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Quantitative Biology Genomics

q-bio.GN Watch Genomics

Tuesday 13 October · still arriving

Genomics · Cross-listed from q-bio.MN

Deep learning-based virtual perturbation reveals increasing heterogeneity of gene regulatory responses with aging

Yongjian He, Julia Hanna Haddad Guez, Xu-Wen Wang et al. (+1 more)

A central hypothesis in aging biology proposes that the gradual accumulation of stochastic molecular damage drives transcriptional deregulation (TD), ultimately leading to functional decline. However, existing single-cell analyses largely rely on static expression measurements and provide limited resolution of how regulatory responses change at the level of individual cells. Here, we leverage DKO, a deep learning framework that predicts cell-specific virtual gene perturbation effects from unperturbed transcriptomes, capturing cell-to-cell differences in regulatory context without requiring a reconstructed gene regulatory network or matched perturbation data. Applying DKO to single-cell transcriptomic datasets from humans, mice, Drosophila, and C. elegans revealed that the transcriptional consequences of perturbing the same gene became increasingly heterogeneous across cells with age, indicating that gene regulatory responses become less consistent and more dependent on cellular context. Furthermore, we find that cells with higher somatic mutation burdens have greater knockout-response variability, linking accumulated molecular damage to regulatory inconsistency. Within the same cellular populations, higher impact variability was also associated with higher senescence scores, linking transcriptional dysregulation to senescence-associated states. Our findings identify a progressive loss of regulatory robustness at both the gene and cell levels as a conserved feature of aging, linking stochastic molecular damage to increasing heterogeneity in cellular regulatory responses.
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All authors Yongjian He, Julia Hanna Haddad Guez, Xu-Wen Wang, Orr Levy

Genomics · Cross-listed from stat.ME

Spatial Kernel Restricted Canonical Correlation Analysis: Population Targets, Identifiability, and Stability

Zhen Miao, Yilong Qu and Nancy R. Zhang

Relating observations of different categories across space requires methods that accommodate distinct sampling locations and spatial dependence. Spatial kernel restricted canonical correlation analysis (skrCCA) uses spatial weights to identify associated feature combinations across data categories. Here, we develop a statistical theory of its covariance form after feature reduction. Conditional on the observed locations and under a mean-zero model, the pairwise association matrices target kernel-weighted averages of cross-covariances. For prespecified directions, we derive the exact null variance under independence between blocks. We establish recovery and stability bounds in two- and multiblock settings under explicit population models, and show that exact cyclic updates have convergent objective values. The variance characterization shows how spatial weighting and within-block dependence jointly determine the effective information available for estimating association. The recovery bounds separate statistical and optimization error, relating stability to population separation and the strength of connections between blocks. These results clarify what fitted spatial association axes estimate and provide conditions under which they can be recovered reliably.
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Genomics · Cross-listed from cs.AI

Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity

Xiaoqing Lian, Pei Liu, Jiahui Tan et al. (+3 more)

Single-cell RNA sequencing (scRNA-seq) enables cell-level analysis of biological heterogeneity, but learning effective representations from high-dimensional, unordered gene expression profiles remains challenging. Gene Expression-to-Image (GE2I) offers an effective learning strategy by arranging gene values on pixel grids for convolutional neural networks, but its underlying mechanisms remain unclear. We study GE2I on full-transcriptome data while keeping the network and training procedure fixed. Our results reveal a trade-off between spatial smoothness and gene-level fidelity. Structured mappings produce smooth, low-frequency patterns that support faster early optimization, whereas assigning multiple genes to the same pixel obscures expression differences and can reduce predictive performance. This trade-off explains why random mappings can compete with structured layouts when many genes are represented. One-to-one gene-to-pixel assignment improves prediction across the tested visual backbones even when image smoothness decreases. We further find that the benefits of pretraining and augmentation depend on the backbone and mapping. These findings provide practical guidance for pixel allocation and the transfer of vision priors to gene expression data.
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All authors Xiaoqing Lian, Pei Liu, Jiahui Tan, Quan Zou, Chen Lin, Xiangxiang Zeng

Monday 12 October

Genomics · Cross-listed from cs.DB

CellChain: Benchmarking Cross-Scale Evidence Use for Perturbation Reasoning

Xue Xia, Yue Wang, Shuizhou Chen et al. (+6 more)

Recent advances in foundation models and large-scale cellular perturbation datasets have driven growing interest in using LLMs to predict and reason about cellular responses. Biological reasoning in this setting involves relating molecular mechanisms, cellular responses, and functional outcomes across scales. Evaluating foundation models therefore requires assessing not only whether their predictions are correct, but also how those predictions depend on the biological evidence provided. We introduce CellChain, an experimentally grounded benchmark comprising 590 biological cases and 2,360 matched evidence-condition instances across gene-dependency prediction, cytokine-axis identification, and drug-sensitivity ranking. CellChain links perturbation experiments and external biological resources through a provenance-aware evidence graph spanning molecular, pathway, transcriptional, and phenotypic information. Task-specific conditions vary evidence coverage, composition, or assignment while preserving questions, candidate answers, reference targets, and scoring rules. Across 18 foundation models, the highest accuracies under prespecified target-evidence conditions are 57.5%, 75.5%, and 70.8%, respectively, and broader evidence coverage does not consistently improve accuracy. On the drug-sensitivity benchmark cohort, matched reference histories outperform swapped histories by an average of 13.0 percentage points in cluster-weighted accuracy. Decision-transition analysis separates corrections, newly introduced errors, and wrong-answer switches, revealing substantial answer changes hidden by small net accuracy differences. CellChain provides controlled comparisons for attributing changes in model predictions to prespecified expansions of biological context, including changes in evidence coverage, composition, and biological assignment.
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All authors Xue Xia, Yue Wang, Shuizhou Chen, Lang Yu, Hao Wu, Wenxuan Huang, Chengkai Yao, Zhangyang Gao, Siqi Sun

Genomics

CloneOT: predicting clone-specific perturbation responses with lineage-supervised neural inverse optimal transport

Yanshuo Chen, Wei Chen and Heng Huang

Predicting single-cell perturbation responses requires mapping between two unpaired populations of cells measured before and after the perturbation. Optimal transport (OT) provides a principled framework for constructing such mappings by minimizing the transport cost, and neural OT methods (e.g., CellOT) have been employed for this prediction task. However, these methods rely on a fixed transport cost, typically the squared Euclidean distance. This cost may connect cells with the wrong responses. Lineage tracing provides the information needed to learn the cost, as heritable barcodes link the cells of the same clone before and after the perturbation. Here, we propose \CloneOT, a neural inverse OT method that learns both the transport cost and the transport map from partially lineage-traced single-cell datasets. \CloneOT\ represents the cost as a neural network. It learns the cost by minimizing the extra cost that the lineage adds to the optimal transport, so that the cheapest transport keeps the cells of each clone together. Our experiments demonstrate that \CloneOT\ recovers the correct transport on 2D datasets, while the squared Euclidean cost fails. Moreover, \CloneOT\ outperforms five baselines on 14 lineage-tracing perturbation scenarios of cancer cells, for both seen and unseen clones. Furthermore, we show that \CloneOT\ reveals the clone-specific transcriptional programs of drug-tolerant persister cells and forecasts the fate of each clone from its cells before treatment. Our implementation is available at https://github.com/poseidonchan/CloneOT.
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Sunday 11 October

Genomics · Cross-listed from cs.CR

Training-Data Extraction from Genomic Language Models

Tianhao Mao, Zihao Wang, Dongfang Zhao et al. (+3 more)

Genomic language models generate long nucleotide continuations from short prefixes, exposing a direct route for studying training-sequence extraction through the generation interface. We present an output-only black-box attack on Evo 2 that selects convergent groups of repeated generations without gradients, token likelihoods, or training-corpus access during candidate selection. Recovered content is then scored by an evaluator combining aligned length, nucleotide identity, match multiplicity, and sequence complexity. Across 304 runs on Evo 2 7B with bacteriophage and plasmid prefixes, the attack yields 888 candidate clusters, of which 85 match training segments of at least 500 bp, giving a cluster-level attack success rate of 9.57%. Final filtering retains 22 uniquely matched extraction events at the 500-bp threshold and 15 at 1,000 bp, covering 14 and 7 distinct training-sequence regions, respectively. Within-category source-homology screening retains 11 of the 22 events at a 99% exclusion threshold and four at 98%, with the latter mapping to two reference regions. The results establish convergence among repeated nucleotide generations as a practical output-only signal for recovering long training-sequence segments and characterize how recovery depends on prefix length, cluster size, perplexity, early stopping, and sequence domains.
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All authors Tianhao Mao, Zihao Wang, Dongfang Zhao, Rui Zhu, Xiaofeng Wang, Haixu Tang

Saturday 10 October

Genomics

Homogeneity and heterogeneity of functional element composition of genome units across human and seven model organisms

Yanying Wu

Is a chromosome a miniature of its genome, with the same mix of functional elements as the whole? We ask this of eight annotated genomes: human, mouse, zebrafish, Caenorhabditis elegans, Drosophila melanogaster, Saccharomyces cerevisiae, Arabidopsis thaliana and maize. Elements are classified in a four-level hierarchy, from five major classes (protein-coding genes, ncRNA genes, transposable elements (TEs), pseudogenes, IG/TR gene segments) down to finer classes. Units are chromosomes or arms, cytogenetic bands, equal cuts of each chromosome, and random segments of matching size. We compare each unit with the genome and with other units, call compositions homogeneous when they differ by less than 5%, and score each against a random sample of the same size. The first finding is a surprising homogeneity. The level-1 compositions of the major chromosomes match each other and the genome in maize, zebrafish, yeast and C. elegans, and in fly down to the major arms. Finer classes can be homogeneous too: protein function in Arabidopsis, and the Wicker TE orders in maize. Enzyme reaction classes are interchangeable between chromosomes in all eight genomes. The second finding is a structured heterogeneity. Where a unit departs from its genome, it does so in one of three ways: clusters of named elements, such as rRNA genes or immune receptor loci; megabase-scale compartments, such as TE-rich pericentromeric heterochromatin; and lineage-specific gene families, visible only at the finest levels. Corrected for chance, the genomes fall into groups. For protein function and gene families, composition is more uniform in plants and fly than in vertebrates, and least uniform in C. elegans. For the major classes and TE orders, uniformity follows where each genome places its repeats, along the arms (maize, zebrafish) or around the centromeres (Arabidopsis, fly), not its lineage.
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Genomics · Cross-listed from cs.MS

manylp: reproducible, exact, and batched linear programming for genome-scale dynamic flux balance analysis

Fabio Cumbo

Dynamic flux balance analysis (dFBA) and its community, spatial and ensemble variants predict microbial metabolism by solving one linear program (LP) per species and time step, often 10^6 to 10^8 LPs per study. These LPs share their constraint matrix and objective and differ only in their bounds, yet general-purpose solvers treat each one independently, and GPU first-order solvers struggle with the degenerate, badly scaled matrices of genome-scale models. Moreover, metabolic optima are usually faces rather than vertices, so solvers return arbitrary flux vectors and dFBA predictions depend on the solver used. We present manylp, a batched solver for such bounds-parametric LP families that returns certified vertex solutions. Building on the bunching technique of stochastic programming, manylp caches optimal bases and certifies whole batches with one dense matrix product per basis on CPUs or GPUs; LPs outside the cached critical regions are re-solved by a warm-started lexicographic dual simplex, and new bases are propagated across the batch. Each LP is certified optimal and unique under a lexicographic flux rule, or infeasible, in independent double-precision arithmetic. On a genome-scale dFBA workload, manylp certified 47,889 LPs per second on a CPU, 12 to 33 times faster than HiGHS, Gurobi, FICO Xpress, GLPK and OR-Tools (32 processes), and reached 682,000 LPs per second on one GPU for community ensembles, needing 76 simplex solves for 94.6 million LPs. Integrated into the muODE simulator, it accelerated single trajectories 84-fold and 16-member ensembles 635-fold, and made adaptive Runge-Kutta integration affordable, reducing biomass errors 83-fold. Integrating 49 equally optimal flux policies in one batched run showed that alternative optima change predicted acetate 4.2-fold and shift community composition. manylp is open source: https://github.com/cumbof/manylp.
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Friday 9 October

Genomics · Cross-listed from cs.LG

Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory

Vakul Raghavan, Utkarsh Grover, Mingyang Mao et al. (+2 more)

Processing a long sequence does not guarantee that distant information remains usable. As context grows, a fixed-size memory must retain many associations, connect information across positions, and preserve relevant structure without being overwhelmed by everything it observes. These are distinct requirements: a memory may store more information without representing the relationships needed for retrieval, and it may transport those relationships without preserving the ones that matter. Thus, the challenge is not simply extending context length, but constructing a memory that remains usable as context grows. We study this problem in large-chunk test-time training (TTT) and identify two basic failures: single-step writes accumulate interference, limiting capacity, while self-associative writes cannot form cross-position bindings. We introduce Double-Take (DT), which repairs these failures with multi-step unpinned updates and cross-position mixing, and adds bidirectional change-sensitive writes for selective retention. Capacity alone leaves end-to-end recall at the $0.10$ chance floor; adding binding raises it to $0.92$. Trained only on $32\,\mathrm{kb}$ genomic contexts, DT reaches $0.927$ AUROC at $131\,\mathrm{kb}$ and $0.856$ near $1\,\mathrm{Mb}$, while transferring zero-shot from human to mouse. Together, these results identify capacity, binding, and selectivity as three requirements for usable long-range memory.
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All authors Vakul Raghavan, Utkarsh Grover, Mingyang Mao, Chengwen Wang, Xiaomin Lin

Thursday 8 October

Genomics

Unlocking the Regulatory Genome by ARGUS: An Evidence-Constrained Agentic Framework for Interpreting Single Nucleotide Variants

Pratik Dutta, Matthew B. Obusan, Max Chao et al. (+3 more)

Over 90% of disease-associated variants from genome-wide association studies fall in noncoding regulatory regions, yet their functional interpretation remains a central open problem in genomic medicine. Large language models prompted to interpret such variants routinely hallucinate transcription factor (TF) binding changes, fabricate experimental support, and assign biological significance to statistically negligible signals. We present ARGUS (Agentic Regulatory Genomics for an Uncertainty-aware Scientist), which strictly separates deterministic biological computation from LLM-mediated reasoning. ARGUS wraps 458 DNABERT-based TF binding models in a hypothesis-directed investigation loop where a planner selects evidence sources based on current uncertainty, a verifier deterministically interprets each observation, and intermediate results change the investigation path. On variant rs6983267 at the 8q24 cancer risk locus, the same planner produces four divergent trajectories for four TFs. FOXA1 is rescued in 3 steps when real ADASTRA allele-specific binding data (15 experiments, FDR = 0.030) reveals a model false negative masked by saturation. KLF6 traverses 8 steps across ADASTRA, JASPAR motif analysis, and ENCODE cCRE regulatory annotation before abstaining due to mixed indirect evidence. RAD21 abstains in 8 steps after ADASTRA returns a coverage-qualified but nonsignificant allelic test (5 experiments, FDR = 0.65), and SP1, which shares FOXA1's saturated retained prediction, abstains because no direct experimental evidence exists at this locus. All observations come from real ADASTRA, JASPAR, and ENCODE cCRE queries; none are simulated. A comparison of fixed-priority and LLM-mediated planning shows that the LLM planner reaches identical verdicts with fewer tool calls by declining evidence that cannot resolve the claim under test.
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All authors Pratik Dutta, Matthew B. Obusan, Max Chao, Rekha Sathian, Nimisha Papineni, Ramana V. Davuluri

Genomics · Cross-listed from cs.LG

Predictive Multiplicity in Cell-Fate Assignment: Label-Free Rashomon Sets and the Limits of Per-Cell Certification

Arjun Bhupatiraju and Abhiram Bhupatiraju

Single-cell trajectory inference maps transcriptomic measurements onto developmental continua, yet configurations that fit the data equally well can assign conflicting cell fates. FateMultiplicity is a label-free framework that constructs a statistically admissible model set, or Rashomon set, without lineage labels, by evaluating model discrepancy on cross-fitted held-out genes under non-inferiority testing calibrated against random-seed variation. Multiplicity is large and depends more on the diversity of the model space than its size: twelve configurations of a second algorithm expose 20.0% of cells where twenty-four of the first expose 3.8%. Whether the per-cell certified fate margin FM yields more reliable assignments than the fitted model already provides is then tested, and it does not. On simulation ground truth, on the same cells, FM discriminates misassignment at AUC 0.682, against 0.965 for the baseline configuration's own decision margin (p = 0.003) and 0.854 for a seed-dispersion baseline. Informativeness is governed by the breadth of the admitted set, not its cardinality: at cardinality four, seed refits give 0.933 and hyperparameter-perturbed sets 0.701. Relaxing the infimum to a q-quantile recovers discrimination but converges toward the single model's own confidence; the supremum reaches 0.973 because theta*'s membership bounds it from below, while the infimum is unanchored. Multiplicity in trajectory inference is worth measuring and reporting, but per-cell certification over a label-free Rashomon set is not a route to more reliable fate calls. Two constructions survive: a margin-erosion ratio separates real from spurious branch points in simulation (AUC 0.890, untested on real data), and against clonally observed fate, uncertified cells disagree with their clone's outcome 16.4 percentage points more often than certified cells (p < 0.001).
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Wednesday 7 October

Genomics · Cross-listed from cs.DS

Taxonomic Classification with Tag Arrays

Travis Gagie and Gonzalo Navarro

Taxonomic classifiers based on exact matches differ in which matches they use and in how each match splits its credit among the taxa containing it: Kraken~2 gives a $k$-mer's credit to the lowest common ancestor of those taxa, SPUMONI~2 labels each position of the read, Tagger gives each maximal exact match (MEM) one label, and Cliffy splits a match's credit evenly over a roughly logarithmic sample of the taxa. We compare these schemes on the same long MEMs, mainly on short 16S rRNA reads classified to genera with SILVA, where credit proportional to the number of occurrences of the MEM in each taxon is the most accurate, by 1.0 to 6.8 percentage points. We compute long MEMs with Boyer–Moore–Li on a run-length compressed suffix array, and count their occurrences in each taxon with a complete, run-length compressed tag array, in time proportional to the number of tag runs in the MEM's BWT interval, without locating any occurrence. We bound the number of tag runs in terms of the BWT runs and of how often suffixes with long common prefixes have different tags, and show that tags constant on $d$ blocks of the text have a macro scheme with at most $r + 2d$ phrases. On SILVA our index takes 1.44\,GB and classifies a read in 66\,$\mu$s with one thread at 93.8% accuracy, about what Cliffy reports for its 9\,GB index; over minimizer digests it takes 0.49\,GB and reaches 92.5%, against 84.2% for Cliffy's digested index on the same reads. On nanopore reads from whole genomes, where almost every long MEM occurs in one species, we instead use each read's longest MEMs, found in one pass of a variant of Boyer–Moore–Li with a $k$-mer filter, which assigns 99.6% of the reads to their species with no length threshold.
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2610.10500 · 7 Oct · Preliminary version: The ideas in this paper are the authors'; the code, the experiments and the text were developed by Claude Opus 5.5, an AI model by Anthropic, under the authors' direction. We are in the process of verifying the results, including running a comparison with Cliffy, which we cannot build on the commodity machine we are currently using

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Genomics

Truecell reproduces R Seurat's single-cell analysis outputs natively in the Python ecosystem

Nuzla Ismail, Shanika Amarasoma, Michelle Galeas-Pena et al. (+1 more)

Single-cell RNA-sequencing analyses predominantly use either Seurat (R) or Scanpy (Python), with the choice often driven by programming language preference. However, by default, these tools produce divergent variable features, neighbour graphs, clusters, and marker genes. Laboratories requiring Python, which supports most deep-learning, foundation-model, and agent tools, must re-implement Seurat analyses in a framework that does not replicate Seurat's results. Truecell was developed as a Python implementation of the Seurat interface, preserving function names, arguments, default settings, and the object model to facilitate seamless transfer of analyses. In eighteen paired end-to-end evaluations against R Seurat, deterministic outputs matched to floating-point precision, and fold-change order was identical across all nine differential expression tests. In a three-arm benchmark on three datasets, with fixed user parameters and 20 seeds per tool, Truecell more closely reproduced Seurat's clustering than a Seurat-configured Scanpy in all 12 combinations of dataset and resolution settings. Truecell's marker genes and enriched pathways were also closer to Seurat's than Scanpy's were, and pseudobulk DESeq2 reproduced Seurat's gene lists with a Jaccard index ranging from 0.95 to 1.00. This agreement reflects fidelity to Seurat rather than biological correctness.
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All authors Nuzla Ismail, Shanika Amarasoma, Michelle Galeas-Pena, Sarah C.Glover

Tuesday 6 October

Genomics

Beyond the Transcriptome: Chromatin-Informed Prediction of Cell-State-Dependent Perturbation Responses

Jiafa Ruan, Chenyang He, Ruijie Quan et al. (+3 more)

Predicting transcriptional responses to genetic perturbations is central to understanding gene function. Existing predictors primarily rely on transcriptomic measurements, although chromatin accessibility provides complementary information about the cellular context in which perturbations act. Using this information requires linking chromatin context to specific perturbations and accounting for baseline differences between independently sampled control and perturbed populations. We propose ChromaPert, a chromatin-informed framework for predicting state-dependent perturbation responses. ChromaPert combines molecular and DNA-informed locus priors with paired control RNA–ATAC features to jointly represent target identity and measured cellular context. Its Chromatin-Guided Response Router (CGRR) builds a source bank of control-derived expression offsets and retrieves relevant sources using perturbation/locus or control-ATAC similarity. These offsets account for baseline differences, while conditional transport flow learns the remaining response. Across three unseen-target and held-out-state settings on K562 CAT-ATAC and Perturb-Multiome, ChromaPert achieves the highest all-gene response correlation among evaluated methods. When transferring known perturbations to held-out states, it improves correlation by 23.2% across all genes and 46.2% for the twenty most responsive genes over the strongest respective baselines. Correct RNA–ATAC pairing improves recovery of chromatin-associated response slopes over shuffled pairing, while cross-state predictions retain state-specific responses to the same perturbation.
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All authors Jiafa Ruan, Chenyang He, Ruijie Quan, Fanglei Xue, Zongxin Yang, Yi Yang

Genomics

HRPv2: an automated and enhanced method for full-length homology-based R-gene prediction

Giuseppe Andolfo, Thomas Holzweber, Juliane C. Dohm et al. (+1 more)

Motivation: Plant disease resistance genes, particularly those encoding NB-LRR proteins, are important targets for crop improvement. Proteome-based domain or motif searches can only identify NB-LRRs among existing gene models, meaning they cannot recover loci that have been missed or incorrectly predicted by the reference annotation. The full-length, homology-based R-gene prediction (HRP) method circumvents this issue by reconstructing gene models directly on the genome. However, the original implementation of this method requires several separate phases of classification, comparison and filtering operations. Results: HRPv2 is an automated, enhanced version of the original strategy. The labour-intensive, step-by-step curation process used in HRP has been replaced by a reproducible filtering framework. Enhancing two steps of the homology search process has enabled HRPv2 to better account for the specific NB-LRR variability of the genome. Performance validation confirmed that the number of full-length NB-LRRs annotated in both the automatically predicted gene set of the respective genome assembly and the final NB-LRR repertoire has increased in HRPv2 compared to HRP. Availability and implementation: HRPv2 and its associated documentation and reproducible test data are available at https://github.com/AndolfoG/HRPv2. Detailed installation and dependency information is provided in the repository README. A version-pinned Conda package has also been developed and locally validated to provide a reproducible execution environment.
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All authors Giuseppe Andolfo, Thomas Holzweber, Juliane C. Dohm, Heinz Himmelbauer

Monday 5 October

Genomics · Cross-listed from econ.GN

Random Genetic Variation Links Personality to Earnings and Wealth

Leandro Carvalho, Sjoerd van Alten, Silvia Barcellos et al. (+5 more)

Personality is associated with economic outcomes, but whether these relationships are causal remains unclear. We exploit within-family variation in genetic propensity toward the Big Five generated by random genetic transmission from parents to offspring. Combining polygenic indexes from the Lifelines cohort study with 19 years of administrative tax records in the Netherlands, we find that greater genetic propensity toward emotional stability increases earnings and household wealth, while genetic propensity toward conscientiousness increases household wealth. The other three traits show no consistent pattern across outcomes and specifications. The effect of genetic propensity toward emotional stability on earnings remains positive as we progressively account for genetic propensity toward educational attainment, cognitive performance, and depression. These findings strengthen the evidence that personality causally affects earnings and wealth.
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All authors Leandro Carvalho, Sjoerd van Alten, Silvia Barcellos, Stephen Dorn, Titus Galama, Catharina A. Hartman, Qiongshi Lu, Dilnoza Muslimova

Sunday 4 October

Genomics · Cross-listed from cs.LG

TempoBridge: Source-Conditioned Flow Matching with Optimal Transport Couplings for Single-Cell Population Transitions

Bowen Han, Lingbei Meng, Shihuan Luo et al. (+7 more)

Destructive single-cell measurements provide unpaired population snapshots rather than observations of the same cells across conditions. Local cell states and transition requests may also be insufficient to distinguish responses across source populations. We introduce TempoBridge, a common source-conditioned transport formulation for temporal, genetic, and chemical population transitions. Source cells initialize latent transport and provide a fixed empirical population summary. The velocity field receives this summary alongside the evolving cell state, flow time, and a structured transition descriptor. Minibatch optimal transport (OT) supplies couplings only for conditional flow-matching training paths; inference requires neither target expression nor OT computation. On held-out donors, TempoBridge achieves an Energy distance of 0.129 versus 0.144 for scGen. Genetic mean-expression $L_2$ error is 2.261 versus 3.156 for scGPT-scratch under Seen 2/2. On held-out compounds, condition-averaged drug-effect correlation is 0.598 versus 0.561 for the CellFlow adapter. Temporal ablations show higher mean distributional error after removing source context, replacing optimal transport with random pairing, or replacing flow matching with static residual regression. Together, these results demonstrate the predictive utility of a common source-conditioned transport formulation across held-out donors, gene combinations, and compounds.
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All authors Bowen Han, Lingbei Meng, Shihuan Luo, Yupeng Zang, Wenlin LI, Peize He, Yaodi Luo, Lian Zhang, Jianqing Zhu, Jinchao Xu

Saturday 3 October

Genomics

Tracing model-generated DNA with position-independent watermarking

Kimon Antonios Provatas1, Christos Galanopoulos and Ilias Georgakopoulos-Soares

Genomic language models can write synthetic DNA that carries no record of its origin. A generation-time watermark could provide a provenance signal, if a verifier can detect it using only the DNA sequence, a key, and published detector settings, without knowing where the generated region starts, which strand it lies on, or how it was divided into six-base tokens. We embed the SynthID tournament watermark in two genomic language models, Carbon and GENERator-v2. The verifier searches both strands, every start position, and four window lengths, and corrects its decision for the whole search. In a detection cohort of 1,544 held-out prompts, it found all 3,088 marked sequences per model, unedited and after one substituted, inserted, or deleted base. For ordinary sequences, the one-sided 95% upper confidence bound on the false-positive rate was at most 0.850%; one bound for the wrong-key control reached 1.015% (Carbon, after a deletion). In a development cohort, neither model showed a detectable change in likelihood or in predefined sequence measures. Under random edits placed without reference to the detector, detection stayed complete or nearly complete up to a 2% per-base edit rate. These results show statistical detection, not biological function, secret-key security, or robustness to an editor who sees the detector.
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Genomics

A Unified Unsupervised Framework for Genome-Wide Association Studies in Heterogeneous Populations

Xiong Shen, Zhenshuang Tang, Yong Liao et al. (+14 more)

Genome-wide association studies (GWAS) have greatly advanced the discovery of genetic variants underlying complex traits and diseases. Yet in heterogeneous populations, existing GWAS strategies typically either pool all individuals under an assumption of population homogeneity or perform meta-analysis across predefined subgroups, both of which are limited when latent genetic heterogeneity attenuates subgroup-specific effects and masks true associations or subgroup labels are imprecise. Here we present UCALM, a unified unsupervised framework that infers genetically homogeneous subgroups directly from the data and integrates subgroup-specific GWAS with a novel layered meta-analysis method to capture both shared and subgroup-specific association signals. Through extensive simulations and analyses of large-scale human and livestock cohorts, including the UK Biobank ($n \approx 487{,}000$) and a heterogeneous pig cohort ($n \approx 85{,}000$), we demonstrate that UCALM substantially alleviated the mean genomic inflation across 24 UK Biobank traits to 1.17 compared with 1.43 for GLM and 1.37 for LDAK-KVIK, and further revealed 74 loci in the pig cohort that were previously obscured by conventional approaches. Our results establish a robust and broadly applicable strategy for association mapping in structured populations and improve the resolution of genetic signals across diverse species.
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All authors Xiong Shen, Zhenshuang Tang, Yong Liao, Zhiyuan Lin, Huajun Zhou, Haohao Zhang, Yangfan Liu, Dong Yin, Yue Wang, Yuan Quan, Zhuqing Zheng, Xiong Xiong, Yuhua Fu, Shuhong Zhao, Xinyun Li, Lilin Yin, Xiaolei Liu

Friday 2 October

Genomics · Cross-listed from stat.ME

On the sensitivity to hyperparameters of a Bayesian method to infer selection signatures

Carlos A. Martinez, Ronnal E. Ortiz, Nelson A. Cruz et al. (+2 more)

Motivated by a real-life problem, namely, inferring genomic regions affected by selection, we studied some properties of a two-step method that performs this task based on the FST. This approach exhibits computational simplicity as an appealing feature. Step one involves fitting a conjugate Bayesian model to find point estimates of FST and selecting the markers to be considered in step two, where they are grouped using such estimates. Subjective priors are frequently used in this problem and large hyperparameters may be encountered; hence, there is interest in addressing their impact. Preliminary analyses suggested that, the number of markers selected in step one tends to zero when the hyperparameters have the same value and tend to infinity, or are different but tend to infinity and their difference tends to zero. Thus, we studied how hyperparameters affect the posterior mean, variance, and predictive density of allele frequencies, and the posterior distribution of FST, formally. Moreover, an empirical approach based on simulated and real data was performed. Our main findings are summarized in two theorems that set the asymptotic behavior of the method, providing a foundation to understand the aforementioned phenomena. As to the empirical studies, the groups created in the second step, the estimated posterior means and the number of selected markers showed high sensitivity to hyperparameters. Albeit the mathematical intractability of the posterior distribution of FST, our results revealed asymptotic properties explaining two features of the method that lacked a formal elucidation.
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All authors Carlos A. Martinez, Ronnal E. Ortiz, Nelson A. Cruz, Carmen H. Cepeda, Juan Sosa

Genomics

Generating eukaryotic reference genome assemblies: Earth BioGenome Project quality standards and recommendations

Kerstin Howe, Dolores R. Agius, Tyler Alioto et al. (+45 more)

Reference genome assemblies are foundational resources across the biological sciences as they begin to expose the fundamental building blocks of each species and the molecular toolkits vital for adaptation and survival. High-quality genomes enable insights into evolution, facilitate ecosystem monitoring and protection and conservation of species, access to new biomaterials and biomedicine, advances in agri- and aquaculture, and support planetary health. The amount of information gathered from a species' genome assembly is directly dependent on its quality. However, different sequencing technologies and software solutions generate a wide range of quality outcomes. Here, the Earth BioGenome Project's Sequencing and Assembly committee, together with a large community of researchers performing eukaryote sequencing and assembly, outlines quality standards for a "reference" assembly and formulates recommendations to ensure these standards are met.
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All authors Kerstin Howe, Dolores R. Agius, Tyler Alioto, Jean-Marc Aury, Roberto Biello, Mark Blaxter, Matthew W. Brown, Tom Brown, Bernat Burriel-Carranza, Haoyu Cheng, Dario Copetti, Solenne Correard, Fernando Cruz, Paul Davis, Aleksandra Denisova, Giulio Formenti, Pedro A. F. Galante, Caroline Howard, Kamil S. Jaron, Erich D. Jarvis, Sergei Kliver, Sergey Koren, Ilia J. Leitch, Harris Lewin, Heng Li, Kerstin Lindblad-Toh, Jack A. Medico, Matthieu Muffato, Zemin Ning, Javan Okendo, Shujun Ou, Marc Palmada-Flores, Brendan J. Pinto, Aurora Ruiz-Herrera, Abdoallah Sharaf, Ying Sims, Jeramiah J. Smith, Marco Sollitto, Cibele G. Sotero-Caio, Marcela Uliano-Silva, Sibelle T. Vilaça, Robert M. Waterhouse, Melissa A. Wilson, Chentao Yang, Guojie Zhang, Chenxi Zhou, Yang Zhou, Richard Durbin

Thursday 1 October

Genomics · Cross-listed from cs.LG

Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening

Ziyuan Gao

High-throughput perturbation screens increasingly use machine-learning models to predict the effects of gene combinations that were never measured. A difficult case arises when both genes in a test pair were seen individually during training, but their combination was not. In this setting, standard OOD methods often fail because neither gene appears unfamiliar on its own. We therefore compare each model prediction with a simple additive prediction obtained by summing the two observed single-gene effects. The difference between them, which we call additive disagreement (AD), measures how much extra interaction effect the model introduces for an unseen combination. Across eight prediction methods and three Perturb-seq datasets, specialised neural predictors do not consistently outperform this simple additive baseline. More importantly, predictions with larger deviations from the additive baseline tend to have larger errors. Conventional output-based and embedding-based confidence signals are inconsistent across datasets and models, whereas AD remains informative across all evaluated compositional settings. We further study how this signal can guide decisions in an agentic perturbation-screening workflow. The code is available at https://github.com/zygao930/OOD.
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Genomics

GCTAg: scalable mixed-model analysis for biobank-scale agricultural cohorts

Alexander S. Leonard, Qiongyu He, Natasha Watson et al. (+2 more)

Genome-wide association studies identify genomic variants associated with traits. Mixed linear model association (MLMA) methods using a whole-genome relationship matrix, such as those implemented in GCTA, are powerful but computationally expensive. Here, we remove key memory and CPU bottlenecks in GCTA, reducing REML memory usage by nearly 75% and substantially accelerating MLMA by orders of magnitude in biobank-scale cohorts while preserving exactness. We further exploit relatedness in the mapping cohort through a reduced-rank Woodbury matrix approach, delivering further orders of magnitude performance gains with controlled genomic inflation. Native on-the-fly dominance recoding also eliminates slow I/O-operations on intermediate files, enabling efficient additive and dominance MLMA analyses in large agricultural cohorts.
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All authors Alexander S. Leonard, Qiongyu He, Natasha Watson, Naveen Kumar Kadri, Hubert Pausch

Wednesday 30 September

Genomics · Cross-listed from cs.LG

From DNA Design to DNA Slimming: Auditable Agentic Discovery of a Deletion-Only Designer

Joel Shor

Compact regulatory DNA can free up space in vector payloads, reduce synthesis and assay burden, and expose which sequence features drive predicted activity. Yet most model-based nucleic-acid designers optimize fixed-length sequences through substitutions; they do not ask which bases of an existing functional element can be removed while retaining predicted activity. We define the task of sequence slimming as selecting an exact-length, order-preserving subsequence while retaining activity. Modeled on the design benchmark NucleoBench, we propose a quantitative evaluation for slimming that balances sequence reduction with maintaining function. Each slimmer must return both the subsequence and its source indices, which can be used to verify that the slimmer obeyed task requirements. To our knowledge, this is the first dedicated benchmark of this deletion-only problem. The coding agent Empirical Research Assistant (ERA) then searched over executable designer programs. ERA received the task prompt and a successful substitution-only designer GrAdaBeam as a starting program, and it modified the designer to produce GRADASLIM. We report held-out evaluations for five transcription-factor binding targets, comparing random, greedy, and ERA-guided slimming at 400 and 100 bp. ERA has the highest mean in 9/10 settings. Paired bootstrap intervals for ERA minus greedy are above zero in all five 400-bp settings, below zero in one 100-bp setting, and overlap zero in the remaining four.
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Genomics

MerKurio: sequence extraction and annotation based on matching k-mers

Lukas Schönmann, Heinz Himmelbauer and Juliane C. Dohm

K-mers, short subsequences of length k, play an important role in computational analyses using biological sequence data. A critical downstream processing step involves getting back to the source sequences of selected k-mers for further analysis or validation. Here, we present MerKurio, a high-performance command line tool written in Rust with two main functionalities: 1) extracting sequence records from FASTA/FASTQ files using k-mers and 2) annotating or filtering aligned sequences in SAM/BAM files with k-mer tags. The tool automatically selects the pattern matching algorithm via empirically derived rules based on query characteristics and supports paired-end reads, compressed input files, and reverse complement searching. Benchmark comparisons demonstrate that MerKurio outperforms existing tools in terms of speed while providing additional utility including detailed matching statistics and comprehensive file format support. MerKurio is a fast and user-friendly tool for sequence record extraction and tagging of aligned sequences based on k-mers. MerKurio is freely available at https://github.com/lschoenm/MerKurio.
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Genomics

CellMSA: Context Modeling for Single-Cell Representation Learning

Suyuan Zhao, Minghao Liu, Yizhen Luo et al. (+1 more)

Single-cell transcriptomics enables profiling of cellular states at unprecedented resolution, but its high dimensionality, sparsity, and technical batch effects pose significant challenges for representation learning. Existing single-cell foundation models typically encode each cell independently or only model cells from the same batch for denoising, thereby underutilizing the rich relational information across batches and cell types to model gene expression patterns. We argue that single-cell models can benefit from more informative cell-context modeling. By comparing consistency and variation across cells, models can capture fine-grained gene-gene dependencies associated with cell states, which are essential for learning high-quality representations. Inspired by the use of multiple sequence alignment (MSA) context in protein modeling, we propose CellMSA, a single-cell representation learning framework that introduces an MSA-inspired inductive bias into transcriptomic modeling. For each target cell, CellMSA retrieves relevant cells from different batches and biologically related cell types as context, and summarizes cross-cell patterns into a context-dependent gene-pair representation. This representation is then injected into a pair-aware target-cell encoder for fine-grained representation learning. We pretrain CellMSA on a large-scale human single-cell corpus of approximately 109 million cell observations, including 65.6 million primary observations. Experiments show that our framework consistently outperforms existing methods across multiple benchmarks. Code is available at the following repository: https://github.com/PharMolix/CellMSA.
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All authors Suyuan Zhao, Minghao Liu, Yizhen Luo, Zaiqing Nie

Tuesday 29 September

Genomics

Fragment Path Complex Networks for Viral Genome Classification

Alice Wachira, Xiang Liu and Guo-Wei Wei

Accurate classification of viral families is essential to understanding genomic diversity, but rapid evolution and heterogeneous genome structure complicate reliable assignment. Newly discovered viruses, particularly those from underrepresented ecosystems, expand the known diversity of viral genomes. This growing diversity poses challenges for classification methods that rely on similarity to existing references. We present Fragment Path Complex Networks (FPCN), a framework that represents viral genomes as sequences of ordered fragments at various scales. FPCN captures contextual sequence information within each fragment and uses path complexes to model complex relationships between neighboring fragments. We evaluated FPCN on well-established NCBI benchmarks, and FPCN achieved higher classification accuracy than competing methods. Further evaluations on newly released datasets revealed FPCN's ability to generalize to genomes that were not presented in the training. Together, these results position FPCN as an effective tool for classifying viral genomes within known families across benchmark datasets and newly released genomes.
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Genomics

CancerZigZag: Iterative Seed-Anchored Diffusion for Generative Modeling of Single-Cell State Transitions

Johannes Schlüter and Alexander Schönhuth

Single-cell cancer datasets are predominantly cross-sectional and rarely provide paired or longitudinal observations linking individual healthy-like cells to tumor-associated states. We introduce CancerZigZag, a seed-initialized diffusion-based framework for exploratory generation of tumor-associated single-cell candidate clouds from unpaired epithelial cell populations. For each cancer context, a diffusion model is trained exclusively on tumor-derived epithelial cells and applied through repeated partial latent-space perturbation and reverse diffusion to held-out healthy-like seeds, generating stochastic candidate clouds without paired measurements or classifier guidance. We applied CancerZigZag to colorectal, breast, lung, and renal cell carcinoma contexts and explored parameter landscapes defined by perturbation depth and the number of ZigZag cycles. Across the reported operating configurations, candidate clouds contained outputs classified toward held-out tumor-derived reference populations for each evaluated seed. Residual seed-dependent organization varied across contexts, with the clearest structure in colorectal cancer, more modest organization in lung cancer, and limited cloud-level structure in breast and renal cell carcinoma. Representative candidates also showed directional concordance with transcriptional shifts observed between held-out healthy-like and tumor-derived reference populations. CancerZigZag is not interpreted as a model of deterministic healthy-to-tumor transformation or cellular progression. Instead, it provides a reference-informed framework for exploring tumor-associated candidate distributions from unpaired healthy-like seeds and quantifying the context-dependent relationship between tumor-associated displacement and residual seed dependence.
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Monday 28 September

Genomics · Cross-listed from cs.AI

GenoMorph: Pathway-Grounded Genomic Disease Reasoning via Adaptive Latent Computation

Tanmoy Kanti Halder, Akash Ghosh, Arijit Roy et al. (+1 more)

Large language models (LLMs) have demonstrated strong capabilities in biological reasoning; however, genomic disease inference remains largely dependent on memorized gene-disease associations rather than understanding biological pathways. This shortcut learning undermines robustness and generalization, and breaks down when molecular identifiers are unavailable. We present GenoMorph, a multimodal genomic reasoning framework that shifts disease prediction from associative gene-disease mapping toward pathway-grounded reasoning. GenoMorph couples a frozen DNA foundation model with question-conditioned cross-attention fusion, self-adaptive latent reasoning (LatentSp), a residual reasoning gate for iterative genomic evidence reinjection, and rejection sampling fine-tuning regularized by hierarchical optimal transport (OT). Rather than learning direct gene-disease mappings, GenoMorph aligns genomic sequence representations with latent pathway dynamics, enabling reasoning trajectories that follow molecular interactions before producing disease predictions. LatentSp dynamically allocates computation according to reasoning confidence, reducing unnecessary reasoning steps and improving inference efficiency. We further construct an anonymized benchmark from the Kyoto Encyclopedia of Genes and Genomes (KEGG), replacing every gene and molecular identifier with anonymous symbols while preserving sequences and pathway topology, thereby removing memorization shortcuts. GenoMorph raises the weighted F1 from 0.7863 (BioReason) to 0.9412, and rejection sampling fine-tuning with self-adaptive latent reasoning pushes it to 0.9725 while cutting latency nearly 60%. On the anonymized benchmark it reaches 0.9465 F1, substantially outperforming prior systems and confirming that accurate disease prediction can arise from pathway reasoning rather than memorized gene-disease associations.
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All authors Tanmoy Kanti Halder, Akash Ghosh, Arijit Roy, Sriparna Saha

Sunday 27 September

Genomics

Generalizable single-cell perturbation response prediction using energy-guided flow matching

Jianan Wei, Jiajun Hong, Guikun Chen et al. (+3 more)

Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems. However, existing methods typically rely on fixed mappings learned during training, making it challenging to calibrate distribution shifts or adapt to novel perturbation conditions during inference. Here, we present scEGFlow, an energy-guided flow matching framework that dynamically bridges control and perturbed cellular states. scEGFlow models continuous transitions from control cell populations to perturbed states using conditional flow matching. It then applies condition-specific energy gradients to correct and steer these predictions, enabling flexible adjustments without retraining the flow model. Evaluations across benchmarks spanning imaging phenotypes and transcriptomic profiles show that scEGFlow outperforms existing methods in reconstructing response distributions under both seen and unseen perturbation conditions, faithfully preserving cellular manifold geometry and population heterogeneity. This advantage is notable when adapting to new conditions with only a few measured cells, consistently improving prediction accuracy. Furthermore, scEGFlow accurately recapitulates perturbation-induced up- and down-regulation patterns across consensus gene expression signatures, where energy guidance improves the agreement between predicted and observed regulatory directions. Ultimately, these findings demonstrate that scEGFlow provides a modular, generalizable, and steerable solution for single-cell perturbation modeling. By grounding generative flows in learned biological landscapes, this architecture establishes a new computational paradigm for navigating and manipulating cellular behavior in silico.
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All authors Jianan Wei, Jiajun Hong, Guikun Chen, Ning Yang, Lifeng Fan, Wenguan Wang