADH oxidase rescued both glycolysis and thermogenic gene expression. In support of this mechanism, we defined a previously undescribed enhancer of Ucp1 whose H3K27ac was tightly controlled by GPD2. Remarkably, replenishing the acetyl-CoA pool or directly restoring H3K27ac at the Ucp1 enhancer reversed UCP1 expression in GPD2-deficient adipocytes. Importantly, we find that human adipose GPD2 expression is inversely correlated with BMI, WHR, and HOMA-IR, underscoring its clinical relevance. Collectively, our findings establish GPD2 as an essential node in the metabolic-epigenetic axis that governs thermogenic activation and energy balance.

biochemistry↗

bioRxiv2026

ITPKB is a conserved regulator of natural killer cell desensitization/education that constrains antitumor immunity

Natural Killer (NK) cell desensitization induced by persistent stimulation limits durable antitumor immunity, yet the molecular mechanisms governing this dysfunctional state remain poorly defined. To identify conserved regulators of NK cell desensitization, we performed comparative transcriptomic analyses across multiple murine models of persistent activation and dysfunction. This approach defined a shared transcriptional program of desensitization and identified Itpkb to be upregulated across multiple distinct contexts. Genetic and pharmacological inhibition of ITPKB enhanced degranulation, cytokine production, and cytotoxicity in both murine and human NK cells under multiple desensitization settings. Mechanistically, ITPKB regulated signaling downstream of persistent activation through the IP3/IP4 axis, limiting calcium mobilization and NFAT-dependent transcriptional responses in desensitized NK cells. Furthermore, inhibition or deletion of ITPKB enhanced NK-cell mediated tumor control in vivo and improved the efficacy of adoptively transferred CAR-NK cells, underscoring the translational potential of targeting this pathway. Together, these findings identify ITPKB as a cell-intrinsic regulator of NK cell desensitization and support targeting the IP3/IP4 signaling axis to enhance NK cell-mediated antitumor immunity.

immunology↗

bioRxiv2026

Rapid immunostaining and high-resolution three-dimensional light-sheet microscopy of intact calcified tissues

High-resolution 3D imaging is an important strategy for visualizing and analysing complex skeletal tissue architecture and the bone marrow microenvironment. However, multicolor immunolabeling and imaging of intact skeletal tissues are technologically challenging. The current immunolabeling and clearing methods for intact skeletal elements are very limited, time-consuming and generate low-resolution data or depend on the use of reporter mice. Here, we describe a protocol for efficient clearing and immunolabeling of intact calcified tissues that enables superfast, single-cell resolution, and quantitative 3D light-sheet imaging of intact skeletal elements and teeth. A key aspect of our protocol is the addition of a collagenase digestion step after fixation and decalcification. This step enhances antibody penetration, resulting in deep, comprehensive staining throughout immunostained bones and other calcified tissues. The protocol includes soft tissue removal, fixation, decalcification, bone dehydration, and bleaching, followed by antigen retrieval and permeabilization before the collagenase digestion step. This procedure is performed to prepare the samples for the tissue clearing process that improves bone tissue transparency prior to light-sheet imaging. The entire protocol, from bone collection to image analysis and quantification, takes about 4 days to complete, thus offering significant improvements over previous methods. This protocol is broadly applicable to the visualization of bone microstructure, bone marrow analysis, vascular and neural network mapping, and the study of signaling molecules in bone development and growth. The protocol requires experience with standard tissue processing and immunostaining techniques, and prior experience in tissue clearing and light-sheet imaging is beneficial but not essential. Key pointsO_LIA protocol for efficient clearing and immunolabeling of intact calcified tissues that enables superfast, high-resolution, and quantitative 3D imaging of various intact bones and teeth. C_LIO_LIThe entire protocol takes only 4 days to complete the comprehensive staining and perfect transparency throughout the intact bones, offering significant improvements over previous methods. C_LI Key referencesBiswas, L. et al. Cell 186, 382-397.e24 (2023): https://doi.org/10.1016/j.cell.2022.12.031

cell biology↗

bioRxiv2026

StructureSAFE: A structure-aware chemical language model for unified hit identification and lead optimization

Structure-based generative models (SBGMs) hold great promises for accelerating drug discovery by enabling target-aware molecular design. However, existing approaches face fundamental challenges: three-dimensional graph-based models can explicitly incorporate protein structural information but often generate chemically implausible molecules due to limited training data, while chemical language models (CLMs) produce chemically plausible molecules but struggle to effectively leverage three-dimensional structural information for structure-conditioned generation and hard to incorporate lead optimization functionality due to the nature of SMILES string. Here, we present StructureSAFE, a structure-aware chemical language model that resolves this trade-off by integrating protein structural and evolutionary encoders with the SAFE molecular representation via pretraining and finetuning training scheme, enabling both de novo hit identification and a comprehensive suite of lead optimization subtasks within a unified framework. Comprehensive benchmarking on the MolGenBench dataset demonstrates that StructureSAFE achieves state-of-the-art (SOTA) performance across multiple metrics, with particularly pronounced improvements in chemical plausibility relative to graph-based models lacking pretraining. Evaluation on a rigorously constructed held-out test set further confirms its ability to generate drug-like, synthetically accessible molecules with competitive predicted binding affinities for previously unseen targets on both hit identification and lead optimization setting. In silico case studies across four therapeutically relevant targets validate its capacity to generate chemically plausible molecules that recapitulate key binding interactions of known high-affinity ligands while proposing novel interactions for potential better affinity and exploring previously unknown regions of chemical space. Taking together, StructureSAFE represents a versatile and practical tool to provide high-quality candidate molecules for augmenting medicinal chemistry workflows in both hit identification and lead optimization campaigns.

bioinformatics↗

bioRxiv2026

CWF19L2 couples pre-mRNA alternative splicing with the maternal-to-zygotic transition to safeguard female fertility

The maternal-to-zygotic transition (MZT) requires precise spatiotemporal execution of pre-mRNA alternative splicing (AS), yet the core splicing machinery driving this developmental reprogramming remains incompletely understood. Here, we identify the CWF19-like protein 2 (CWF19L2) as an indispensable pre-mRNA AS regulator that safeguards against oocyte and early embryo competence defects (OECD). While murine germline-specific depletion of Cwf19l2 spares morphological folliculogenesis, oocyte maturation, or fertilization, it induces complete female sterility characterized by profound developmental arrest at the 2-cell stage. Mechanistically, maternal deficiency of CWF19L2 localized to nuclear speckles disrupts transcription-splicing-translation coupling, collapsing AS homeostasis during maternal reserves and zygotic genome activation. We further demonstrate that CWF19L2 orchestrates the pre-mRNA splicing network through directly binding to target transcripts and indirectly modulating via interacting with the core spliceosomal factor PRPF8. Importantly, exogenous Cwf19l2 mRNA partially rescues the embryonic arrest. Together, our findings establish CWF19L2 as an indispensable AS engine during the MZT, providing a mechanistic foundation for OECD and a novel molecular etiology for female infertility.

Cell Biology↗

bioRxiv2026

OmicOS: A Comprehensive Omics Ecosystem Infrastructure and Agent System for the AI Era

Biology has accumulated a vast ecosystem of omics methods, but much of this ecosystem remains built for expert humans rather than scientific agents. Methods are scattered across Python packages, R/Bioconductor and CRAN workflows, command-line tools, incompatible data containers and implicit object states, making even routine analyses difficult for an AI system to choose, execute and verify reliably. Here we introduce OmicOS, a comprehensive omics ecosystem infrastructure and agent system that turns OmicVerse V2, an open-source omics community, into an executable foundation for agentic biology. OmicVerse V2 provides the community substrate: scalable AnnDataOOM-compatible rust backends, agent-friendly Python algorithms for single-cell, spatial, bulk and multi-omics analysis, interfaces to single-cell foundation models, and Python-native reconstructions of historically R-centred Bioconductor/CRAN-style workflows. OmicOS makes this substrate actionable by registering analytical functions as state-aware capability contracts, allowing agents to inspect live data objects, select valid methods, execute controlled workflows and record provenance. The result is not a fixed pipeline, but a programmable omics environment in which agents compose real analyses from verified community methods rather than inventing tools. Across external and purpose-built benchmarks, OmicOS ranked first among the evaluated systems, reaching 81.2% on BiomniBench. Adding OmicVerse to a minimal agent improved task completion by up to 34.2 percentage points with qwen-3.6-35b, and controlled ablations showed that the gains came from registry-grounded execution rather than from larger models, documentation retrieval or unrestricted tool exposure. The same infrastructure scaled to atlas-sized data, reproduced R-centred workflows in Python and converted external pathology software into agent-usable skills. In a discovery task starting from a whole-body spatial map and the term "Alzheimers disease", OmicOS composed a non-canonical workflow that integrated spatial expression, genetic association, eQTL and colocalization evidence to nominate a colon epithelial risk axis centred on PICALM, CD2AP and CR1. Together, OmicVerse and OmicOS define an open foundation for AI-era omics, showing how a community of biological methods can be transformed into a reliable, extensible and agent-operable system for discovery. HighlightO_LIOmicVerse 2.0 consolidates 694 methods spanning 11 omics domains into agent-callable high-level APIs. C_LIO_LIRebuildR automatically reconstructs and evolves R/Bioconductor methods as Python-native implementations under output-equivalence gates. C_LIO_LIOmicOS establishes a state-of-the-art omics agent harness, ranking first on general omics benchmarks across models and substantially improving the analytical capability of local open-source models. C_LIO_LICompositional use of ecosystem modules nominates a colon epithelial axis associated with Alzheimers disease risk. C_LIO_LIExternal algorithm packages supporting automatic iterative evolution can be integrated into the OmicOS ecosystem. C_LI

bioinformatics↗

bioRxiv2026

STITCH links cellular morphology and gene expression in spatial transcriptomics

In situ spatial (ISS) sequencing can uncover co-variation between cellular morphology and gene expression in vivo. However, a principled and interpretable mathematical representation of morphology has not yet been applied in this context. In particular, current deep learning-based representations of cell images confound a cell's shape with its size. We present an interpretable representation of cellular boundary contours, based on tangent principal component analysis (TPCA) in a Kendall shape manifold, that captures size-independent contour shape features. This approach successfully recovers shape-perturbing genes in an RNAi screen than a previous metric geometry-based approach. We build on TPCA to develop KSTITCH (Kendall Shape-TranscriptomIc Correlation and Harmonization), an approach to reveal covariation between cell morphology with gene expression in ISS datasets. In a Xenium dataset, KSTITCH recovers known morphology-transcriptomic relationships in keratinocytes, macrophages and endothelial cells. Across samples in a melanoma CosMx dataset, KSTITCH reproducibly associates elongated and triangular fibroblasts with proximity to malignant cells and myofibroblast-like transcriptional program. Finally, KSTITCH independently recovers a known link between mesenchymal-like malignant cell states and increased cell area in two melanoma cohorts. KSTITCH can thus yield interpretable morphology-transcriptome relationships across cell types, patients, and spatial transcriptomics platforms. KSTITCH is available at https://github.com/vishakagopalan/kstitch .

bioinformatics↗

bioRxiv2026

TetraFuse: A Synergistic Four-Dimensional Dynamic Fusion Framework for Efficient and Robust Medical Image Classification

Accurate and robust classification of medical pathology images is pivotal for computer-aided diagnosis. However, the deployment of deep learning models in high-throughput clinical screening faces a fundamental challenge: the trade-off between diagnostic accuracy and computational efficiency. Current lightweight architectures, while reducing parameter complexity through grouped convolutions, often lead to cross-channel information isolation and diminished representational capacity. In this paper, we propose TetraFuse, a novel framework that systematically integrates features from four complementary domains: space, channel, statistics, and frequency. TetraFuse introduces a novel Cross-Channel Dynamic Aggregation (CCDA) paradigm that reconstructs global channel topology with negligible computational overhead, resolving the inter-group isolation issue. To balance perceptual fidelity and efficiency, we design a stage-aware local enhancement mechanism: Local Variance-Guided Enhancer (LVGE) is employed to filter out shallow-stage background noise, while High-Frequency Boundary Injection (HFBI) reinforces deep-stage pathological contours, preventing spatial over-smoothing. Experimental results on the COVID-19, ISIC 2018, and Kvasir datasets confirm that TetraFuse outperforms state-of-the-art (SOTA) methods. Notably, TetraFuse-Tiny achieves a transformative 91.53% reduction in FLOPs compared to ResNet50; on the Kvasir dataset, it achieved an accuracy of 0.926 and an AUC of 0.994 with only 0.345G FLOPs. By combining high representational power with minimal computational demand, TetraFuse offers a scalable solution for large-scale medical image analysis, especially in resource-constrained clinical environments.

bioinformatics↗

bioRxiv2026

Single-Cell Multi-Omics Dissection of Malignant Evolutionary Mechanisms and Construction of a Prognostic Model for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits pronounced heterogeneity across WHO histological grades, yet systematic single-cell multi-omics studies characterizing these transitions remain limited. We integrated scRNA-seq and scATAC-seq data across ccRCC WHO grades to establish a multi-omics framework encompassing tumor cells and immune populations. Using pseudotime trajectory analysis and machine learning ensembles, we developed a prognostic signature (CBG) from core nodes of transcriptional regulatory networks. We found that in tumor cells, epigenetic alterations consistently precede metabolic reprogramming and invasive adaptation. CD8+ T cell exhaustion followed a trajectory shifting from IRF7- to ZNF683-regulated states, while monocytes differentiated toward M1 and M2 macrophages orchestrated by NFIC/IL1B and CEBPD/GLI2. Intercellular communication networks showed a temporal progression from inflammation, through vascular remodeling, to immunosuppression dominance. The CBG signature demonstrated robust performance in independent cohorts, identifying SLC11A1 and SH3YL1 as antagonistic survival determinants. This study elucidates the dynamic molecular and immunological mechanisms underlying ccRCC grade progression, providing a robust framework for subtype-specific prognostication and precision therapeutic targeting.

bioinformatics↗