Biology subjects

Aerts, S.

Publications and source records attributed to Aerts, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Measuring conscious tactile perception without report: pupil, blink, and microsaccade signatures of a tactile no-report paradigm

Conscious perception is often inferred from behavioral reports, which conflate awareness-related neural signals with the motor and decision processes required to respond. No-report paradigms address this by using spontaneous physiological signals, such as eye movements, to infer perception. Although our group developed a no-report visual paradigm, we had not applied this approach to touch. We developed a no-report tactile paradigm using the Galileo stimulation system to deliver pneumatic air puffs to non-thumb fingers on both hands while tracking eye movements. Participants reported sensations from the Report hand while receiving threshold-level puffs on both hands; eye metrics were used to infer perception in the No-Report hand. Perceived threshold-level stimuli elicited greater pupil dilation and more post-stimulus blinking than identical unperceived stimuli, with effects lasting 1 - 2 seconds. Microsaccade rate decreased after stimulus onset, but this suppression did not reach statistical significance. A stacked support vector machine trained on the Report eye-tracking measures was applied to classify No-Report trials and successfully separated [Predicted] Perceived from [Predicted] Not Perceived trials (AUC = .92; cross-validated accuracy = 72%; sensitivity = 90% at 80% specificity; label-permutation p < .001). These findings show that eye tracking can detect conscious tactile perception without overt responses and offer a noninvasive, scalable approach for studying tactile perception in patients with self-reported disabilities, including comatose or intubated patients.

neuroscience↗

bioRxiv2026

DeepSCENIC: transfer learning from sequence-to-function models enables causal gene regulatory network inference

Sequence-to-function (S2F) deep learning models have become an important aid to decipher the genomic cis-regulatory code. However, current S2F models do not take the cellular trans-environment of transcription factors (TF) into account. Conversely, methods for gene regulatory network (GRN) inference often rely on heuristics or simple position weight matrices (PWMs), without exploiting the combinatorial grammar of genomic enhancers. Here, we present DeepSCENIC, a deep learning framework that enables causal GRN inference by performing transfer learning from S2F models to single-cell multiome atlases. We first test and validate DeepSCENIC on ENCODE cell lines, demonstrating that the framework accurately predicts single-cell gene expression and chromatin accessibility by leveraging pretrained S2F models like Enformer and Borzoi. We show that DeepSCENIC recovers TF-region (TF-RE) interactions with high fidelity, and captures de novo TF binding motifs without prior PWM knowledge. The model improves enhancer-gene associations (RE-TG) over correlation-based baselines when benchmarked against large-scale CRISPRi screens. After training a DeepSCENIC model, it enables the prediction of perturbation effects during cell state changes by acting as a mechanistic simulator. In a melanoma cell line atlas, the model accurately recapitulates the transcriptional shift from melanocytic to mesenchymal states, and predicted knock-down effects show high concordance with experimental time series data. Finally, we use DeepSCENIC to identify mouse-human cortex conserved GRNs, finding high cross-species concordance in TF activity programs across matched neuronal subclasses and validating top-ranked enhancers against experimental reporter assays. By unifying S2F enhancer representations with single-cell multiomics, DeepSCENIC provides a new paradigm for jointly modeling and simulating cis-sequence and trans-cellular perturbations.

bioinformatics↗

bioRxiv2026

Individual, behavioural, and environmental determinants of personal light exposure in daily life: A multi-country wearable and experience-sampling study

Light supports circadian regulation and is associated with non-communicable diseases, yet ocular exposure patterns remain poorly understood. We recruited 191 adults across nine sites in seven countries, combining diaries and contextual reports with near-eye (141 participants; 816 participant-days) and complementary chest-level measurements (154 participants; 902 participant-days). In near-eye analyses, only 24.0% of recorded daytime minutes met the recommendation of at least 250 lx melanopic equivalent daylight illuminance; 63.3% of pre-sleep and 87.7% of bedside sleep-environment minutes met their respective limits. Variation among people and days within sites exceeded that among sites (ratio 1.99; 95%-CI, 1.29-4.77). In hourly models, light source and setting contributed the largest shares of fitted variation after time of day. Site-average outdoor exposure was 9.35 times that while awake at home (95%-CI, 6.95-12.59; 714.2 versus 76.4 lx). This baseline identifies a daytime exposure gap and supports testing interventions in everyday settings to improve light exposure and health.

physiology↗

bioRxiv2026

Microglial lipid signaling drives glioblastoma invasion and represents a therapeutic vulnerability

Glioblastoma (GBM) is characterized by diffuse infiltration into the surrounding brain, which precludes complete surgical resection, the strongest determinant of patient survival. The mechanisms that drive this invasive growth remain incompletely understood. Here we identify a lipid-mediated paracrine signaling axis through which microglia, the resident macrophages of the brain, promote glioma invasion. Integrating single-cell transcriptomics, spatial lipidomics, and functional perturbation across mouse models and human GBM samples, we show that invading tumor cells engage and reprogram microglia via CSF1R-PI3K signaling. This induces a metabolic switch in microglia, leading to the secretion of bioactive lipids, including lysophosphatidylcholines (LPCs) and lysophosphatidic acids (LPAs), which act as pro-invasive cues across GBM subtypes through distinct downstream pathways. Disruption of the microglia-GBM axis, either by inhibiting CSF1R signaling or by blocking lipid mobilization, reduces lipid secretion and suppresses tumor invasion. Targeting downstream LPA-LPAR or YAP/TAZ signaling further constrains invasion in a context-dependent manner. Together, these findings define a lipid-driven signaling circuit that links the tumor microenvironment to glioma invasion and identify therapeutic strategies to limit tumor infiltration and improve surgical resectability.

cancer biology↗

bioRxiv2026

Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes

Understanding and modeling how a single human genome concurrently encodes gene regulatory programs for thousands of cell types remains a central challenge in genomics and machine learning. Most human cell types emerge during embryonic, fetal, and pediatric development which are inaccessible to comprehensive molecular profiling. To circumvent this, we hypothesized that the mismatch in evolutionary rates between cis-acting enhancers that modulate gene expression (fast) and the trans-acting regulatory factors that specify cell types (slow) creates an opportunity for evolutionary transfer learning. Specifically, models trained to predict cell type-specific enhancers in one species should generalize to the orthologous cell types and enhancers of related species. To test this, we generated a single-cell atlas of chromatin accessibility spanning mouse embryonic day 10 (E10) to birth (P0). Using combinatorial indexing1, we profiled 3.9 million nuclei from 36 staged embryos, resolving genome-wide accessibility in 36 cell classes and 140 cell types. We then trained a series of multi-output deep learning models (CREsted2), each addressing limitations of the preceding approach, towards the goal of genome-wide prediction of distal enhancers across major developmental lineages. An evolution-naive model achieved strong performance on heldout peaks, but exhibited two failure modes during genome-wide inference: overprediction at tandem repeats and conflation of promoter and distal enhancer grammars. An evolution-aware model resolved these by regrouping accessible regions based on their retention and functional coherence across mammalian evolution, but failed to generalize across species. Finally, an evolution-augmented model, STEAM (Synteny-aware Transfer learning for Enhancer Activity Modeling), incorporated enhancer orthologs from 241 mammalian genomes (Zoonomia3) in a synteny-supervised manner. This increased the effective data scale by as much as 195-fold, markedly improving generalization across mammals despite greater label noise. We applied STEAM to the genome-wide inference of cell class-specific distal developmental enhancers in humans, mice (HumMus) and 239 additional mammals3 (BabaGanoush), i.e. 32 x 241 = 7,712 genome-wide distal enhancer prediction tracks. Together, our results unify advances in single-cell profiling, deep learning, and comparative genomics into a framework for the evolutionary transfer learning of noncoding regulatory grammars. More broadly, our work supports the view that model organisms and evolutionarily diverse genomes are indispensable resources for accelerating and enhancing the AI-enabled exploration of human biology. NoteAn interactive version of this preprint, together with count matrices, CREsted models, prediction tracks, code and reproducible figures, is available at this link (ref 4).

genomics↗

bioRxiv2026

Modeling cis-regulatory variation in human brain enhancers across a large Parkinson's Disease cohort

Genome-wide association studies (GWAS) have linked more than a hundred non-coding genomic loci to Parkinsons disease (PD) risk. Deciphering their functional impact on gene regulation requires cell type-aware modeling approaches to assess the effects of sequence variation on enhancer function and target gene expression. To address this challenge, we generated a comprehensive matched dataset from 190 human donors (115 controls and 75 PD), comprising long-read whole-genome sequencing alongside single nucleus multiome atlases (snATAC-seq and snRNA-seq for 3.1 and 1.1 million nuclei respectively) of the anterior cingulate cortex and substantia nigra. By integrating chromatin accessibility quantitative trait loci (caQTL), DNA methylation QTL (meQTL), and allele-specific chromatin accessibility (ASCA), we identified 53,841 high-confidence cis-acting genetic variants that modulate cell type-specific enhancer accessibility in one or both brain regions. We further demonstrate that sequence-to-function models can accurately predict the impact of these variants directly from the genomic sequence. Novel explainability approaches allowed stratifying these variants according to their regulatory function, with the majority disrupting specific transcription factor binding sites in a cell type specific manner. Integrating these "enhancer variants" (EV) with eQTL mapping and gene locus modeling linked a subset of EVs to their target genes. Finally, we applied these models to prioritize regulatory variants at known PD GWAS loci, bypassing statistical limitations in rare disease-relevant populations like dopaminergic neurons. All together, we establish a unique resource and new sequence modeling strategies to interpret functional non-coding variation in the human brain.

genomics↗

bioRxiv2026

System-wide extraction of cis-regulatory rules from sequence-to-function models in human neural development

The genomic cis-regulatory code (CRC) underlies spatiotemporal specificity of gene expression. While sequence-to-function (S2F) models can accurately encode the CRC of transcriptional enhancers, decoding these models into human-interpretable rules remains a major challenge. Here we tackle this challenge in human neural development, for which we generate two new single-cell multiome atlases, one from a human embryo and one from neural tube organoids. We use this comparative framework to robustly extract combinations of transcription factor (TF) binding sites that are necessary and sufficient to design enhancers. As such we extract cis-regulatory rules for dorsal-ventral progenitors, neural crest, mesenchyme and neurons. To enable this, we develop a new strategy and computational package, called TF-MINDI, to embed, cluster, and annotate candidate TF binding sites, and to extract combinatorial rules for each cell type. We evaluate rule-based models in conjunction with blackbox S2F models through simulations, evolutionary comparisons with zebrafish, topic modeling, and enhancer reporter-assays. Our findings show robust and interpretable rule extraction and constitute a step forward in deciphering, explaining, and formalizing the CRC. TF-MINDI is available at: https://github.com/aertslab/TF-MINDI.

bioinformatics↗

bioRxiv2025

Circuit specific specialization of human basal ganglia astrocytes

Astrocytes shape synapses and circuits, yet human basal ganglia astrocyte diversity is incompletely defined. We built a multimodal atlas by integrating single-nucleus RNA-sequencing and chromatin accessibility with DNA methylation, 3D chromatin conformation, and spatial transcriptomics, then mapped basal ganglia programs onto a whole-brain reference. Astrocytes segregated into three anatomical subgroups spanning striatal gray matter, extra-striatal gray matter, and white matter, with subgroup-biased neurotransmitter transporters and synapse-associated programs consistent with differences in dominant afferent input. Within striatum, dorsal and ventral astrocyte populations aligned with distinct microcircuits and were conserved in nonhuman primates. A deep learning sequence model identified subgroup-associated enhancer code and, when benchmarked against published enhancer-AAV datasets, supported the design of candidate viral tools to target basal ganglia astrocyte programs in vivo. Together, these data define major axes of human astrocyte specialization and provide a framework for cell type-specific dissection of basal ganglia function.

neuroscience↗

bioRxiv2025

Expanding the fly eye gene regulatory network: from Drosophila to the hoverfly Episyrphus balteatus.

Comparing developmental mechanisms across long evolutionary distances is challenging, especially when the level of description in the compared species is very disparate. This is the case for the gene regulatory network (GRN) that underlies the development of compound eyes in insects, where the very detailed level description of the network in the fruit fly Drosophila melanogaster contrasts with the knowledge of the network in other well studied insects, such as the flour beetle Tribolium or the desert locust Schistocerca. It is not even known to what extent the Drosophila eye GRN is typical of flies (diptera). Here we have introduced the marmalade fly Episyrphus balteatus (Syrphidae), separated from Drosophila approximately 90 MYrs, as a fly species to compare the mechanisms of compound eye development. The Episyrphus eye develops from an imaginal disc which is, in the overall, similar to that of Drosophila. By generating parallel data sets in Episyrphus and Drosophila to maximize comparability, our results show how the genes known to participate in the early specification of the Drosophila eye and the onset of its differentiation are likely participating in the eye GRN of Episyrphus, further expanding the set of conserved genes/nodes in this network. This gene set might be the basis for further explorations of the developmental mechanisms involved in compound eye development beyond flies. By combining RNAseq and ATACseq profiling with TF motifs we derive the first eye GRN in Episyrphus. When built using Episyrphus/Drosophila conserved nodes, the resulting GRN model exhibited abundant internal connectivity, suggesting that, as proposed in Drosophila, the GRN is knitted with many regulatory feedbacks. Using link conservation as a criterion for discovering new regulatory interactions, we find that the AML1/Runx transcription factor lozenge (lz) is a negative regulator of the retinal determination gene dachshund (dac) in Drosophila. However, although many of the predicted regulatory links are conserved, a large number of regulatory connections seem species-specific, even among conserved nodes. This suggests quite a significant regulatory rewiring during the 90Myr period separating Episyrphus and Drosophila.

developmental biology↗

bioRxiv2025

Species-specific Rates of Fatty Acid MetabolismSet the Scale of Temporal Patterning of Corticogenesisthrough Protein Acetylation Dynamics

Developmental processes display temporal differences across species, leading to divergence in organ size and composition. In the cerebral cortex, neurons of diverse identities are generated sequentially through a temporal patterning mechanism conserved throughout mammals. This corticogenesis process is considerably prolonged in the human species, leading to increased brain size and complexity, but the underlying molecular mechanisms remain largely unknown. Here we found that human cortical progenitors displayed lower levels of fatty acid oxidation than their mouse counterparts, in line with their protracted pattern. Treatments that enhance mitochondrial fatty acid oxidation (FAO) accelerated the development of human cortical organoids, including faster progression of neural progenitor cell fate and precocious generation of late-born neurons and glia. FAO accelerated temporal patterning through increased Acetyl-CoA-dependent protein acetylation, including on specific histone transcriptional marks. Thus, species-specific metabolic rates regulate the turnover of post-translation modifications to set the scale of temporal gene regulatory networks of corticogenesis.

neuroscience↗

bioRxiv2025

GAME: Genomic API for Model Evaluation

The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models on practical applications has been challenging because individual projects evaluate their models in ad hoc ways, and there is substantial heterogeneity of both model architectures and benchmarking tasks. To address this challenge, we have created GAME, a system for large-scale, community-led standardized model benchmarking on user-defined evaluation tasks. We borrow concepts from the Application Programming Interface (API) paradigm to allow for seamless communication between pre-trained models and benchmarking tasks, ensuring consistent evaluation protocols. Because all models and benchmarks are inherently compatible in this framework, the continual addition of new models and new benchmarks is easy. We also developed a Matcher module powered by a large language model (LLM) to automate ambiguous task alignment between benchmarks and models. Containerization of these modules enhances reproducibility and facilitates the deployment of models and benchmarks across computing platforms. By focusing on predicting underlying biochemical phenomena (e.g. gene expression, open chromatin, DNA binding), we ensure that tasks remain technology-independent. We provide examples of benchmarks and models implementing this framework, and anticipate that the community will contribute their own, leading to an ever-expanding and evolving set of models and evaluation tasks. This resource will accelerate genomics research by illuminating the best models for a given task, motivating novel functional genomic benchmarks, and providing a more nuanced understanding of model abilities.

bioinformatics↗

bioRxiv2025

Decoding cnidarian cell type gene regulation

Animal cell types are defined by differential access to genomic information, a process orchestrated by the combinatorial activity of transcription factors that bind to cis-regulatory elements (CREs) to control gene expression. However, the regulatory logic and specific gene networks that define cell identities remain poorly resolved across the animal tree of life. As early-branching metazoans, cnidarians can offer insights into the early evolution of cell type-specific genome regulation. Here, we profiled chromatin accessibility in 60,000 cells from whole adults and gastrula-stage embryos of the sea anemone Nematostella vectensis. We identified 112,728 CREs and quantified their activity across cell types, revealing pervasive combinatorial enhancer usage and distinct promoter architectures. To decode the underlying regulatory grammar, we trained sequence-based models predicting CRE accessibility and used these models to infer ontogenetic relationships among cell types. By integrating sequence motifs, transcription factor expression, and CRE accessibility, we systematically reconstructed the gene regulatory networks that define cnidarian cell types. Our results reveal the regulatory complexity underlying cell differentiation in a morphologically simple animal and highlight conserved principles in animal gene regulation. This work provides a foundation for comparative regulatory genomics to understand the evolutionary emergence of animal cell type diversity.

genomics↗

bioRxiv2025

HyDrop v2: Scalable atlas construction for training sequence-to-function models

Deciphering cis-regulatory logic underlying cell type identity is a fundamental question in biology. Single-cell chromatin accessibility (scATAC-seq) data has enabled training of sequence-to-function deep learning models allowing decoding of enhancer logic and design of synthetic enhancers. Training such models requires large amounts of high-quality training data across species, organs, development, aging, and disease. To facilitate the cost-effective generation of large scATAC-seq atlases for model training, we developed a new version of the open-source microfluidic system HyDrop with increased sensitivity and scale: HyDrop v2. We generated HyDrop v2 atlases for the mouse cortex and Drosophila embryo development and compared them to atlases generated on commercial platforms. HyDrop v2 data integrates seamlessly with commercially available chromatin accessibility methods (10x Genomics). Differentially accessible regions and motif enrichment across cell types are equivalent between HyDrop-v2 and 10x atlases. Sequence-to-function models trained on either atlas are comparable as well in terms of enhancer predictions, sequence explainability, and transcription factor footprinting. By offering accessible data generation, enhancer models trained on HyDrop-v2 and mixed atlases can contribute to unraveling cell-type specific regulatory elements in health and disease.

bioinformatics↗

bioRxiv2025

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species

Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at deciphering sequence features and grammar that underlie their spatiotemporal activity. To enable end-to-end enhancer modeling and design, we developed a software and modeling package, called CREsted. It combines preprocessing starting from single-cell ATAC-seq data; modeling with a choice of several architectures for training classification and regression models on either topics or pseudobulk peak heights; sequence design using multiple strategies; and downstream analysis through a collection of tools to locate transcription factor (TF) binding sites, infer the effect of a TF (activating or repressing) on enhancer accessibility, decipher enhancer grammar, and score gene loci. We demonstrate CREsted using a mouse cortex model that we validate using the BICCN collection of in vivo validated mouse brain enhancers. Classical enhancers in immune cells, including the IFNB1 enhanceosome are revisited using a PBMC model, and we assess the accuracy of TF binding site predictions with ChIP-seq. Additionally, we use CREsted to compare mesenchymal-like cancer cell states between tumor types; and we investigate different fine-tuning strategies of Borzoi within CREsted, comparing their performance and explainability with CREsted models trained from scratch. Finally, we train a CREsted model on a scATAC-seq atlas of zebrafish development and use this to design and in vivo validate cell type-specific synthetic enhancers in three tissues. For varying datasets, we demonstrate that CREsted facilitates efficient training and analyses, enabling scrutinization of the enhancer logic and design of synthetic enhancers across tissues and species. CREsted is available at https://crested.readthedocs.io.

genomics↗

bioRxiv2025

A dynamic gene regulatory code drives synaptic development of hippocampal granule cells

Connecting neurons into functional circuits requires the formation, maturation, and plasticity of synapses. While advances have been made in identifying individual genes regulating synapse development, the molecular programs orchestrating their action during circuit integration of neurons remain poorly understood. Here, we take a multiomic approach to reconstruct gene regulatory networks (GRNs), comprising transcription factors (TFs), regulatory regions, and predicted target genes, in hippocampal granule cells (GCs). We find a dynamic gene regulatory code, with early and late postnatal GRNs regulating cell morphogenesis and synapse organization and plasticity, respectively. Our results predict sequential regulations, with early-active TFs delaying the activation of later GRNs and their putative synaptic targets. Using a loss-of-function approach, we identify Bcl6 as a regulator of pre- and postsynaptic structural maturation, and Smad3 as a modulator of inhibitory synaptic transmission, in GCs. Together, these findings highlight the networks of key TFs and target genes orchestrating GC synapse development.

neuroscience↗

bioRxiv2025

The evolution of gene regulation in mammalian cerebellum development

Gene regulatory changes are considered major drivers of evolutionary innovations, including the cerebellums expansion during human evolution, yet they remain largely unexplored. In this study, we combined single-nucleus measurements of gene expression and chromatin accessibility from six mammals (human, bonobo, macaque, marmoset, mouse, and opossum) to uncover conserved and diverged regulatory networks in cerebellum development. We identified core regulators of cell identity and developed sequence-based models that revealed conserved regulatory codes. By predicting chromatin accessibility across 240 mammalian species, we reconstructed the evolutionary histories of human cis-regulatory elements, identifying sets associated with positive selection and gene expression changes, including the recent gain of THRB expression in cerebellar progenitor cells. Collectively, our work reveals the shared and mammalian lineage-specific regulatory programs governing cerebellum development.

evolutionary biology↗

bioRxiv2024

Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for identifying functional enhancers in mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian cortex and to designing more efficient genetic tools to target cortical cell types.

genomics↗

bioRxiv2024

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Combinations of transcription factors govern the identity of cell types, which is reflected by enhancer codes in cis-regulatory genomic regions. Cell type-specific enhancer codes at nucleotide-level resolution have not yet been characterized for the mammalian neocortex. It is currently unknown whether these codes are conserved in other vertebrate brains, and whether they are informative to resolve homology relationships for species that lack a neocortex such as birds. To compare enhancer codes of cell types from the mammalian neocortex with those from the bird pallium, we generated single-cell multiome and spatially-resolved transcriptomics data of the chicken telencephalon. We then trained deep learning models to characterize cell type-specific enhancer codes for the human, mouse, and chicken telencephalon. We devised three metrics that exploit enhancer codes to compare cell types between species. Based on these metrics, non-neuronal and GABAergic cell types show a high degree of regulatory similarity across vertebrates. Proposed homologies between mammalian neocortical and avian pallial excitatory neurons are still debated. Our enhancer code based comparison shows that excitatory neurons of the mammalian neocortex and the avian pallium exhibit a higher degree of divergence than other cell types. In contrast to existing evolutionary models, the mammalian deep layer excitatory neurons are most similar to mesopallial neurons; and mammalian upper layer neurons to hyper- and nidopallial neurons based on their enhancer codes. In addition to characterizing the enhancer codes in the mammalian and avian telencephalon, and revealing unexpected correspondences between cell types of the mammalian neocortex and the chicken pallium, we present generally applicable deep learning approaches to characterize and compare cell types across species via the genomic regulatory code.

genomics↗