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PubMed2025

Inference and visualization of complex genotype-phenotype maps with gpmap-tools.

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet our knowledge of genotype-phenotype maps in natural systems is limited due to the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs), along with large collections of natural sequences, offer new opportunities to empirically characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analysis of these high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library that integrates a series of models for inference, phenotypic imputation, and error estimation from MAVE data or collections of natural sequences in the presence of genetic interactions of every possible order. gpmap-tools also provides methods for summarizing patterns of epistasis and visualization of genotype-phenotype maps containing up to millions of genotypes. To demonstrate its utility, we used gpmap-tools to infer genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence from both genomic 5'UTR sequences and experimental MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, opening new avenues for understanding the architecture of genetic interactions and their evolutionary consequences.

Gaussian process↗

PubMed2025

DeNoFo: a file format and toolkit for standardised, comparable de novo gene annotation.

MOTIVATION: De novo genes emerge from previously non-coding regions of the genome, challenging the traditional view that new genes primarily arise through duplication and adaptation of existing ones. Characterised by their rapid evolution and their novel structural properties or functional roles, de novo genes represent a young area of research. Therefore, the field currently lacks established standards and methodologies, leading to inconsistent terminology and challenges in comparing and reproducing results. RESULTS: This work presents a standardised annotation format to document the methodology of de novo gene datasets in a reproducible way. We developed DeNoFo, a toolkit to provide easy access to this format that simplifies annotation of datasets and facilitates comparison across studies. Unifying the different protocols and methods in one standardised format, while providing integration into established file formats, such as fasta or gff, ensures comparability of studies and advances new insights in this rapidly evolving field. AVAILABILITY AND IMPLEMENTATION: DeNoFo is available through the official Python Package Index (PyPI) and at https://github.com/EDohmen/denofo . All tools have a graphical user interface and a command line interface. The toolkit is implemented in Python3, available for all major platforms and installable with pip and uv.

Journal Article↗

PubMed2025

Mapping Allosteric Communication in the Nucleosome with Conditional Activity.

The nucleosome core particle (NCP) regulates genome accessibility through dynamic allosteric communication between histone proteins and DNA. Building on the concept of conditional activity introduced by Lin (2016), we use molecular dynamics simulations and develop an open-source Python library, CONDACT (CONDitional ACTivity), to quantify time-resolved kinetic correlations in nucleosome systems. We analyze long-time simulations of the nucleosome core particle, including two different DNA sequences, the Widom-601 and ASP (alpha-satellite palindromic) sequences. By tracking dihedral angle transitions, we identify residues with high dynamical memory and map inter-residue communication pathways across histone subunits and DNA. Our analysis reveals kinetically connected domains involving post-translational modification sites, oncogenic mutation sites, and DNA contact regions, with dynamic coupling observed over distances up to 7.5 nm. These findings offer new insight into the long-range allosteric behavior of the nucleosome and its potential role in regulating chromatin accessibility. Quantifying this allosteric behavior potentially identifies targetable residues and domains for therapeutic intervention.

Journal Article↗

PubMed2025

A Systematic Review of Spatial Epidemiological Modeling Approaches Applied During the COVID-19 Pandemic.

BACKGROUND: A wide range of epidemiological modeling approaches have been applied to the SARS-CoV-2 pandemic, which presents an opportunity to assess common approaches applied to specific research questions. Spatial models interrogate how heterogeneities and host movement dynamics influence local and regional patterns of disease, issues that were of great interest for understanding and controlling SARS-CoV-2. OBJECTIVE: Here we present a systematic review of spatial epidemiological modeling approaches of SARS-CoV-2. We describe common themes and highlight unique strategies, providing a foundation for researchers to devise spatial models most appropriate for future pathogens and epidemics. Our review also categorizes the research questions that were addressed with spatial models, highlights parameter estimation techniques, and describes the cyber infrastructure used for model development. METHODS: We conducted a systematic review using Web of Science and a standardized set of keywords, followed by thorough examination of abstracts and full texts to determine which studies met our inclusion criteria. To guide our description and comparisons of models, we developed a Geography, Population, Movement (GPM) framework that conceptualizes the interactions between three distinct subcomponents of any spatial model. The geographic model represents the physical arena in which the model is implemented, the intra-population model describes the transmission and disease processes that occur within distinct spatial units of the geography, and the movement model describes the algorithms that dictate how hosts move among spatial units within the geography. RESULTS: The search identified a total of 193 articles, of which 109 were included in our review. The most abundant intra-population modeling methods were agent-based (47.7%) and compartmental modeling (29.4%) approaches. Movement models ranged in complexity, with the most complex models implementing commuter movement among many points of interest in the geographic arena, which were sometimes parameterized by fine-scale mobility data. Geographic models ranged from describing microcosms, such as single classrooms, all the way up to multi-country models. Of the 63.3% of models studies that specified the programming language used, we detected ten different languages, with Matlab and Python being the most frequent, although only 30.6% of studies provided open-access code for their models. We also described eight specialized software systems that were used to construct agent-based or compartment models of COVID-19. CONCLUSIONS: Our review identified and characterized a variety of spatial modeling strategies and software that were usefully employed to address many relevant epidemiological questions for COVID-19. Future research is needed to quantitatively assess which modeling approaches are most appropriate in specific situations, to answer specific questions, or to apply to certain disease systems. Moreover, future cyberinfrastructure could help to modularize and standardize modeling approaches, which would increase transparency and reproducibility, and which would facilitate a detailed examination of which model attributes relate to model performance in a variety of contexts.

COVID-19↗

PubMed1990

Shed snake skin and hairless mouse skin as model membranes for human skin during permeation studies.

Difficulties in obtaining and using human skin have tempted many workers to employ animal membranes for percutaneous absorption studies. We have investigated the suitability of two species of snake (Elaphe obsoleta, Python molurus) for this purpose and compared our in vitro experimental results for human skin and for hairless mouse, a currently popular model. The effects of long-term hydration on the membranes were investigated over 8 d using tritiated water as a model permeant. The initial permeability coefficients of all the membranes were similar (0.74-2.2 X 10(-3) cm 2h-1). Although the human and squamate skins did not change significantly over the test period, the permeability of hairless mouse skin increased 37 times. The actions of typical enhancers on the permeabilities of the membranes to a model penetrant 5-fluorouracil (5-FU) were tested using 3% Azone in Tween 20/saline, propylene glycol (PG), 2% Azone in PG, and 5% oleic acid in PG. While the data from snake membranes tended to underestimate the enhancer effects, those from hairless mouse skin greatly overestimated the changes. None of the membranes was a completely reliable model for assessing human percutaneous absorption as modified by accelerants. Pretreatment with acetone did not significantly change the permeability of human or squamate skins to 5-FU, although that of hairless mouse increased twentyfold. An overall conclusion is that, wherever possible, human skin should be used in absorption studies and not hairless mouse or snake skin; otherwise, misleading results may be obtained.

Acetone↗

PubMed1975

Human infestation by Ophionyssus natricis snake mite.

A family presented with a papular vesiculo-bullous eruption of the skin, found to be caused by the snake mite, Ophionyssus natricis (Cervais, 1844). A pet python was the primary host. Treatment of the animal and its environment led to clearance of the human skin lesions.

Adult↗

PubMed2000

A fossil snake with limbs.

A 95-million-year-old fossil snake from the Middle East documents the most extreme hindlimb development of any known member of that group, as it preserves the tibia, fibula, tarsals, metatarsals, and phalanges. It is more complete than Pachyrhachis, a second fossil snake with hindlimbs that was recently portrayed to be basal to all other snakes. Phylogenetic analysis of the relationships of the new taxon, as well as reanalysis of Pachyrhachis, shows both to be related to macrostomatans, a group that includes relatively advanced snakes such as pythons, boas, and colubroids to the exclusion of more primitive snakes such as blindsnakes and pipesnakes.

Animals↗

PubMed1980

Monkey responses to three different alarm calls: evidence of predator classification and semantic communication.

Vervet monkeys give different alarm calls to different predators. Recordings of the alarms played back when predators were absent caused the monkeys to run into trees for leopard alarms, look up for eagle alarms, and look down for snake alarms. Adults call primarily to leopards, martial eagles, and pythons, but infants give leopard alarms to various mammals, eagle alarms to many birds, and snake alarms to various snakelike objects. Predator classification improves with age and experience.

Animal Communication↗

PubMed2000

The Ensemble/Legacy Chimera extension: standardized user and programmer interface to molecular Ensemble data and Legacy modeling programs.

Ensemble/Legacy is a toolkit extension of the Object Technology Framework (OTF) that exposes an object oriented interface for accessing and manipulating ensembles (collections of molecular conformations that share a common chemical topology) and driving Legacy programs (such as MSMS, AMBER, X-PLOR, CORMA/MARDIGRAS, Dials and Windows, and CURVES). Ensemble/Legacy provides a natural programming interface for running Legacy programs on ensembles of molecules and accessing the resulting data. Using the OTF reduces the time cost of developing a new library to store and manipulate molecular data and also allows Ensemble/Legacy to integrate into the Chimera visualization program. The extension to Chimera exposes the Legacy functionality using a graphical user interface that greatly simplifies the process of modeling and analyzing conformational ensembles. Furthermore, all the C++ functionality of the Ensemble/Legacy toolkit is "wrapped" for use in the Python programming language. More detailed documentation on using Ensemble/Legacy is available online (http:¿picasso.nmr.ucsf.edu/dek/ensemble. html).

Computer Graphics↗

PubMed1980

Dorsal root projections in various types of reptiles.

The distribution of dorsal root fibers into the spinal cord as well as to the brainstem have been studied in various types of reptiles. At the site of entrance into the spinal cord no clear segregation of large fibers medially and smaller fibers laterally has been observed. A peculiarity for reptiles seems to be a lateral bundle of primary afferent fibers which traverses the dorsal part of the lateral funiculus. The fibers of this bundle enter the spinal gray at the lateral side of the dorsal horn. Notable variation in the distribution of dorsal root fibers has been observed in the reptiles studied. In the turtle Testudo hermanni and in the snake Python reticulatus almost no fibers were found to extend into the ventral horn. However, in the lizard Tupinambis nigropunctatus a distinct projection into the ventral horn was observed. This closer potential coupling between the primary input and output systems of the spinal cord than in the turtle Testudo hermanni seems to be related to the long multijointed digits in the lizard studied which give its limbs a marked prehensile character.

Afferent Pathways↗

PubMed1987

A comparative study on glyoxalase II from vertebrata.

S-2-hydroxyacylglutathione hydrolase (glyoxalase II) from the liver of animals belonging to the various vertebrate classes (Oryctolagus cuniculus, Gallus gallus, Python molurus, Rana esculenta, Esox lucius) have been purified from 100,000 g supernatants of liver homogenates, using acetone fractionation and affinity chromatography. Subsequent comparative studies were concerned with some molecular and kinetic properties. Isoelectric focusing gave evidence for a single form of liver glyoxalase II in O. cuniculus, P. molurus and E. lucius, while the enzyme from G. gallus and R. esculenta showed respectively two and three forms with different pI values. All studied enzymes are basic proteins. The relative molecular mass values range from 18,000 to 23,000. The various glyoxalases II do not display markedly different Kn or Ki values. Their stability behavior at different temperatures is also quite similar.

Animals↗

PubMed1985

Localization of immunoreactive synthetic atrial natriuretic factor (ANF) in the heart of various animal species.

The localization of two synthetic fragments of the C-terminal portion of atrial natriuretic factor: Arg 101-Tyr 126 which displays full biological activity and Leu 94-Arg 109 which is completely devoid of biological activity, has been investigated by immunohisto- and immunocytochemical methods in the heart of mammals (rat, mouse, guinea pig, hamster, rabbit, cat, dog, man) and nonmammalian vertebrates toad (Bufo marinus), frog (Rana catesbeiana), fish (Cyprinus carpio, Puntius schwanenfeldi, Cichlosoma biocellatum, Carrasius auratus), snake (Python reticulatus) and hen. Antibodies against the synthetic fragments of ANF were raised in rabbits and used either for immunofluorescence (Coons' technique), immunohistochemistry (unlabeled antibody technique) or immunocytochemistry (protein A-gold technique). Results obtained by immunofluorescence and by the unlabeled antibody technique were similar: antibodies against Arg 101-Tyr 126 ANF allowed visualization of granulated cardiocytes in the atria of all mammals. While the reaction was very strong in rat and mouse, it was less so in the rabbit and very weak in all other species studied including man. Antibodies against Leu 94-Arg 109 ANF produced a reaction only in the rat and mouse. In nonmammalian vertebrates, the reaction was always much stronger in atria than ventricles of all species with both antibodies.

Animals↗

PubMed2026

Comprehensive in silico genomics analysis of global trends and host-specific emergence of aminoglycoside resistance in Staphylococcus aureus: a One-Health perspective.

BACKGROUND: Aminoglycosides remain clinically valuable against Staphylococcus aureus. Aminoglycoside resistance in S. aureus represents a critical One Health concern and is primarily driven by aminoglycoside-modifying enzymes (AMEs), which are frequently plasmid-encoded. Although regional studies have provided valuable insights, the global epidemiology of aminoglycoside resistance determinants remains poorly characterized because comprehensive data integrating human, animal, and environmental reservoirs are still lacking. This study addresses this gap by analyzing over 110,000 S. aureus genomes (2000-2025) to map the global resistome, quantify temporal and host-specific trends, and assess the association between genetic determinants and phenotypic resistance. METHODS: We performed a retrospective One Health meta-analysis of 110,309 S. aureus genomes collected between 2000 and 2025 from 128 countries. Genomes were quality-filtered and aminoglycoside resistance determinants were identified using NCBI AMRFinderPlus (v4.0.23). Multilocus sequence typing and host-source harmonization (Human, Animal, Environment, Unknown) enabled clonal and reservoir stratification. Temporal trends in gene prevalence and resistance burden were modeled with robust regression. Geographic and host-associated structuring of key genes was assessed via χ2 and enrichment tests. Machine-learning models (elastic-net, random forests, XGBoost) were benchmarked for minimum inhibitory concentration (MIC) prediction via nested cross-validation, with performance evaluated by mean absolute error, RMSE, and SHAP-based feature importance. All analyses were conducted in R and Python using publicly available, de-identified genomic data. RESULTS: Aminoglycoside resistance-associated genes were dominated by modifying enzyme determinants, with ant(6)-Ia, ant(9)-Ia, aph(3')-IIIa, sat4, aadD1, and aac(6')-Ie/aph(2'')-Ia occurring in 14-22% of isolates worldwide. Temporal analysis revealed significant declines in several major determinants, most notably ant(9)-Ia (-2.22 percentage points per year, p < 0.001), whereas apmA exhibited a non-significant decreasing trend in animal isolates. Host structuring was marked: human clinical isolates concentrated common determinants, while animal and environmental isolates harbored rare alleles (apmA, spw, str, spd). Geographic mapping confirmed near-universal distribution of common genes but focal restriction of rare ones. Publicly available phenotypic data indicated strong activity of amikacin, whereas gentamicin showed a distinct resistant subpopulation that closely corresponded with AME gene carriage. Genotype-phenotype analyses demonstrated strong concordance, with gene-rich complements predicting resistant MIC strata and absence of determinants predicting susceptibility. Analysis across different gene classes revealed frequent co-occurrence of aminoglycoside resistance genes with determinants from other classes, such as mecA, blaZ, and MLS_B, embedding them within multidrug-resistant (MDR) genomic contexts. CONCLUSION: Over 25 years, the prevalence of aminoglycoside resistance-associated genes in S. aureus has declined for several common determinants, while rare veterinary-linked alleles are emerging in animal isolates. Strong genotype-phenotype concordance supports genomic prediction for gentamicin and amikacin, where MIC data are available, although phenotypic confirmation remains essential. The frequent co-occurrence of aminoglycoside resistance genes with other antimicrobial resistance determinants indicates their integration within co-occurrence patterns of MDR genes, defined here as clusters of co-occurring resistance genes often carried on shared mobile genetic elements. These patterns highlight the need for integrated One Health surveillance combining clinical, veterinary, and environmental monitoring with plasmid-context resolution to anticipate emerging threats.

Aminoglycosides↗

PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗

PubMed2026

'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome.

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed 'PePApipe', a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

African Swine Fever Virus↗

PubMed2026

REAPER: a project-centric workflow layer for comparative repeatome analysis.

INTRODUCTION: Repeatome characterization from short-read sequencing data is widely performed using RepeatExplorer2/TAREAN. However, long-lived multisample projects and explicit comparative designs are often executed as ad hoc command sequences that are hard to version, rerun, and monitor on shared compute environments - a gap that motivates a project-centric workflow layer for repeatome analysis. METHODS: We present REAPER (Repeatome Extended Analysis Pipeline-Execution and Reporting), a project-centric workflow layer that couples a modular Snakemake pipeline with a Python project manager to enforce a stable on-disk layout and configuration-driven execution for single-sample and comparative repeatome analyses. REAPER does not implement a new repeat-discovery algorithm; it is an orchestration layer, and biological accuracy for clustering and satellite calling depends on the underlying RepeatExplorer2/TAREAN and satMiner methods it coordinates. REAPER standardizes: Read QC Deterministic subsampling and preparation RepeatExplorer2/TAREAN execution via seqclust, with satMiner-inspired iterative assembly Post-TAREAN BLAST-based annotation against curated repeat collections (optionally including taxon-scoped NCBI-derived resources with freshness checks) Optional graph-based comparative reports The pipeline makes comparative read allocation, prefix policy, and analysis-ready tables explicit; caching supports incremental reruns and structured logs support monitoring. Performance was assessed using a Triticeae short-read dataset (five samples), with rule-level logging of runtime and memory across pipeline stages. RESULTS: Rule-level performance logs show that graph-based clustering dominates runtime and memory, while QC and preparation steps are lightweight by comparison. Graph-report annotations for the Triticeae project additionally link high-ranking clusters to established repeat markers - including pTa794- and pSc119-class entries in curated databases. DISCUSSION: These findings illustrate biologically interpretable outputs (recovery of known Triticeae repeat markers) alongside quantitative performance metrics (identification of graph-based clustering as the dominant computational cost). By making comparative read allocation, prefix policy, and analysis-ready tables explicit - and by supporting caching and structured logging - REAPER supports reproducible comparative repeatome analysis in evolving multisample projects. As an orchestration layer rather than a discovery algorithm, REAPER's contribution lies in reproducibility, monitorability, and comparative-analysis infrastructure, with biological accuracy remaining contingent on the underlying RepeatExplorer2/TAREAN and satMiner methods.

TAREAN↗

PubMed2026

BioEMMA: Automated Generation of Model-Specific Escher-Compatible Maps from KEGG Pathways.

Genome-scale metabolic models are widely used to investigate cellular metabolism, but their interpretation and comparison are limited by the lack of reproducible pathway-level visualizations with a common spatial organization. This study presents BioEMMA, a Python-based tool for the automated generation of model-specific metabolic pathway maps in the Escher JSON format using coordinate information from curated KEGG pathway maps. BioEMMA parses KGML files, map reaction and metabolite identifiers to model database namespaces, filters pathway elements according to an input SBML model, adds non-primary metabolites, reconstructs Escher-compatible layouts, and supports flux visualization. The tool was integrated into a reproducible BioUML workflow for metabolic model reconstruction. BioEMMA was evaluated using the e_coli_core model and the KEGG glycolysis/gluconeogenesis pathway while generating a model-specific map with overlaid FBA fluxes. It was then applied to compare E. coli reconstructions generated by gapseq, ModelSEEDpy, and Reconstructor across three central carbon metabolism pathways. To broaden the evaluation, BioEMMA was applied using 87 prokaryotic BiGG models and three eukaryotic models. The analysis revealed pathway-specific differences in reaction coverage, shared and model-specific reactions, and predicted flux activity. BioEMMA therefore provides a reproducible framework for pathway-level visualization and comparison of genome-scale metabolic reconstructions within a common spatial coordinate system.

Escher maps↗

PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗