Biology subjects

Li, C.

Publications and source records attributed to Li, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Simultaneous multi-area recordings in macaque frontoparietal cortex during a manual object-interaction task

Dexterous hand control is essential for daily activities and a top restoration priority for people with tetraplegia, yet neurophysiological datasets suitable for studying and decoding naturalistic hand behavior remain scarce. Most available recordings constrain movements to a small number of degrees of freedom or cover only a single stereotypical grasp-and-hold structure. Moreover, previous brain-computer interface (BCI) studies have largely relied on signals from a single cortical area, even though dexterous hand control is distributed across an interconnected frontoparietal network. To address these limitations, we designed a paradigm that elicits rich hand movements across 40 distinct hand-object interaction conditions. We recorded simultaneous frontoparietal activity with hand kinematics from a rhesus macaque performing them. Neural activity was recorded from the primary motor cortex, anterior intraparietal area, and ventral premotor cortex using microelectrode arrays spanning 1,920 channels, while a multi-camera markerless motion-capture system acquired video from which kinematics were extracted offline. The dataset comprises data from 20 sessions, including processed neural features, synchronized 3D coordinates of hand and elbow keypoints, and trial-level metadata including task conditions and event timing. We further provide technical validation, including a neural decoding benchmark with multiple algorithms for continuous keypoint coordination reconstruction. We found that cross-day and cross-task decoding performance varied among algorithms. Variation in channel availability leads to different performance degradation curves in different algorithms. This task-rich dataset offers a valuable resource for investigating the neural mechanisms of dexterous hand control and for advancing the performance of motor BCIs.

neuroscience↗

bioRxiv2026

Explainable t-SNE for Single-Cell RNA-seq Data Analysis: A Cell-DrivenInformation Fusion Framework

Single-cell RNA sequencing fundamentally depends on defining cell-to-cell similarity to resolve distinct cell states. However, conventional dimensional-reduction frameworks such as t-SNE and UMAP inherently rely on Euclidean geometry, a construct ill-suited for high-dimensional, sparse count matrices, and produce cluster boundaries that lack biological interpretability. Here, we introduce cell-driven information fusion, an end-to-end framework that constructs cell affinity directly from raw counts by integrating three biological signals: gene detection patterns, expression magnitude of dominant transcripts, and global profile correlation. A sparsity-adaptive operator merges these signals into a unified affinity matrix, while an entry-usage diagnostic traces which biological component governs local cluster boundaries. The framework comprises two complementary implementations: c-TSNE for low-to-moderate sparsity (below 80 percent), which consistently outperforms Euclidean baselines across six benchmarks, and c-UMAP for high-sparsity regimes. Evaluated across 29 datasets and two protocols against 11 baseline methods, c-UMAP achieved the highest mean normalized and adjusted mutual information (NMI and AMI) in every setting, with all improvements remaining significant after Bonferroni correction. Because a single fixed weight vector was applied across all 29 datasets per protocol, these gains reflect intrinsic algorithmic robustness rather than dataset-specific hyperparameter tuning. Furthermore, c-UMAP scales seamlessly to 1.14 million cells, running up to 22-fold faster than the leading baseline. Crucially, by operating directly on raw counts without variance-based gene selection, our approach avoids the median 46.9 percent loss of top differentially expressed genes per cell class, preserving essential lineage markers such as IAPP and FOXP3. Together, these results demonstrate that atlas-scale efficiency, raw-data preservation, and mechanistic interpretability are fully compatible.

bioinformatics↗

bioRxiv2026

Stressed alga-free coral cells can initiate coral bleaching

Coral bleaching is driving the rapid decline of coral reefs worldwide, yet a fundamental question remains unresolved: does the bleaching cascade begin in the coral host or in its intracellular algal symbionts, Symbiodiniaceae? Resolving where this process starts is critical for understanding how symbiosis collapses and where early intervention might be effective. Here we use transient stress to create a temporal window between the early cellular response and subsequent symbiosis breakdown. Across transient menthol and thermal stress in soft and stony corals, bleaching developed after the stress had ended. In Xenia sp., single-cell transcriptomics revealed a broad host response before detectable symbiont loss, including coordinated redox changes consistent with conditions favouring reactive oxygen species (ROS) accumulation. Cell-resolved imaging showed that alga-free coral cells were the first to become ROS-positive, whereas Symbiodiniaceae showed no detectable ROS signal. This early host response was transmissible. Conditioned medium from stressed cultures induced the emergence of ROS-positive host cells and subsequent symbiosis breakdown in unstressed recipients. Purified ROS-positive alga-free coral cells alone were sufficient to trigger symbiosis breakdown in recipient cultures without direct stress exposure. Suppressing the early emergence of ROS-positive coral cells markedly reduced later bleaching, whereas intervention after stress was substantially less effective. These findings identify a host-initiated, intercellularly propagating route to coral bleaching and reveal an early host response that can determine the subsequent fate of the symbiosis.

cell biology↗

bioRxiv2026

The recoverable resolution of cellular perturbation-response prediction

Virtual cell models aim to predict how cells respond to perturbations, yet accurate prediction of post-perturbation gene expression can conceal failures to recover context-specific responses. Here we identify context geometry compression (CGC), in which models preserve cellular identity and shared perturbation effects while collapsing differences in how the same perturbation acts across contexts. Across biological systems, reproducible context-specific responses remained difficult to recover; in a large drug-response matrix, this persisted even with nearly complete perturbation coverage. The same drug-response predictions achieved higher normalized recovery in predefined pathway summaries than in gene-level response profiles, while coarse response programs were more predictable in human lymphoblastoid cell lines. Compact baseline transcriptomic representations retained most full-RNA predictive performance for patient-specific ex vivo drug responses. Together, these findings establish recoverable biological resolution, rather than measurement dimensionality, as a criterion for evaluating virtual cell predictions and matching model outputs to the biological detail supported by evidence.

bioinformatics↗

bioRxiv2026

DECIPHER integrates disentangled representation learning and prototype-based cell-type deconvolution across molecular modalities

The growing availability of single-cell resources creates new opportunities to extract cell-type-resolved information from the vast body of existing bulk omics data through cell-type deconvolution. Conventional deconvolution methods often rely on linear mixture models or specific probabilistic assumptions and can be sensitive to batch effects, whereas many deep-learning approaches are modality-specific or lack a unified end-to-end learning framework. Here, we developed DECIPHER, an end-to-end representation-learning framework for cell-type deconvolution that can be applied across multiple molecular modalities. DECIPHER learns a domain-constant representation (Zc) for deconvolution and a domain-specific representation (Zs) to model domain-associated variation. By integrating nonlinear representation learning with differentiable non-negative least-squares optimization, DECIPHER estimates cell-type proportions from Zc. Across simulated datasets, experimentally generated bulk-cell mixtures, real-world datasets, and multiple molecular modalities, DECIPHER showed robust and competitive cell-type deconvolution performance. Beyond cell-type proportion estimation, the learned Zc supported chronological age prediction across independent cohorts and prognostic stratification in lung adenocarcinoma, demonstrating that DECIPHER can transform high-dimensional bulk omics data into low-dimensional, biologically informative representations. DECIPHER thus broadens the utility of existing bulk omics resources for biological discovery and clinical research.

bioinformatics↗

bioRxiv2026

Decoding natural scenes from patterned optogenetic responses in mouse visual cortex

A central challenge in developing visual cortical prostheses is to determine how visual stimuli should be transformed into effective patterns of cortical stimulation. Although advances in stimulation technologies, including optogenetics, provide increasingly precise control over cortical activity, it remains unclear whether artificially evoked activity can reproduce the information content of naturally evoked visual representations. Here we establish a quantitative framework for evaluating visual encoding strategies by decoding cortical responses evoked by natural vision and patterned optogenetic stimulation. We developed a novel dual-modal paradigm in awake mice to bridge the gap between endogenous photostimulation and artificial network driving. By co-expressing the high-performance calcium indicator GCaMP6s and the red-shifted, ultra-sensitive opsin rsChRmine-oScarlet in the primary visual cortex (V1), we successfully translated dynamic natural movie frames into patterned, spatiotemporal optogenetic stimulation. Quantitative comparisons of macro-scale dynamics demonstrated that this patterned optogenetic injection evokes cortical states highly comparable and representationally aligned with those driven by actual visual photostimulation. To systematically evaluate the fidelity of these responses, we developed STAR, a deep learning model featuring spatial and temporal attention mechanisms, and successfully reconstructed the frames of natural movies from V1 signals under both experimental modalities. Collectively, our results demonstrate that complex sensory information can be both naturally encoded and synthetically injected into V1 circuits with high decoding fidelity. This work provides an empirical and computational proof-of-concept for intelligent, closed-loop biomimetic encoders, establishing a robust framework for next-generation cortical visual neuroprostheses and bidirectional brain-machine interfaces.

neuroscience↗

bioRxiv2026

PTMExplorer: A Multi-Dimensional Integrative Visualization Platform for Protein Post-Translational Modification Function and Structure

Deciphering the functions of post-translational modifications (PTMs) is a critical bridge connecting large-scale modification proteomics data to mechanistic studies. However, most existing tools for visualizing PTM omics data are limited to site catalogs or single-dimensional feature displays. They lack the capability to simultaneously map user-derived differential modification sites onto multi-dimensional contexts, including protein three-dimensional (3D) structure, evolutionary conservation, functional sites, and disease associations. This limitation makes it difficult for researchers to rapidly assess the biological importance of candidate sites from among a vast number of differentially modified sites. Here, we present PTMExplorer, an interactive platform for the multi-dimensional visualization of protein PTMs. PTMExplorer comprises three core modules: PTM Inspector, built upon ProtVista, provides a multi-track, sequence-feature integrated view incorporating intrinsically disordered region (IDR) prediction (via flDPnn), surface accessibility calculation (via FreeSASA), and UniProt functional annotations; PTM 3D Locator, leveraging the Nightingale/Mol* engine, anchors modification sites onto AlphaFold/Protein Data Bank (PDB) 3D structures through residue mapping via PDBe-SIFTS; and PTM Overview, utilizing the R circlize package, presents a panoramic polar circos plot illustrating modification distribution and inter-group differential regulation. Additionally, three major disease-associated modification databases (PTMD, qPTM, and PhosCancer) are integrated as PTM-Disease Nexus, enabling co-localization comparison between user-defined differential sites and reported disease-related sites. PTMExplorer currently supports eight model organisms, accepts user-uploaded differential analysis results, and provides multi-dimensional annotations and various visualization options (https://www.bioladder.cn/PTMExplorer/). Using a multi-omics dataset from hepatocellular carcinoma (18 patients, 9 modification types) as a case study, we demonstrate the practical utility of PTMExplorer in screening potential biomarkers, revealing multi-modification coordination mechanisms, and distinguishing between absolute and relative quantification patterns.

bioinformatics↗

bioRxiv2026

Multiomics Characterization Identifies S1P-secreting USSHigh Skeletal Muscle Stem Cells as Essential Drivers of Niche Remodeling and Muscle Regeneration

Acutely induced cellular senescence is increasingly recognized as vital for tissue repair but the precise roles of senescent cells in skeletal muscle regeneration remain paradoxical. Here, using single-cell RNA sequencing and a Unified Senescence Score (USS) methodology, we identify a distinct USSHigh (USSHi) muscle stem cell (MuSC) subpopulation emerging in early phase of acute injury induced muscle regeneration. Isolation via the surface marker MHCII enables characterization of these cells as exhibiting canonical senescent-like features. Further characterization through integrated transcriptomics, proteomics, and secretomics reveals their highly pro-inflammatory secretory capacity. Moreover, lipidomic profiling uncovers that the USSHi MuSCs undergo profound sphingolipid metabolic remodeling to generate upregulated bioactive lipid Sphingosine-1-phosphate (S1P). In vivo specific ablation of this subpopulation using a Pax7-CreERT; Ccl2-LSL-DTA genetic strategy significantly impaired muscle regeneration. Furthermore, spatial transcriptomics and cell-cell communication analyses revealed that USSHi MuSCs act as critical niche modulators, directing crosstalk with multiple cell types. Specifically, S1P is essential for mediating direct USSHi interaction with endothelial cells and USSLo cells to promote endothelial angiogenesis and MuSC expansion in the regenerating muscle. Crucially, exogenous S1P rescues the regenerative deficits caused by USSHi MuSC ablation. Altogether our findings define a senescent-like MuSC population emerging in acute injury induced muscle regeneration and provide definitive evidence to demonstrate the positive role of these cells in enhancing muscle repair via their robust secretory phenotypes and inter-cellular communicative capacity.

cell biology↗

bioRxiv2026

Physiological signatures but no evidence for universal genomic predictors of extreme drought responses in European beech

Following a severe 2018 drought affecting much of Europe, European beech (Fagus sylvatica) exhibited patchy canopy dieback. To investigate the drivers of this mortality, we used a paired case-control design across Swiss forests, integrating phenotypic assessments and genomic analyses. Phenotypically, crown transparency and discoloration were used to classify tree damage state, while several other characteristics were measured to evaluate whether paired, neighboring damaged and undamaged trees were otherwise comparable, and to identify physiological stress signatures. Elevated leaf calcium content and red edge reflectance in dried leaves emerged as robust indicators of physiological stress. Genomic association models using reference-based SNP markers and reference-free k-mers were heavily confounded by micro-environmental variance and local population stratification even within our geographically limited study region. Our cross-validated genomic outliers showed no spatial overlap with predictive drought-associated loci previously identified in an independent German cohort. These findings indicate that drought survival in European beech is not governed by a universal set of major-effect alleles. Instead, tree performance appears highly context-dependent, likely shaped by micro-environmental heterogeneity and phenotypic condition. However, our results do not exclude polygenic drought responses involving multiple small-effect loci.

ecology↗

bioRxiv2026

DNMT1-Mediated Epigenetic Reprogramming Drives KMT2A Amplifications and Rearrangements

MLL/KMT2A amplifications and rearrangements are prevalent in infant, adult, and therapy-induced leukemia; however, the molecular contributors controlling these alterations remain elusive. Here, we reveal that rapid CTCF degradation generates local genome structure alterations at KMT2A and copy gains and rearrangements. We then established a conserved, coordinated interplay between DNA and histone methylation pathways that control CTCF occupancy, and in turn, KMT2A locus stability. For example, DNMT1 overexpression promotes increased H3K9 methylation, reduced CTCF occupancy, and causes KMT2A alterations. However, DNMT1 inhibition suppresses these events and KMT2A alterations caused by topoisomerase II inhibition. Locus-specific epigenome targeting demonstrated that DNA or H3K9 methylation promotes KMT2A copy gains and rearrangement, whereas targeted TET activity suppresses these events upon methylation perturbation or doxorubicin treatment. These findings identify a conserved, coordinated DNA-histone methylation axis governing KMT2A amplifications and rearrangement susceptibility, revealing biomarkers and therapeutic targets to predict and intercept these events in cancer.

cancer biology↗

bioRxiv2026

Field-Based Characterization of Temporal Interference Stimulation: Beyond the Target-Centric Perspective

Temporal interference (TI) stimulation is a promising non invasive technique for deep brain neuromodulation, yet accurately characterizing its complex and spatially distributed electric fields remains a fundamental challenge. This study proposes a novel computational framework that integrates topological analysis with mesh-aware metrics to overcome the limitations of conventional global field descriptors. The method introduces three interconnected components: (1) connected component , which extracts spatially continuous field regions above thresholds; (2) discrete ellipsoid modeling, which quantifies the geometric centroid, directional spread, and anisotropy of each region; and (3) Local Moran's I to identify robust spatial clusters while suppressing numerical noise. Applied to TI simulations using individualized tetrahedral head models, the framework demonstrates that the few largest connected components above an elevated intensity threshold collectively provide a more accurate representation of the stimulation targets. The approach provides highly interpretable visual outputs that directly map field topology to brain anatomy, including layered component maps, discrete ellipsoids, and spatially weighted clustering landscapes. By offering a topologically coherent, noise robust, and visually intuitive analytical toolbox, this work advances the precision and interpretability of TI field assessment, supporting more reliable target localization, focality quantification, and parameter exploration in translational neuromodulation research.

neuroscience↗

bioRxiv2026

A Variational Modeling Framework for Population Genetic Dynamics

Population genetic dynamics are shaped by multiple evolutionary processes, including mutation, recombination, selection, and genetic drift. With the rapid growth of genomic data, modeling multilocus evolutionary dynamics and the resulting patterns of genetic variation has become increasingly important. Existing approaches often face challenges in jointly describing multiple evolutionary processes and modeling multilocus systems, motivating the development of more flexible and extensible frameworks. Here, we develop a variational framework for population genetic dynamics based on generalized gradient-flow theory, drawing on nonequilibrium thermodynamics. In our model, mutation, recombination, and selection are represented as modular variational components, with different life-cycle stages connected through a gamete-individual two-state system. Mutation and recombination act on the gamete state, selection acts on the individual state, and finite-population genetic drift is represented by a stochastic extension. Our model represents multilocus genetic variation directly in the full haplotype-frequency space, recovers classical mutation, recombination, and selection dynamics in the corresponding limits and provides a natural stochastic extension for finite populations. Numerical experiments and SLiM forward simulations show that our model captures the dynamics of allele frequencies, haplotype frequencies, and linkage disequilibrium in multilocus systems. The variational formulation opens avenues for future extensions to additional evolutionary processes and more complex multilocus systems, as well as for developing differentiable computational methods for gradient-based parameter inference and scalable genomic modeling.

evolutionary biology↗

bioRxiv2026

Phenolic Chemical Defense Contributes to Resistance against Multiple Soybean Cyst Nematode Populations in Wild Soybean

Soybean cyst nematode (Heterodera glycines, SCN) is one of the most damaging pathogens of soybean worldwide. Virulence diversity among SCN populations and reliance on a limited number of resistance sources present major challenges for durable SCN management. Here, we investigated a wild soybean (Glycine soja) genotype (WsR), that is resistant to two SCN populations, race 2 (HG type 1.2.5.7) and race 5 (HG type 2.5.7), and compared its transcriptional and metabolic responses with those of the susceptible genotype (WsS). WsR exhibited a substantially stronger defense-associated transcriptional response to both SCN populations, including preferential induction of genes associated with Ca2+/calmodulin and salicylic acid signaling. Integration of transcriptomic and time-resolved metabolomic analyses revealed convergence on phenolic metabolism, with enhanced accumulation of phenolic acids, flavonoids, and isoflavonoids in resistant WsR. Genes involved in phenolic biosynthesis and modification were also preferentially induced in WsR, linking transcriptional reprogramming with the observed metabolic response. Importantly, two resistance-associated phenolic compounds, 4-hydroxybenzaldehyde and 2,3-dihydroxybenzoic acid, directly increased mortality of SCN second-stage juveniles from both populations in a concentration-dependent manner. By connecting resistance-associated transcriptional responses and metabolic reprogramming with direct activity of specific phenolic compounds against SCN, our study provides functional evidence linking phenolic metabolism to chemical defense against SCN. Together, these findings identify enhanced phenolic chemical defense as a major component of resistance to multiple SCN populations in wild soybean and highlight wild soybean as a valuable source of molecular and biochemical diversity for SCN resistance.

plant biology↗

bioRxiv2026

In vivo HSPC gene therapy of hemoglobinopathies without drug selection of corrected cells

In vivo hematopoietic stem/progenitor cell (HSPC) gene therapy remains limited by low gene-editing efficiency and a lack of clinically applicable selection strategies to enrich therapeutically corrected progeny. We used in vivo base and prime editing to introduce a nonpathogenic EPOR variant into HSPCs, conferring erythropoietin hypersensitivity and promoting preferential expansion of gene-corrected erythroid cells. EPOR editing was combined with three therapeutic approaches for the correction of hemoglobinopathies: {gamma}-globin gene addition, {gamma}-globin reactivation, or correction of the sickle cell disease mutation. Tropism-modified helper-dependent adenoviral vectors (HDAd6/3+) targeting HSPCs were used to simultaneously deliver the EPOR-editing machinery and the corresponding therapeutic components. In vitro studies in an erythroid progenitor cell line and primary CD34+ cells demonstrated that the EPORW439* variant conferred a strong proliferative advantage to therapeutically modified erythroid progenitors. Mice humanized with CD34+ cells from a {beta}/{beta}-thalassemia patient were subjected to EPORW439*-mediated EPO hypersensitivity alongside a therapeutic {gamma}-globin transgene which resulted in >70% HbF-positive erythroid cells and substantial reversion of the disease-associated phenotype, including reduced oxidative stress, near-complete elimination of splenic iron deposition, and reduced splenomegaly. Importantly, these effects were achieved after simple intravenous administration of the vectors following HSPC mobilization and cytokine prophylaxis, without subsequent pharmacologic selection. Together, these findings establish a strategy to amplify the therapeutic benefit of otherwise limited in vivo HSPC gene editing for hemoglobinopathies.

bioengineering↗

bioRxiv2026

A J-protein-licensed CHD3 chromatin remodeling complex promotes H3K27me3 spreading to maintain cell identity in plants

Stable inheritance of epigenetic states is essential for cellular identity and developmental robustness and requires ATP-dependent chromatin remodeling, yet the molecular composition and regulatory mechanisms of plant CHD3 chromatin remodelers have remained unknown. Here we show that the Arabidopsis CHD3 remodeler PICKLE (PKL) assembles with a defined set of J-domain proteins (JDPs) to form stable CHD3 chromatin remodeling complexes in plants that are compositionally distinct from their animal counterparts. Loss of JDPs phenocopies pkl mutants, and JDPs are required for nucleosome densification and H3K27me3 spreading at thousands of Polycomb target genes. Mechanistically, JDPs perform dual functions by promoting PKL protein stability while directly stimulating PKL chromatin remodeling activity through their plant-specific DUF3444 domain that binds the N-terminal tail of histone H3. Deletion of the DUF3444 domain severely impairs PKL remodeling activity without disrupting complex assembly, leading to defective H3K27me3 spreading, impaired facultative heterochromatin maintenance, and loss of cell identity. Together, our findings define plant CHD3 chromatin remodeling complexes and reveal a mechanism that regulates CHD3-mediated chromatin remodeling to support the propagation of repressive chromatin states in plants.

plant biology↗

bioRxiv2026

ImmuneLens: linking transcriptional states and TCR clonotypes through disentangled multimodal learning

Single-cell multi-omics technologies simultaneously capture the transcriptome and TCR sequence of T cells, providing an opportunity to study the relationship between transcriptional states and clonal architectures. However, jointly modeling the relationships between transcriptional states and TCR sequences while preserving modality-specific information remains challenging. Here, we present ImmuneLens, an interpretable multimodal representation learning framework designed for paired single-cell transcriptome and TCR sequence data. ImmuneLens supports the construction of a transferable multi-cohort immune reference atlas and enables unsupervised mapping of external query data. The complementarity between GEX and TCR information improves the stability of antigen-specificity prediction. In neoadjuvant immunotherapy cohorts, ImmuneLens resolves response-associated T cell heterogeneity and reveals links between clonal expansion and CD8 T cell functional states. Overall, ImmuneLens provides a

bioinformatics↗

bioRxiv2026

Predicting Speech in Noise Perception using Measures of Neural Entrainment

Speech perception ability in a cocktail party environment is highly variable, even for people with clinically healthy hearing. The mechanisms of these differences are poorly understood. A possible reason for these differences could be due to the neural mechanisms of auditory attention. This investigation aims to quantify how well a person is able to neurally entrain to a continuous narrative using a temporal response function (TRF), then evaluate if that TRF measure is correlated with an individual's performance on a selective attention and narrative comprehension task. This was accomplished using a cohort of twenty-five young and healthy listeners who completed speech-in-noise (SIN) perception tasks while their neural activity was recorded with EEG. This EEG data and regressors (speech envelope and gammatone spectrogram) of an auditory stimulus was used to train TRF models. TRF cross validation correlation coefficients were then used in multiple regression models to predict a participant's SIN performance. Task performance had a significant positive relationship with the neural entrainment to an attended stimulus and a negative relationship with the neural entrainment to an unattended stimulus. This was observed with both the selective attention and narrative comprehension measures. The difference of the attended and unattended correlation coefficients were also significantly related to increased task performance. These measures could be a potential biomarker to predict an individual's speech perception in a noisy environment.

neuroscience↗

bioRxiv2026

labapi: a Python object model for the LabArchives electronic lab notebook

labapi is a Python library that enables computational workflows to connect to LabArchives' electronic lab notebook (ELN). Without an Application Programming Interface (API) connection, researchers must manually add workflow outputs through the LabArchives web interface, navigating to the appropriate page and uploading each output so that it appears with the experimental notes that provide context. labapi translates the flat LabArchives API into a Python object model following the existing hierarchy of the web interface, allowing workflows to navigate and modify notebook content through familiar paths. labapi enables researchers to build interconnected workflows that both read in and write data to LabArchives' ELN automatically. Researchers can inspect those outputs in the notebook, and later analysis code can read them back for another stage of analysis.

bioinformatics↗