Biology subjects

Banerjee, A.

Publications and source records attributed to Banerjee, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Dengue Virus Serotype 2 Infection Transcriptionally Reprograms Host Neutrophils to Establish an Immunosuppressive Microenvironment in Mice

Dengue virus (DV) infection triggers profound alterations in host immune responses, yet the transcriptomic reprogramming of innate immune cells during disease progression remains incompletely understood. Here, we demonstrated that Dengue virus serotype 2 (DV-2) infection transcriptionally reprograms host neutrophils, establishing an immunosuppressive microenvironment that facilitates pathogenesis. Through scRNA transcriptomic profiling of bone marrow and spleen cells from the DV-2-infected AG129 mouse model, we delineated a distinct neutrophil signature that shows dynamic shifts in immature and activated populations over the course of dengue infection. We also characterized a specific Ly6Glow subpopulation, notably Fibrinogen-like protein 2 (FGL2)-expressing subsets, that appeared with disease progression. Serum FGL2 levels were significantly elevated in both DV-2-infected mice and human dengue-positive patients. Furthermore, treatment of splenocytes with FGL2 or FGL2-enriched neutrophil secretomes suppressed T cell functional activity and promoted an immunosuppressive phenotype. Together, these findings reveal how DV-2-driven neutrophil reprogramming shapes an immunosuppressive network, highlighting the importance of studying FGL2-expressing neutrophil subsets to understand dengue immunopathogenesis.

immunology↗

bioRxiv2026

A unified framework for quantitative splicing and transcript prediction

RNA splicing is frequently disrupted by genetic variation, contributing to a wide range of human diseases. Identifying the variants responsible for altered splicing is therefore crucial but has long been challenging. Deep learning models that predict splicing from genomic sequence have substantially advanced their detection, but accurately resolving their quantitative effects on splicing and the resulting transcripts remains an unmet need. Here we introduce SpliceAI2, a deep learning model trained end-to-end on splice site, splice junction, and transcript usage across multiple mammalian species. SpliceAI2 reconstructs complete transcripts directly from genomic sequence and considerably improves identification of splice-altering variants and quantification of their effects in diverse benchmarks spanning cryptic splicing, splicing quantitative trait loci, and massively parallel reporter assays. Human population datasets provide independent validation of these predicted functional effects through evidence of natural selection and associated changes in plasma protein abundance. Application to a rare disease cohort reveals that predicted splice-altering variants are highly enriched in disease-associated genes and account for 15% of the excess genetic burden, with approximately half residing in deep intronic regions likely missed by exome sequencing. Together, these findings establish SpliceAI2 as a major step forward in interpreting splice-altering variants across the human genome.

genomics↗

bioRxiv2026

Unsupervised 3D classification and characterisation of osteocyte and chondrocyte lacunae in the murine osteochondral unit

In the murine osteochondral unit, morphology of chondrocyte lacunae (CCL) within calcified articular cartilage and osteocyte lacunae (OCL) within the underlying subchondral bone reflects local biomechanical conditions that are altered in disease, making reliable discrimination of both lacunar populations important for studying osteochondral physiology and pathology. Histology is generally considered the gold standard for identifying CCL and OCL. However, it is inherently limited to two-dimensional assessment and a relatively small tissue section, restricting comprehensive characterisation of three-dimensional lacunar structure. Synchrotron-radiation micro-computed tomography (SR-microCT) enables volumetric quantification of lacunae; where classification is typically based on the position of each lacuna relative to the cement line separating calcified cartilage from bone, requiring manual delineation of this convoluted interface. Such approaches are labour-intensive, operator-dependent, and rely on access to specialised synchrotron infrastructure that is not widely available. To address these limitations, we present a fully automated, annotation-free pipeline for classifying OCL and CCL in the mouse osteochondral unit based on desktop micro-computed tomography (microCT), independent of cement line identification. The proposed unsupervised workflow requires neither tissue separation nor labelled training data, enabling objective and scalable discrimination of lacunar populations directly from volumetric image data. Seven morphometric and positional features per lacuna were extracted from SR-microCT and desktop microCT datasets and analysed using Uniform Manifold Approximation and Projection (UMAP). Validation of the pipeline outcome on SR-microCT against manual classification across specimens yielded very-high agreement, with Gwet's AC1, the Matthews correlation coefficient, and balanced accuracy each approaching 0.99, in both healthy and post-traumatic osteoarthritis specimens. Such a workflow can be readily adapted for the investigation of osteochondral interface in various other species.

bioengineering↗

bioRxiv2026

Determining Spatial Patterns of Human-Tiger and Human-Leopard Conflict across South Asia through A Multi-Scale Regional Approach

Over the past two decades, significant population recoveries of tigers and leopards have driven a substantial rise in human-carnivore conflict across South Asia. Sustaining the long-term conservation of both species demands effective, adaptive conflict management. However, existing research is largely confined to isolated landscapes or protected areas, hindering the identification of spatial conflict drivers across broader, heterogeneous regions. To address this gap, this study compiled 4,457 human leopard conflict (HLC) and 2,563 human tiger conflict (HTC) records spanning 2000 to 2024 across Bangladesh, Bhutan, India, and Nepal. We evaluated conflict locations against 33 landscape, anthropogenic, climatic, topographic, and hydrological predictors across three spatial scales using binomial generalized linear regression models and three alternative background sampling methods. Model performance peaked at the largest spatial scale for both species and remained consistent across sampling strategies. HTC correlated positively with forest fragmentation and proximity to human settlements, but decreased with higher overall anthropogenic disturbance. Conversely, HLC concentrated in highly productive, high-precipitation, human-modified landscapes. These contrasting spatial patterns highlight distinct ecological responses to habitat modification and carry unique conservation implications. Ultimately, this study underscores the need to integrate carnivore abundance, wild prey densities, and socioeconomic factors to achieve a holistic understanding of conflict dynamics.

ecology↗

bioRxiv2026

MetagenomicsBench: A Verifiable Benchmark for Agentic Metagenomics Analysis

Metagenomics has transformed our ability to characterize microbial communities without relying on cultivation, but translating complex microbiome datasets into reliable biological conclusions remains a major analytical challenge. Although AI agents have improved substantially at software engineering and general data analysis, it remains unclear whether they can reliably analyze and interpret real-world metagenomic data. We introduce MetagenomicsBench, a benchmark of 100 verifiable evaluations derived from published datasets spanning community structure, host-microbiome associations, microbial function, longitudinal dynamics, and microbiome interventions. Each evaluation provides experimental data and a deterministic grader that evaluates recovery of a key analytical or biological result. Across 10,200 trajectories from 34 model-harness configurations, the strongest configuration achieved a 60.0% pass rate. Performance varied across functional domains, data modalities, and biological systems, while greater cost, token usage, and tool use did not consistently correspond to higher accuracy. Trajectory analysis showed that agents often produced technically plausible analyses while making mistakes in problem interpretation, statistical reasoning, and biological interpretation. MetagenomicsBench provides a framework for measuring progress toward AI agents that can reliably make and validate the analytical and biological decisions required for metagenomic research.

microbiology↗

bioRxiv2026

SARS-CoV-2 maturation driven by a mechano-active nucleocapsid-RNA condensate

The nucleocapsid (N) protein, the core component of the SARS-CoV-2 virus, binds to the 30-kb viral genomic RNA (vgRNA) to form ribonucleoprotein assemblies that are packaged into ~100 nm membrane-enclosed virions. In addition to viral assembly, the N protein performs other functions, including roles in viral mRNA transcription, replication, and immune regulation, making it a key target for developing diagnostics and vaccines. Recent studies show that N protein and RNA undergo phase separation to form a biomolecular condensate that constitutes the viral core. However, how this condensate becomes selectively enveloped by a membrane remains unclear. Here, using a minimal reconstituted system, we demonstrate that N protein-vgRNA condensates are spontaneously enveloped by lipid bilayer membranes, whereas condensates formed by N protein alone or with genomic RNA fragments fail to undergo envelopment and instead adhere to or weakly deform membranes. We show that RNA length tunes the material properties of N protein condensates, with vgRNA imparting enhanced elasticity. We propose a physical model for SARS-CoV-2 maturation in which adhesion between the membrane and the N protein-vgRNA condensate drives membrane bending, while condensate elasticity preserves core geometry. These results identify RNA-dependent material properties as an underlying physical principle for selective SARS-CoV-2 maturation.

biophysics↗

bioRxiv2026

Carboxysome-Inspired Protein Coacervates for Light-Driven CO2 Reduction and H2 Evolution

Efficient catalysis often requires high local concentrations of reactants and catalysts, which cells achieve through compartmentalization within organelles, such as carboxysomes, that increase the efficiency of bacterial carbon fixation. Here, we engineered a photocatalytic reaction compartment that concentrated an artificial metalloenzyme, carbon dioxide, and a photosensitizer by liquid-liquid phase separation triggered by a cationic polypeptide, deca(L-arginine) (R10). At low R10 concentrations, CoPPIX binding increases the alpha-helical structure of the otherwise disordered protein, supercharged cytochrome b5622(-22). At higher concentrations, electrostatic complexation produces spherical droplets that enrich the protein and cobalt cofactor and recruit the photosensitizer [Ru(bpy)3]2+. Under illumination, coacervation increased hydrogen evolution 1.9-fold and CO formation from carbon dioxide; 1.3-fold relative to the corresponding solution-phase protein system. Co-encapsulation of carbonic anhydrase changed the product distribution specifically in the condensed phase: CO production increased 3.2-fold, hydrogen evolution decreased from 1.51 to 0.70 mol, and CO selectivity among the detected two-electron products rose from 33% to 77%. These results demonstrate that bioinspired coacervates can stabilize reactive intermediates, enrich local substrate concentrations, and integrate multiple catalytic functions, providing a generalizable framework for programmable, light-driven synthetic organelles.

synthetic biology↗

bioRxiv2026

Remodelling of chromatin architecture and super-enhancer landscape in lamin A/C depleted HGSOC

Lamins are nuclear intermediate filament proteins that maintain nuclear architecture through interactions with the chromatin. The A- and B-type lamins tether the genome at the peripheral lamina underlying the inner nuclear membrane in the form of heterochromatic lamina-associated domains (LADs). However, LADs associated with A-type lamins are confined not only to lamina but also to the nuclear core, thereby pointing to their distinct and multifarious roles in chromatin organisation and regulation. Previous studies involving lamin B1 depletion depicted the detachment of LADs from the nuclear periphery, accompanied by alteration of chromatin distribution while preserving the topologically associating domains (TADs) in structurally intact form. In this piece of work, we have shown, for the first time in HGSOC, the effects of lamin A/C knockdown on chromatin organisation, which was characterised by significant detethering of gene-poor chromatin from the periphery along with active (A) to inactive (B) compartment switching. This was associated with a rewiring of oncogenic super-enhancer elements corroborated by the differential gene expression profile. Overall analysis tipped the scale in favour of reduced cellular proliferation upon lamin A/C knockdown. This finding was strengthened by the observed proliferative potential of spheroids ex vivo and tumours in a mouse xenograft model.

cancer biology↗

bioRxiv2026

BioSecBench-Function: A Verifiable Benchmark for Reasoning about Biological Function from Experimental Data

Inferring biological function from experimental data is central to understanding emerging pathogens and developing effective countermeasures, yet interpreting these data remains slow and expert-intensive. AI agents could help accelerate this process by reasoning across sequence, structural, and biophysical evidence. We present BioSecBench-Function, a verifiable benchmark for recovering biosecurity-relevant function from real biological data. The benchmark comprises 111 evaluations built from published datasets and graded deterministically against ground truth. We organize evaluations along two dimensions: threat axis (spanning seven biosecurity-relevant question types) and biological question (indicating whether the solution depends primarily on sequence, structure, or biophysical assay data). Across 7,326 runs from twenty-two model-harness configurations, Opus 5 under Claude Code led on endpoint pass rate at 50.3%, and Grok 4.6 under Grok Build led on overall pass rate at 44.1% when refusals counted as failures. Performance varied substantially across both model-harness configurations and task categories. Refusal rates differed sharply by provider, and cost was a poor predictor of accuracy: several configurations exceeded 40% pass rate at low cost. BioSecBench-Function provides a standard for measuring whether agents can be trusted to interpret what a new pathogen or variant does when the next outbreak arrives.

biophysics↗

bioRxiv2026

Retrieval uncertainty drives rapid consolidation of confabulated memories in retrosplenial cortex

Learning to make associations in the absence of direct reinforcement, based on previous memories of reward or punishment, is an evolutionary trait quintessential for survival. Little is known about if such higher order associations (HOA) can lead to fabrication of new memories for unreal events. We probed if uncertainty during retrieval of old memory representations can trigger such confabulation. Using chemogenetics, in vivo imaging, and behavior we show that uncertainty in memory perception can not only transfer conditioning to hitherto unexperienced novel context but also can get rapidly consolidated. We find while acquisition of HOA require dorsal-CA1 parvalbumin interneuron activity in intact hippocampus, retrieval requires retrosplenial cortex (RSc). We develop an automated Large-scale Brainwide Correlated Activity Mapping (LaBCAM) method to identify functional connectivity changes across different brain regions in such scenarios. LaBCAM finds that only uncertain remote retrieval engages canonical fear circuitry while regular retrieval does not. Longitudinal imaging of dendritic spines in RSc, a region involved in detection and context-dependent conflict resolution, reveals differential reorganization of spines during acquisition and rapid systems consolidation of HOA. Interestingly, we find HOA is implicated early in AD mice model (APP/PS1) and also in aged animals.

neuroscience↗

bioRxiv2026

Time-dependent effect of fluoride on caries lesions development in a rat caries model

Since model dose-response is critical when assessing caries lesion development over time, this study evaluated the influence of fluoride dose and treatment duration on caries progression in a rat caries model. Streptococcus mutans-infected Sprague-Dawley rats were treated with deionized water, 226 ppm F-, or 2,260 ppm F- twice daily for 3, 4, or 5 weeks. Caries lesions were assessed using Larson's modification of the Keyes scoring system and complemented by micro-computed tomography (microCT). Intraoral fluoride availability, serum and bone fluoride concentrations and microbial counts were also determined. Fluoride reduced caries severity in a dose- and time-dependent manner. While early enamel lesions were detected in all groups, extensive dentine lesions increased over time, in a dose-dependent manner, in the control and 226 ppm F- groups, and were not observed in the 2,260 ppm F- group after 5 weeks. Intraoral and bone fluoride availability increased significantly with fluoride concentration and treatment duration, whereas serum fluoride levels reflected fluoride dose instead of treatment duration. MicroCT-derived enamel volume correlated negatively with both total and extensive caries scores, supporting its utility as an objective measure of lesion severity. In conclusion, extending model length from 3 to 5 weeks increased the severity of caries lesions in a dose-dependent manner. Fluoride intraoral availability and bone fluoride also demonstrated a dose and time-dependent response.

pathology↗

bioRxiv2026

Regulation of Sulfolobus acidocaldarius surface structures by the PP2A core interaction module

Protein phosphorylation is a central regulatory mechanism that enables organisms to adapt to changing environmental conditions. The hyperthermophilic archaeon Sulfolobus acidocaldar-ius encodes only two phosphatases: the dual-specificity phosphatase PTP and the ser-ine/threonine phosphatase PP2A. PP2A has previously been implicated in archaellum regula-tion, and its deletion results in a hypermotile phenotype. Under starvation conditions, PP2A associates with a stress regulatory module comprising the archaellum repressors ArnA and ArnB, the universal stress protein UspA, and a GPN-loop GTPase. Here, we investigated PP2A-associated proteins under normal growth conditions and following UV-induced DNA damage. Pulldown experiments using a genomically HA-tagged PP2A strain identified a PP2A-associated basal regulatory module consisting of ArnA, ArnB, ArnE, and PTP, distinct from the previously described starvation-associated network. In addition, several proteins involved in the biogenesis and regulation of type IV pili co-purified with PP2A. Functional analyses using thermomicroscopy and electron microscopy revealed that deletion of {triangleup}pp2a, {triangleup}arnA, or {triangleup}arnB abolishes Aap-pilus formation and twitching motility, demonstrating that the PP2A regulatory network controls both swimming and surface-associated motility. In con-trast, the same network exerted only a modulatory effect on UV-induced cell aggregation. Together, our findings establish PP2A as a central regulator coordinating multiple archaeal surface structures through phosphorylation-dependent signaling.

microbiology↗

bioRxiv2026

Distinct Orbitofrontal Feedback Signals Shape Sensory Behavioral Strategies during Flexible Learning

Animals adapt their behavior by integrating sensory evidence with prior experience and contextual information. Understanding how animals employ specific behavioral strategies to integrate these variables and how neural mechanisms support such strategies remains unclear. We trained mice on a tactile reversal learning task and used a trial-by-trial computational model to infer latent decision strategies. Early in learning, mice relied on action-based ("choice-driven") policies but progressively transitioned to stimulus-guided choices ("cue-driven") as they learned the task. Following a rule reversal, mice flexibly reinstated this policy to adapt their behavior. Chemogenetic silencing of the lateral orbitofrontal cortex (lOFC) delayed this transition and impaired reversal learning. To identify the neural basis of strategy learning, we developed a novel approach combining low-dimensional analysis of neural activity with decoding of behavioral strategies from longitudinal two-photon imaging. We revealed reward- and error-strategy representations in excitatory layer 2/3 neurons in the primary somatosensory cortex (S1) that were differentially modulated by OFC instructive signals. Within S1, distinct subpopulations of positive and negative-valence-coding neurons tracked evolving decision strategies through trial-history integration. Together, these findings revealed how mice flexibly deploy distinct exploratory strategies during adaptive behavior, highlighting OFCs role in supporting reward and error-guided learning through distinct corticocortical interactions.

neuroscience↗

bioRxiv2026

Dancing to the beats of the mammalian connectome: Topological and dynamical asymmetries shape infra-slow oscillations

Flexible brain network dynamics unfold on several timescales, despite being constrained by a relatively static structural connectome. Recent cross-species evidence has described infra-slow network modulation, < 0.1 Hz, through two distinct but fundamentally aligned frameworks: transitions between states of integration and segregation identified by high and low global coherence respectively, and periodic transitions between internally and externally oriented attention indexed by the dynamic modulation of alpha power. Here we propose that these infra-slow, structured fluctuations in network engagement stem from a common underlying dynamical motif. Using a network of coupled oscillators embedded in human and macaque empirical connectomes, we show that topological features of brain organization such as modularity, hierarchy and intrinsic dynamical asymmetries naturally give rise to low-frequency collective modes that could nest high frequency states of neuronal communication. These "breathing" dynamics, analogous to beat phenomena in acoustic systems, produce infra-slow fluctuations in global synchrony. Disrupting the connectome topology abolishes such slow coherence oscillations, indicating their dependence on network topology. Furthermore, analytical results from reduced oscillator models reveal how small frequency differences between weakly coupled modules generate slow coherence oscillations, highlighting the importance of dynamical asymmetry. Finally, how neuromodulatory inputs can tune these emergent timescales is discussed, providing a mechanistic link between structural architecture and the dynamic regulation of large-scale brain function.

neuroscience↗

bioRxiv2026

A Cofactor-Activated Molecular Switch for Condensate Biogenesis and Catalysis in Escherichia coli

Biomolecular condensates formed through liquid-liquid phase separation (LLPS) compartmentalize biochemical reactions without enclosing membranes, enabling spatiotemporal control over diverse cellular processes. Engineering genetically encoded proteins that phase separate in response to defined chemical inputs remains a central challenge for synthetic biology. Here, we report a coiled-coil peptide polymer, M1, that undergoes cofactor-dependent condensation both in vitro and in Escherichia coli. M1 is an ABA triblock construct comprising two terminal helical domains connected by a flexible, intrinsically disordered linker. The terminal domains are derived from a heme-responsive coiled-coil motif that is destabilized in the apo state but assembles into a four-helix bundle upon metalloporphyrin coordination. We demonstrate that M1 forms condensates exclusively in its cofactor-bound state, both in vitro and in cells. In E. coli, these intracellular condensates accumulate at the cell poles in a concentration-dependent manner. Depletion of cellular heme biosynthetic capacity suppressed condensate formation, which was rescued by supplementation with the heme precursor {delta}-aminolevulinic acid ({delta}-ALA) and iron, consistent with metalloporphyrin coordination triggering assembly. The condensates retain peroxidase activity characteristic of heme-containing proteins and catalyze the oxidation of Amplex Red to resorufin both in vitro and in living cells. These results establish metalloporphyrin binding as a molecular switch for condensate biogenesis in a structured peptide polymer, directly coupling cofactor coordination, mesoscale assembly, and catalytic function within a single designed system. SYNOPSIS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/734084v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1bc678eorg.highwire.dtl.DTLVardef@32bb4corg.highwire.dtl.DTLVardef@a6890aorg.highwire.dtl.DTLVardef@16f13c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Synthetic Biology↗

bioRxiv2026

Auditory Working Memory and Sound Segregation Ability Predict Speech-in-Noise in Adult Cochlear Implant Users

ObjectivesOutcomes following cochlear implantation vary substantially across adult recipients, and the cognitive and perceptual factors contributing to this variability are not fully understood. This poses a challenge for developing strategies to improve cochlear implant outcomes, as such approaches require a clearer understanding of the mechanisms underlying individual listening difficulties. In this study, we investigated auditory cognitive measures in cochlear implant (CI) users to further elucidate the origins of this variability. DesignThirty-seven adult cochlear implant users completed measures of auditory cognition, comprising auditory working memory (AWM) and sound segregation ability, measured using an auditory figure-ground task (AFG), as well as measures of peripheral temporal and spectral processing, comprising the temporal modulation detection threshold (TMDT) and spectral ripple discrimination threshold (SRDT). Speech perception outcomes were assessed using word-in-noise (WIN) and sentence-in-noise (SIN) tasks. Separate multiple linear regression models evaluated the unique contribution of the auditory cognition measures to WIN and SIN performance, after accounting for the peripheral measures. ResultsBoth regression models explained a substantial proportion of variance in speech-in-noise outcomes (WIN: adjusted R{superscript 2} = 0.55; SIN: adjusted R{superscript 2}=0.57, both p < 0.001). For WIN performance, AFG and AWM were significant predictors. A similar pattern was found for SIN performance, where lower AWM ability and poorer AFG segregation were linked to poorer sentence listening in noise. No significant effects of spectral ripple discrimination or temporal modulation detection were observed in either model, even though both were significantly correlated with WIN performance. ConclusionsThese findings indicate that auditory working memory and sound segregation ability are robust predictors of speech-in-noise outcomes in adult cochlear implant users, across both word- and sentence-level measures. Together, the results may help explain why speech-in-noise outcomes remain highly variable among CI users, even when basic sensory encoding abilities are taken into account. Incorporating measures of auditory working memory and fundamental sound segregation may therefore improve outcome prediction and help in developing more individualised rehabilitation strategies.

neuroscience↗

bioRxiv2026

Neurodevelopmental Inequality arising from Early Childhood Stunting: Evidences from Brain Connectivity

Early childhood stunting (ECS) affects millions of children globally and conjectured to result in suboptimal brain and cognitive development later in life. Charting out the trajectory of brain network development and most importantly how the compensation of function can be achieved gives windows of intervention to clinicians, educators and policy makers. In this study, advanced network neuroscience tools, graph theoretical methods applied on diffusion weighted MR-imaging (DWI) revealed the effects of ECS on white matter (WM) organization in a community-based birth cohort of 170 children (mean age = 9.18 years, SD = 0.28) from Vellore, India. Based on stunting status at ages two, five, and nine years, children were categorised into four groups: always stunted (AS; n=21), stunted until five with catch-up at nine (S5C9; n=31), stunted until two with catch-up at five (S2C5; n=28), and never stunted or typically developing (TD; n=90). The catch-up groups showed strikingly similar anthropometric measures compared to TD. However, all stunted groups (AS, S5C9, S2C5) showed significantly lower performance and verbal IQ scores compared to TD children. DWI data revealed AS children exhibited shorter tract lengths across select cortico-cortical connections, an increased number and strength of short- and medium-range connections, and a corresponding reduction in long-range connections and strength. Network analyses further revealed that AS and S5C9 groups displayed higher local clustering and local efficiency relative to TD, reflecting greater local segregation of brain networks. Hub-like modules was broadly conserved across groups, although both catch-up groups (S2C5 and S5C9) showed additional hubs, suggesting compensatory network reorganization via a more modular architecture. Together, these findings provide novel evidence that ECS is linked to altered structural reorganization of brain networks and reduced cognitive performance in later childhood. While persistent stunting (AS) is associated with the most pronounced alterations, partial catch-up (S2C5 and S5C9) is accompanied by compensatory adaptations, such as increased short-range connectivity and recruitment of additional hubs. These results underscore the critical importance of early nutritional interventions to support optimal brain network development.

neuroscience↗

bioRxiv2026

Min-frame transformation enables more sensitive viral genome alignment

MotivationMaximal unique matches (MUMs) are a fundamental primitive in genome comparison, where they serve as high-confidence anchors for downstream multiple genome alignment. However, because MUMs rely on exact string matching, their effectiveness degrades with increased genome divergence and larger sets of genomes, inhibiting their ability to recover long homologous regions and reducing the number of base pairs covered by the multiple genome alignment. Additionally, existing approaches that improve robustness to mutation, such as spaced seeds or translated alignment methods, introduce trade-offs in specificity, scalability, or computational complexity. MethodsTo address this gap, we introduce the Min-Frame Transformation (MFT), a deterministic encoding of nucleotide sequences to sequences over a transformed alphabet that preserves the coordinate structure of the original sequence. At each position, the MFT selects a k-mer from a local window according to a fixed global ordering and assigns it a character in the transformed alphabet via a predefined mapping. This process captures local sequence context and can mask the impact of mutations, increasing the likelihood that homologous regions remain detectable as exact matches. The resulting transformed sequences can be indexed using standard string data structures, such as suffix arrays and suffix trees, enabling efficient extraction of MUMs without modifying existing algorithms. ImpactThe MFT is a novel computational approach for improving the robustness of MUM-based seeding for genome alignment by producing longer and more contiguous matches that span a greater fraction of the genome, leading to improved alignment coverage and SNP recall. Altogether, these improvements have the potential to result in improvements for downstream viral genome analysis applications such as phylogenetic inference and transmission analysis. FundingTandy Warnow: NSF grant 2316233 Todd J. Treangen: NSF grants 2126387, 2239114, NIH grants U19-AI144297, P01-AI152999

bioinformatics↗