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This feed contains articles for bioRxiv Subject Collection "Genomics Bioinformatics"
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<title>bioRxiv</title>
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<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.756023v1?rss=1">
<title>
<![CDATA[
Machine learning classifies subcellular Golgi morphology from imaging flow cytometry during inflammatory activation: an exploratory study 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.756023v1?rss=1
</link>
<description><![CDATA[
Inflammatory activation remodels the Golgi apparatus to meet increased secretory demand, yet whether these structural changes carry computationally accessible information about immune cell state has not been explored. Here we show that machine learning applied to imaging flow cytometry images of the Golgi can distinguish lipopolysaccharide (LPS) treated from control cells. We trained models on Golgi images from human THP-1 monocytes before and 16 h after LPS stimulation in vitro, and from circulating myeloid cells of mice 24 h after intratracheal LPS or saline in vivo. In each dataset, the best-performing model was a deep learning model trained directly on images rather than a classifier using predefined morphometric features. Cross-domain performance varied with the model, transfer direction and use of labelled target data: several deep learning models trained on mouse images classified THP-1 cells above chance without changing model parameters. Grad-CAM highlighted regions within or adjacent to the GM130 fluorescence pattern. These findings indicate that GM130 images carry information associated with LPS treatment that supports classification within and across the two datasets. To our knowledge, this is the first application of supervised deep learning to imaging flow cytometry images of a subcellular organelle for inflammatory phenotyping, establishing organelle level image analysis as a potential complement to molecular assays for immune cell classification.
]]></description>
<dc:creator><![CDATA[ Zhang, X., Fang, M., Geng, B., Che, P. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.756023</dc:identifier>
<dc:title><![CDATA[Machine learning classifies subcellular Golgi morphology from imaging flow cytometry during inflammatory activation: an exploratory study]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.07.757297v1?rss=1">
<title>
<![CDATA[
Sketching the SRA: experiment-wide similarity search across 4.8 million metagenomes 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.07.757297v1?rss=1
</link>
<description><![CDATA[
Recent efforts have demonstrated that systematic analysis of the millions of metagenomes in the Sequence Read Archive (SRA) enables groundbreaking biological discoveries. However, SRA-scale comparison of whole metagenomes has remained elusive. Here, we introduce the most comprehensive resource to date for experiment-to-experiment analysis of all SRA metagenomes, comprising FracMinHash sketches (470GB), hypergen sketches (19GB), and an all-pairs Jaccard similarity matrix (90GB), which we release online at https://github.com/RolandFaure/SRA-metagenome-sketches-databases. These resources enable a wide range of analyses, which we demonstrate with several applications. First, we use the FracMinHash sketches to uncover temporal trends, finding that the amount of newly discovered microbial sequence continues to grow, rather than saturate or shrink. Second, we use the hypergen sketches, which enable nearest-neighbor search, to identify co-assembly candidates and thereby improve assembly quality. Third, we use the similarity matrix to analyze all SRA metagenomes, identifying duplicated submissions and over 50K candidates for metadata correction. Together, our resources unlock systematic analysis of metagenomic data at unprecedented scale.
]]></description>
<dc:creator><![CDATA[ Faure, R., Nakshi, A. H., Abrar, M. H., Chikhi, R., Lemane, T., Taheri, M., Won, S., Wu, H., Koslicki, D., Medvedev, P. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.07.757297</dc:identifier>
<dc:title><![CDATA[Sketching the SRA: experiment-wide similarity search across 4.8 million metagenomes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.756072v1?rss=1">
<title>
<![CDATA[
Co-occurrence network multicollinearity artefacts using centered log-ratio transformation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.756072v1?rss=1
</link>
<description><![CDATA[
Next-generation sequencing data is compositional in nature and ignoring this fact can significantly impact downstream data analyses. This is especially true in co-occurrence (correlation) networks because compositional data is prone to many false negative correlations. The centered log-ration (clr) transformation is a commonly used compositional data analysis (CoDA) approach that converts compositionally constrained data into real number space. The clr transformed values can subsequently be used in machine learning and statistical analyses such as Spearman or Pearson correlations used to generate co-occurrence networks. Here we show that the necessary zero substitution performed prior to clr transformation can create numerous multicollinearity artefacts. We also show that a newer CoDA approach called the robust centered log-ratio (rclr) eliminates these artefacts in correlation-based network analysis.
]]></description>
<dc:creator><![CDATA[ Krutkin, D. D., Kelley, S. T. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.756072</dc:identifier>
<dc:title><![CDATA[Co-occurrence network multicollinearity artefacts using centered log-ratio transformation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.756055v1?rss=1">
<title>
<![CDATA[
8-Oxoguanine transcriptional miscoding can transiently reproduce PAH-associated premature-stop transcripts 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.756055v1?rss=1
</link>
<description><![CDATA[
Pulmonary arterial hypertension (PAH) is associated with increased oxidative DNA damage, while haploinsufficiency of BMPR2 and other dosage-sensitive genes contributes directly to disease. Template-strand 8-oxoguanine (8-oxoG) provides a potential transient route to the same loss-of-function outcome through transcriptional mutagenesis without altering genomic DNA. RNA polymerase II can insert adenine opposite the lesion and generate a coding-sense C>A error in the nascent transcript. We asked whether this chemistry could generate premature-stop transcripts in dosage-sensitive PAH genes. Across 17,880 C>A-accessible coding positions in pulmonary vascular genes, 1,192 could generate premature termination codons, including 883 predicted to engage nonsense-mediated decay (NMD). Restricting these sites to expressed autosomal-dominant PAH genes with definitive ClinGen validity and established haploinsufficiency identified 116 NMD-prone positions in BMPR2, KDR, SMAD9, and TBX4. Eight exactly reproduce ClinVar pathogenic or likely pathogenic stop variants, including five in BMPR2. At BMPR2 c.1424, template-strand 8-oxoG and the expert-panel-reviewed genomic c.1424C>A allele is predicted to generate the same UAA-containing transcript. Published oxidized-guanine maps detect signal at seven of the eight coordinates, providing coordinate-level support for oxidative modification at these sites. These findings define a sequence-resolved route through which repairable oxidative DNA damage could transiently reproduce loss-of-function transcripts encoded by pathogenic PAH alleles without permanent genomic mutation.
]]></description>
<dc:creator><![CDATA[ Moreno-Caceres, S., Hayes, A. M., Delmas Eliason, A., Marciano, D. P. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.756055</dc:identifier>
<dc:title><![CDATA[8-Oxoguanine transcriptional miscoding can transiently reproduce PAH-associated premature-stop transcripts]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.756075v1?rss=1">
<title>
<![CDATA[
REFinder: Mining new enzymes from metagenomes 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.756075v1?rss=1
</link>
<description><![CDATA[
Microbial metagenomes encode vast catalytic diversity, but recovering enzymes of novel, yet-undescribed functionality typically requires whole-community assembly plus a means of identifying active proteins ab initio. The first step of the task is compute-intensive and misses low-abundance sequences. The second is complicated by our inability to predict novel functions. REBEAN, our DNA language model, sidesteps the latter by assigning each sequencing read a high-level Enzyme Commission (EC) class or a non-enzyme label without alignment. Here we build REFinder, a pipeline that addresses both steps by routing REBEAN-annotated reads to assemble only the putative enzymatic reads that have identified catalytic signatures. In our evaluation of 50 microbiome metagenomes, REFinder identified 1.2 to 2.1 fold more enzymes than could be recovered via homology-based annotation of the proteins from the corresponding full assemblies. Moreover, it was as much as 6.4-fold cheaper computationally than full assembly. Across all samples, REFinder identified at least three fourths and as many as 90% of the homology-accessible enzymes identified via full assembly of the complete metagenomes. Notably, a fraction of these, 22% to 45% per EC class, carried no similarity to Swiss-Prot proteins, i.e. a set of enzymes homology cannot annotate. For roughly two fifths of the over thirteen thousand such novel oxidoreductases from two saliva samples, ESMFold predicted structures aligned with TM-score[&ge;]0.7 to a characterized enzyme structure in the PDB - a substantial structural similarity without sequence homology. These results illustrate that targeted, alignment-free assembly turns even well-mapped microbiomes into a source of thousands of previously invisible but credible novel enzymes, raising our expectations for exploration of environmental microbiomes.
]]></description>
<dc:creator><![CDATA[ Prabakaran, R., Bromberg, Y. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.756075</dc:identifier>
<dc:title><![CDATA[REFinder: Mining new enzymes from metagenomes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.754999v1?rss=1">
<title>
<![CDATA[
KENT: Fast Pathogen Screening with NVIDIA Jetson Cluster in Resource-Constrained Areas 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.754999v1?rss=1
</link>
<description><![CDATA[
Metagenomic sequencing enables broad pathogen detection without requiring prior assumptions about the organisms present, but the memory and computational demands of classifying large sequencing datasets against reference databases often lead analysis pipelines to rely on desktop or server-grade computing platforms. This limits practical use in disasterstricken and other resource-constrained environments, where portable sequencing may be available but reliable access to cloud or laboratory infrastructure is not. We present KENT, a portable edge-computing framework for metagenomic pathogen screening based on a cluster of NVIDIA Jetson Nano modules. KENT adapts CuCLARK-l for low-memory embedded execution and extends it with a custom software stack for distributed read classification, abundance estimation, split-run abundance merging, and report generation. Using an MPI-based controller/responder architecture, KENT supports local, offline analysis with sample-level parallelism across multiple nodes.

We evaluate KENT using long read wastewater metagenomic samples with a custom pathogen database and the CAMI2 Marine long read dataset. In the wastewater setting, KENT achieves an average speedup of 3.1x over the CPU-based CLARK-l baseline while maintaining close agreement in classified read abundance estimates. On CAMI2 Marine, KENT achieves an average speedup of 2.68x and similarly preserves CLARK-l abundance profiles. Evaluation against the CAMI2 long read taxonomic ground truth yields a mean species-level precision of 58.55%, recall of 93.36%, F1 score of 71.74%, and L1 abundance distance of 0.731, closely matching CLARK-l. Among ground truth species represented in the reference database, KENT detects 99.86% on average. A controlled database-size experiment further shows little change in classification throughput across the tested database configurations, while large sequencing inputs remain constrained by the Jetson Nanos unified memory capacity. Overall, KENT demonstrates that GPU-accelerated metagenomic classification can be adapted to low-memory embedded hardware while preserving the biological profiling behavior of the CLARK-l baseline, supporting portable and offline pathogen screening in resource-constrained settings.

AvailabilityKENT is available at https://github.com/BilkentCompGen/KENT
]]></description>
<dc:creator><![CDATA[ Icoz, A., Roman-Brenes, R., Bingöl, Z., Alkan, C. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.754999</dc:identifier>
<dc:title><![CDATA[KENT: Fast Pathogen Screening with NVIDIA Jetson Cluster in Resource-Constrained Areas]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.755011v1?rss=1">
<title>
<![CDATA[
FilAdh: degradation-aware analysis of focal adhesions and actin stress fibers 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.755011v1?rss=1
</link>
<description><![CDATA[
Focal adhesions (FAs) and actin stress fibers (SFs) are mechanically coupled but often analyzed separately. We present FilAdh, a unified framework for FA instance segmentation, SF instance reconstruction, morphometry, and FA-anchorage-based SF classification. Across 57 held-out HeLa, SUIT-2, and MRC-5 cell line images, FilAdh achieved the highest FA Dice, intersection over union (IoU), and recall and the highest SF clDice and skeleton precision among benchmarked methods; paired t tests with Holm correction indicated significant gains over all compared methods for FA Dice, IoU, and recall, and over classical methods for SF centerline metrics, with no significant differences detected relative to the deep-learning comparators. Conditional on expert-curated FA and SF masks, SFs were assigned to dorsal, ventral, and peripheral classes with 81.5%-87.3% accuracy. Controlled Poisson-Gaussian noise, Gaussian defocus, and point-spread-function blur revealed distinct, structure-specific errors in overlap and object-level measurements. By linking segmentation and reconstruction to count, area, length, apparent width, and structural class, FilAdh offers an interpretable view of the FA-actin structural system and makes degradation-dependent measurement biases explicit.

BIGGER PICTURECells sense and respond to their physical environment through focal adhesions and actin stress fibers. The two form a mechanically coupled system, yet they are usually analyzed separately, with workflows re-tuned for each new set of images. FilAdh analyzes both within a single framework, tailoring segmentation, reconstruction, and classification to each structures geometry: compact adhesion patches versus thin, extended fiber networks. Testing on expert-annotated references and under controlled degradations that mimic low signal, focus errors, and optical blur shows that these common imaging imperfections distort adhesion and fiber measurements in different ways. The same design principle--matching the analysis to each structures shape and judging methods by how errors propagate into the final biological measurements--may benefit other bioimage-analysis problems. Released code, trained weights, and fixed data splits make all comparisons reproducible and extensible.

HIGHLIGHTSO_LIFilAdh jointly quantifies focal adhesions and actin stress fibers
C_LIO_LIFilAdh achieves the highest benchmarked FA overlap and comparable SF topology
C_LIO_LIMatched synthetic degradations reveal geometry-specific measurement errors
C_LIO_LIFA-SF spatial context enables dorsal, ventral, and peripheral SF classification
C_LI
]]></description>
<dc:creator><![CDATA[ Rey-Paniagua, G., Garcia-Lopez-de-Haro, C., Hernandez-Martinez, M., Lachowski, D., Munoz-Barrutia, A. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.755011</dc:identifier>
<dc:title><![CDATA[FilAdh: degradation-aware analysis of focal adhesions and actin stress fibers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755675v1?rss=1">
<title>
<![CDATA[
CryoCodex: learning discrete structural representations for cryo-EM map post-processing 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755675v1?rss=1
</link>
<description><![CDATA[
Cryo-EM maps frequently suffer from weak structural signals, spatially variable quality, and noise, complicating interpretation and downstream modeling. Existing post-processing approaches treat density enhancement, quality assessment, and masking as separate tasks, often amplifying residual noise or introducing local artifacts. Here, we present CryoCodex, a unified framework that jointly performs density enhancement, local quality estimation, and molecular masking. CryoCodex introduces a multi-head, multi-scale vector-quantization module that projects encoder-derived latent features onto a shared codebook of structural prototypes. By imposing discrete constraints across feature subspaces and spatial scales, the codebook suppresses stochastic noise while preserving recurrent structural patterns. On a non-redundant test set of 126 experimental maps spanning 3-8 [A], CryoCodex outperformed four representative methods, achieving a mean map-model FSC-0.5 of 3.93 [A] versus 4.62 [A] for EMReady2 and 6.87 [A] for the deposited maps. Downstream automated de novo model building with Phenix further confirmed the quality of the CryoCodex-enhanced maps. These gains extended to maps containing nucleic acids, where CryoCodex produced the best map-model agreement and improved model building. Half-map cross-validation indicated that these improvements are anchored to experimental data rather than driven by half-map-specific noise or structural hallucination. CryoCodex also delivered reliable local quality estimates and molecular masks within the same framework. Together, these results establish CryoCodex as a practical tool for cryo-EM map post-processing and structural determination in challenging cases.
]]></description>
<dc:creator><![CDATA[ Cheng, B., Su, B., Yang, J. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755675</dc:identifier>
<dc:title><![CDATA[CryoCodex: learning discrete structural representations for cryo-EM map post-processing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.755010v1?rss=1">
<title>
<![CDATA[
FlyAnatoView: Visualization of Drosophila Single-cell RNA-seq Datasets on Anatomographs 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.755010v1?rss=1
</link>
<description><![CDATA[
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for measuring the transcriptomic landscape at single-cell resolution. However, while scRNA-seq data reveals what genes each cell expresses, they do not indicate where that cell was located within the tissue or organ, anatomically. We developed FlyAnatoView (https://www.flyrnai.org/tools/rna_seq_base/web/svg_files/), a resource that projects scRNA-seq data onto anatomical diagrams, restoring this missing spatial context.
]]></description>
<dc:creator><![CDATA[ Hu, Y., Kalina, J., Veal, A., Ewen-Campen, B., Porter, T., Song, J., Luo, Y., Huang, K., Goutte-Gattat, D., Brown, N. H., Papatheodorou, I., Perrimon, N. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.755010</dc:identifier>
<dc:title><![CDATA[FlyAnatoView: Visualization of Drosophila Single-cell RNA-seq Datasets on Anatomographs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1?rss=1">
<title>
<![CDATA[
VINTER: a generative vision-language model for annotating and interrogating spatial tissues 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.752251v1?rss=1
</link>
<description><![CDATA[
Spatial transcriptomics links gene expression to tissue structure, yet current models learn embeddings whose meaning is assigned afterwards or read histology without measured expression. Tissue decisions are therefore rarely made from morphology, genes and neighbourhood together, or examined through them. Here we introduce VINTER, a generative vision-language model aligned on 1.45 million histology-caption pairs that reads histology, measured gene expression and spatial neighbourhood as one sequence. It returns the probability of every tissue class, and because genes enter as text, its molecular evidence can be edited with the image held fixed. Reading all three sources together, VINTER annotated tissue more accurately than spatial-domain methods and a spatial foundation model, and transferred across cohorts without target labels. Editing only the neighbourhood resolved the follicular B-cell and plasma-cell programmes behind its reading of tertiary lymphoid structure (TLS) maturation in lung and kidney cancer. Its class probabilities exposed what single labels conceal in triple-negative breast cancer: immune heterogeneity within stroma and cancer hidden beneath benign labels. Without retraining, the same model transferred to Visium HD and single-cell Xenium data and resolved follicular architecture within colorectal cancer TLS. VINTER thus turns spatial annotation into an interrogable reading of tissue across platforms and resolutions.
]]></description>
<dc:creator><![CDATA[ Wu, J., Xu, W., Zhuang, Y., Zhang, Y., Loza Lopez, M. d. J., yang, y., Nakai, K. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.752251</dc:identifier>
<dc:title><![CDATA[VINTER: a generative vision-language model for annotating and interrogating spatial tissues]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.755933v1?rss=1">
<title>
<![CDATA[
Synthesis-aware generative design in trillion-scale chemical spaces for automated drug discovery 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.755933v1?rss=1
</link>
<description><![CDATA[
Autonomous drug discovery via generative molecular design is critically bottlenecked by the production of chemically intractable structures. Multi-trillion-scale make-on-demand libraries guarantee synthetic feasibility, but conventional virtual screening cannot efficiently navigate these vast spaces or address multi-parameter optimization. We introduce Hyper Screening X, a generative AI framework integrating structure-based design with automated synthesis hardware. By encoding deterministic reaction logic in a generative flow network, the framework evaluates only 10 million candidates to navigate an 11-trillion-compound space, jointly optimizing physicochemical properties while ensuring compatibility with automated synthesis. Prospective validation against an SLC1A5 variant with a cryptic interface yielded a 96% synthesis success rate and a 50% functional hit rate, identifying two first-in-class lead compounds. This synthesis-aware strategy advances autonomous, closed-loop drug discovery.
]]></description>
<dc:creator><![CDATA[ Seo, S., Sung, Y., Hwang, S.-Y., Kang, M., Lee, J., Bordi, S., Raguz, L., Choi, J., Melnichenko, D., Namkung, W., Lee, S., Lim, J., Wanner, B. M., Han, J. M., Kim, W. Y. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.755933</dc:identifier>
<dc:title><![CDATA[Synthesis-aware generative design in trillion-scale chemical spaces for automated drug discovery]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.754115v1?rss=1">
<title>
<![CDATA[
NomadFlow: unindexed multi-motif scaffolding with independently mobile rigid groups 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.754115v1?rss=1
</link>
<description><![CDATA[
Motif scaffolding builds a protein around a prescribed structural motif -- an active site, a binding epitope, a metal site -- so that the motif is presented in its required geometry. The task is usually posed with the motif pinned down: its position along the sequence is given, and several rigid fragments inherit their mutual pose from a native structure. Many applications supply neither. We present NomadFlow, unindexed multi-motif scaffolding on all-atom SE(3) flow matching: the motif enters as a condition, a group projection preserves the internal geometry of each group exactly, and the groups move freely relative to one another until a set time, after which they act only as a condition. We assemble a preliminary benchmark for this setting, varying the granularity of the partition and the structural context around the motif, and obtain unique successes on 19 of 21 tasks. The trained model proposes a diversity of valid mutual poses, and on several tasks the native arrangement is among them. The shape of that solution space, however, varies strongly with the geometry of the condition: across tasks the successful layouts form a wide cloud, collapse onto the native pose, or contract to a connected low-dimensional family -- a continuum of poses rather than a set of isolated solutions. Multi-motif methods are therefore better compared by the space of layouts they propose and the successful part of it they retain than by yield or fold diversity alone. Code and weights: https://github.com/ipermyakov/nomadflow.
]]></description>
<dc:creator><![CDATA[ Ivan, P., Geraseva, E., Golovin, A. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.754115</dc:identifier>
<dc:title><![CDATA[NomadFlow: unindexed multi-motif scaffolding with independently mobile rigid groups]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755776v1?rss=1">
<title>
<![CDATA[
Additive-input encoding fails operator-level target-held-out prediction on current Perturb-seq screens 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755776v1?rss=1
</link>
<description><![CDATA[
Pooled Perturb-seq screens are often interpreted through a linear steady-state model in which each targeted gene supplies an additive perturbation input. Although this formulation identifies an operator on the observed response subspace in closed form, identifiability alone does not establish that inferred operators generalize to new perturbation targets. We evaluated fixed program-space additive inputs in three Perturb-seq screens -- Replogle K562 essential, Replogle RPE1 essential, and Jost 2020 -- using target-grouped nested cross-validation and matched signal-to-noise linear-truth controls. For inverse operator prediction, real held-out error remained at the predict-zero baseline (0.96, 1.00, and 1.00), whereas matched linear controls achieved 0.18, 0.53, and 0.72. This discrepancy persisted across random K562 target panels, an overdetermined Jost dimension sweep, direction-only fits, ridge and truncated-SVD estimators, and five linear ground-truth ensembles. Forward target-held-out prediction was more nuanced: fixed, footprint, and learned linear encodings modestly beat a predict-training-mean baseline in K562 but not in RPE1 or Jost, and the K562 gains recovered only about 7% of the gap to the matched linear control. On the tested screens, fitted fixed program-space operators should not be interpreted as validated target-generalizing regulatory operators. We release anchor-op, a reproducible toolkit for target-grouped evaluation, identifiability checks, and matched-geometry positive controls for perturbation-response models.
]]></description>
<dc:creator><![CDATA[ Fullmer, K., Kutzen, D., Terooatea, T. W. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755776</dc:identifier>
<dc:title><![CDATA[Additive-input encoding fails operator-level target-held-out prediction on current Perturb-seq screens]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.755314v1?rss=1">
<title>
<![CDATA[
Evaluating Autonomous LLM Agents Across Molecular Prediction and Optimization Benchmarks 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.755314v1?rss=1
</link>
<description><![CDATA[
Large language model (LLM) agents are increasingly capable of carrying out autonomous computational research, but it remains unclear whether they can develop molecular modeling methods that compete with strong human-developed approaches. Here, we evaluate autonomous method development across four settings: Therapeutics Data Commons (TDC) ADMET tasks, the OpenADMET ExpansionRx Challenge, the activity prediction track of the OpenADMET PXR Induction Challenge, and the Practical Molecular Optimization (PMO) benchmark. Agents followed the prescribed data splits, metrics, and evaluation protocols for each benchmark. On TDC, the best agent-developed score across configurations improved on the leaderboard reference on 18 of 21 tasks and on the combined leaderboard and peer-reviewed reference on 17, while individual Codex configurations improved on the combined reference on 13 tasks. On ExpansionRx, five-agent and single-agent Codex configurations achieved overall scores corresponding to fourth and sixth place relative to the published final leaderboard, with the five-agent configuration reaching top-five performance on seven of nine endpoints. On PXR, five-agent Codex achieved a mean absolute error slightly outperforming the best published challenge entry. On PMO, five-agent Codex developed a molecular optimizer that outperformed existing methods evaluated under the benchmark's standard setup; other methods obtained higher reported scores under modified conditions, including additional molecular pretraining data or a different oracle-call budget. Together, these results show that current LLM agents can autonomously develop high-performing molecular prediction and optimization methods across substantially different drug-discovery settings, in several cases matching or exceeding leading human-developed approaches.
]]></description>
<dc:creator><![CDATA[ Smbatyan, K., Ghukasyan, T., Petrosyan, G. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.755314</dc:identifier>
<dc:title><![CDATA[Evaluating Autonomous LLM Agents Across Molecular Prediction and Optimization Benchmarks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.755916v1?rss=1">
<title>
<![CDATA[
Evaluation of synthetic data generation for longitudinal 6-year follow-up subclinical cardiovascular disease data using GAN models 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.755916v1?rss=1
</link>
<description><![CDATA[
Although largely preventable, cardiovascular disease (CVD) is the leading cause of death worldwide. Synthetic data generation (SDG) of health data can facilitate data sharing for research and development of personalised cardiovascular disease prevention tools. Current state-of-the-art SDG methods are limited to cross-sectional data or focused on short-term or in-patient EHR applications lacking benchmarks for generalisability. Since prevention is a long-term longitudinal objective, these limitations must be addressed. We evaluated two tabular generative adversarial networks, TGAN and CTGAN, for privacy and fidelity on a structured 6-year longitudinal trial dataset within CVD prevention. We compared performance with a rudimentary perturbation method. In addition, we performed a variable-wise evaluation and an age-stratified analysis of the higher performing model to assess preservation of clinically relevant characteristics. Overall, CTGAN showed better fidelity than TGAN. The models performed similarly within privacy, showing better results than the perturbation method. CTGAN had lower performance in comparison to the perturbation method within numerical fidelity of continuous variables and overall temporal fidelity. Furthermore, CTGAN's performance was lower for younger age groups than older. CTGAN shows better than or equal performance within fidelity and privacy than TGAN and the perturbation method on a long-term longitudinal dataset within CVD prevention. To the best of our knowledge, this is the first study evaluating CTGAN on a longitudinal dataset and our findings suggest that handling of temporal characteristics and imbalanced subgroups need further refinement.
]]></description>
<dc:creator><![CDATA[ Bertgren, A., Ohberg, F., Soda, P., Naslund, U., Wennberg, P., Gronlund, C. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.755916</dc:identifier>
<dc:title><![CDATA[Evaluation of synthetic data generation for longitudinal 6-year follow-up subclinical cardiovascular disease data using GAN models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.752299v1?rss=1">
<title>
<![CDATA[
RiboWin: A computational framework to identify non-canonical open reading frames 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.752299v1?rss=1
</link>
<description><![CDATA[
Abstract Ribosome profiling is widely used to map translation across the genome, yet current sORF discovery pipelines are constrained by their reliance on annotated protein coding regions and by treating biological replicates primarily as tools for quantification rather than as strict filters for reproducibility. This results in two major issues, the systematic exclusion of pseudogene derived loci from analysis and the generation of extremely large candidate sets in which true translation events are difficult to distinguish from noise. To overcome these limitations, a five-stage framework was developed that expands the search space to include pseudogene loci while enforcing dual biological replicate concordance as a hard binary filter. When applied to four human cell lines with matched replicates and benchmarked against existing tools, the framework produces dramatically smaller and more reliable candidate sets, reducing thousands to millions of predicted sORFs down to a small number of high confidence concordant loci. Across HeLa cell cycle conditions, a limited set of unique concordant sORFs was identified spanning multiple transcript classes including upstream ORFs, retained introns, and nonsense mediated decay transcripts, along with a pseudogene derived candidate. Notably, the pseudogene encoded sORF RPL26P19 was detected consistently in HeLa cells but missed or inconsistently identified by existing tools, while a separate pseudogene sORF from UBE3AP2 was observed specifically in normal primary cell types and absent in HeLa. Both candidates were later found to be supported by entries in OpenProt, even though this was not known during the analysis, which gives added confidence that the results are genuine and not biased by prior expectations. Overall, these findings suggest that pseudogenes are not just inactive remnants in the genome but can act as real, functional sources of translated sORFs in a cell type specific manner. At the same time, it becomes clear that most current tools are not designed to detect these signals effectively. This highlights the need for approaches that both broaden the regions being searched and apply stricter reproducibility checks, so that truly meaningful translation events can be identified more reliably.
]]></description>
<dc:creator><![CDATA[ D, V., N, A., Yadav, B. K., Phadnis, S., Verma, B., YS, R. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.752299</dc:identifier>
<dc:title><![CDATA[RiboWin: A computational framework to identify non-canonical open reading frames]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.755973v1?rss=1">
<title>
<![CDATA[
Robust ORF Deconvolution from Multiple Ribo-seq Datasets with Group LASSO Penalty 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.755973v1?rss=1
</link>
<description><![CDATA[
Ribo-seq has provided compelling evidence that there are many translated but unannotated ORFs. Despite a growing body of peptidomic support for cryptic ORFs, the computational discovery of weakly translated, short and mutually overlapping ORFs has remained a major bottleneck. Here we introduce PRICE 2, a method designed to resolve these challenging translation events by adapting concepts from isoform deconvolution. PRICE 2 employs a generative model combined with group LASSO regularized regression to jointly infer active ORFs across multiple sequencing runs. In a comprehensive benchmark, PRICE 2 outperformed thirteen state-of-the-art tools in detecting short and overlapping ORFs. Orthogonal validation via immunopeptidomics demonstrated that ORFs identified by PRICE 2 enable the discovery of [~]40% more cryptic MHC-I peptides than the best-performing competing tool at a fixed false discovery rate. By analyzing public ribo-seq data across 45 distinct cell and tissue types, we constructed ORF-stock, a comprehensive database of non-canonical ORFs for immunopeptidomic studies.
]]></description>
<dc:creator><![CDATA[ Maidhof, K., Schlosser, A., Erhard, F. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.755973</dc:identifier>
<dc:title><![CDATA[Robust ORF Deconvolution from Multiple Ribo-seq Datasets with Group LASSO Penalty]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.06.757201v1?rss=1">
<title>
<![CDATA[
The impact of sex and ancestry on the molecular landscape of pancreatic adenocarcinoma 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.06.757201v1?rss=1
</link>
<description><![CDATA[
Background: Pancreatic ductal adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-related mortality, with an increasing incidence and dismal 10% survival at 5 years despite best available care. PDAC is characterized by low tumour cellularity, recurrent driver mutation alterations, and substantial inter and intra-tumoural heterogeneity. The influence of individual genetic features such as sex and ancestry on tumour heterogeneity remains poorly understood. Methods: We assembled a cohort of PDAC across five datasets for a total of 7,527 tumours. Measures of genomic instability were assessed across sex and ancestry on a global level (SNV density and percent genome altered by copy number events), and at a gene level for recurrent events. KRAS variant frequencies, ploidy and mRNA abundance were also evaluated. Ancestry was assigned using ADMIXTURE from germline genotypes where available, and patient reported otherwise. Results: We observed ancestry-specific mutation patterns in global point mutation density, with higher SNV burden in African PDAC tumours and lower SNV burden in Asian PDAC tumours as compared to European tumours. We also identified an ancestry bias in the driver gene, GNAS, in which driver SNVs in GNAS was detected more frequently in those of Asian ancestry, and less frequently in those of African ancestry. No sex- or ancestry-associations were found for KRAS, but individual KRAS variants showed a trend towards ancestry-related variation. Conclusions: These findings demonstrate that ancestry contributes to the genomic heterogeneity of PDAC, shaping both global and gene-specific mutation patterns. This work underscores the importance of including diverse ancestries in PDAC molecular profiling efforts which may inform the development of tailored molecular diagnostics and targeted therapies.
]]></description>
<dc:creator><![CDATA[ Chen, C. Y., Arbet, J. Y., Zhu, C., Oh, J., Boutros, P. C. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.06.757201</dc:identifier>
<dc:title><![CDATA[The impact of sex and ancestry on the molecular landscape of pancreatic adenocarcinoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.28.755023v1?rss=1">
<title>
<![CDATA[
A strategy for ionic current analysis of nanopore protein readouts 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.28.755023v1?rss=1
</link>
<description><![CDATA[
BackgroundNanopore-based protein reading holds promise for single-molecule proteomics, but analyzing the resulting ionic current signals presents substantial computational challenges. Unlike nucleic acids, peptide-associated ionic signals exhibit complex patterns with subtle transitions between amino acids that can be obscured by noise. We developed an integrated computational strategy for processing nanopore protein ionic current signals from synthetic protein constructs containing all 20 amino acids in controlled sequence contexts. We started with denoising to remove artifacts, followed by change point detection using variable-number and fixed-number segmentation methods.

ResultsWe employed dynamic time warping barycenter averaging to generate amino acid-specific reference templates. In parallel, we performed classification of amino acid regions using bidirectional LSTM networks trained on segment-level statistical features. Accuracy for 20-way amino acid classification remained limited, while charge-based binary classification reached 93.3% accuracy. Pairwise classification revealed that certain amino acids, particularly negatively charged residues, generate distinctive signatures. We used a profile hidden Markov model to capture sequential structure of protein translocation.

ConclusionThis approach provides computational strategies for automated segmentation, classification, and modeling of nanopore peptide translocation ionic current data.
]]></description>
<dc:creator><![CDATA[ Stein, A. J., Ghohabi Esfahani, N., Akeson, S., Kakhaki, P. D., Kontogiorgos-Heintz, D., Nivala, J., Jain, M. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.28.755023</dc:identifier>
<dc:title><![CDATA[A strategy for ionic current analysis of nanopore protein readouts]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1?rss=1">
<title>
<![CDATA[
HapSolo2: GPU-accelerated optimization for removing secondary haplotigs from diploid genome assemblies 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1?rss=1
</link>
<description><![CDATA[
BackgroundSecondary haplotigs retained in a primary assembly inflate genome size and duplicate gene content, and their removal remains a necessary curation step for heterozygous diploid genomes. HapSolo (Solares et al. 2021) treats this step as an optimization problem: it searches three pairwise alignment thresholds (percent identity, query coverage, and the ratio of aligned query to aligned reference length) to minimize a cost defined over single-copy, duplicated, and missing conserved orthologs. The original implementation depended on a separate BUSCO v3 run for every contig, on Python 2.7 and pandas in its inner loop, on a single randomized forward-walk optimizer, and on OrthoDB v9 lineage datasets that are no longer distributed.

ResultsHere we report HapSolo2, a reimplementation that replaces the per-contig BUSCO step with miniprot-based ortholog classification against OrthoDB v9, v10, or v12.2 lineage datasets, adds steepest descent and simulated annealing to the original random forward walk, and evaluates all walkers in batched CuPy array operations on a single GPU. On an avocado assembly of 5,122 contigs scored against 1,440 orthologs, 100,000 iterations of random walk and steepest descent ran 5.6- and 7.6-fold faster on one NVIDIA RTX A6000 than on 10 CPU processes. Across 45 runs on four diploid assemblies (Anopheles funestus, Persea americana, Vitis vinifera, and Amblyraja radi-ata; 212 Mb to 3.2 Gb) and three OrthoDB releases, the three optimizers reached best costs within 1.8% of one another. Independent assessment with BUSCO v6.1 showed that HapSolo2 reduced duplicated orthologs from 52.0% to 5.6% in P. americana, from 17.9% to 10.4% in V. vinifera, and from 7.8% to 2.8% in A. funestus, with completeness decreasing by at most 1.3 percentage points. On the avocado assembly, HapSolo2 reduced the cost reached by the original HapSolo from 0.2200 to 0.1036 and raised contig N50 from 3,366 kb to 4,608 kb.

ConclusionsHapSolo2 makes ortholog-guided haplotig removal practical in minutes on a single workstation, independent of a BUSCO installation, and reproducible across OrthoDB releases. It is distributed as an installable Python package with a unified command-line pipeline, an assembly statistics and plotting tool, an Apptainer container, and archived copies of the legacy ortholog datasets.
]]></description>
<dc:creator><![CDATA[ Yin, J., Agrawal, M., Goel, P., Solares, E. A. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.02.756147</dc:identifier>
<dc:title><![CDATA[HapSolo2: GPU-accelerated optimization for removing secondary haplotigs from diploid genome assemblies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755680v1?rss=1">
<title>
<![CDATA[
Sex-dependent DNA methylation in amyotrophic lateral sclerosis 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755680v1?rss=1
</link>
<description><![CDATA[
Amyotrophic lateral sclerosis (ALS) shows well-established sex differences in incidence, age at onset and progression, but their biological basis is unclear. We hypothesised that the DNA methylation changes associated with ALS differ between the sexes, which would point to sex-specific disease processes. To test this, we performed a blood-based epigenome-wide association meta-analysis of ALS cases and controls from Project MinE, an international ALS genomics consortium, and an independent Australian cohort, comprising 8,946 individuals after quality control (5,129 males and 3,817 females; 8,942 with complete covariates in the primary models). We analysed 781,648 autosomal CpGs using complementary sex-stratified case-control models and an ALS-by-sex interaction model that directly tested whether ALS-associated methylation effects differed by sex. Sex-stratified analyses identified no genome-wide significant differences between male and female ALS effects. In contrast, interaction testing identified seven robust sex-dependent ALS-associated differentially methylated positions (P < 9 x 10-8), mapping to KCNQ1, ADPRH, HLA-F, GHRLOS, IRF5, RAD51B and one intergenic region. The strongest signal was at KCNQ1 (P = 3.1 x 10-9). Six of the seven remained genome-wide significant in a four-stratum sensitivity analysis, and none was detected in previous conventional ALS or population sex-associated methylation analyses. Together, these findings suggest a distinct sex-conditional component of the ALS blood methylome, implicating immune, metabolic and ion-channel loci and providing new candidates for investigating biological heterogeneity between males and females with ALS.
]]></description>
<dc:creator><![CDATA[ Kim, Y. J., Iacoangeli, A., Zwamborn, R. A. J., van Rheenen, W., Byrne, R., Van Eijk, K. R., Marzi, S., Watson, D., Vourc'h, P., Corcia, P., de Carvalho, M., Gotkine, M., Povedano, M., Mora Pardina, J. S., Garton, F., McRae, A., Shaw, P. J., Landers, J. E., Glass, J. D., Shaw, C. E., Basak, N. A., Hardiman, O., Van Damme, P., McLaughlin, R. L., van den Berg, L. H., Project MinE ALS Sequencing Consortium,, Mill, J., Al-Chalabi, A., Al Khleifat, A. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755680</dc:identifier>
<dc:title><![CDATA[Sex-dependent DNA methylation in amyotrophic lateral sclerosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.05.753292v1?rss=1">
<title>
<![CDATA[
Heterochromatin protein 1α acts as an essential stabilizer of chromatin packing domains 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.05.753292v1?rss=1
</link>
<description><![CDATA[
Background: The spatial organization of the human genome emerges from an enzyme-guided self-assembly process in which chromatin has a bidirectional relationship with transcription. Central to this architecture are chromatin packing domains, 50-200 nm structures exhibiting mass-scaling behavior. Nascent domains arise through transcription and cohesin/CTCF-mediated loop extrusion, while their maturation depends on conditions that permit differential access of epigenetic enzymes to their dense cores and continuously lower density periphery, resulting in the accumulation of heterochromatic marks in domain cores and euchromatic marks outside of the domain cores. In turn, packing domains support transcription by creating an "ideal zone" peripherally to their cores due to a nonmonotonic dependence of transcription rate on local chromatin density. Results: We investigated the role of heterochromatin protein 1 alpha (HP1), a key chromatin crosslinking factor with affinity to H3K9me3 marks, in maintaining packing domain organization. Paradoxically, short-term HP1 degradation resulted in overall downregulation of genes, contrasting with the canonical view that repression of heterochromatin allows chromatin to become more accessible for transcription. While some packing domains persisted, heterochromatic cores of dense packing domains were disrupted. When exposed to hypoxia, HP1-depleted cells exhibited a dampened transcriptional response, showcasing that when domain maturation is disrupted, stress response pathways are not effectively activated. Conclusions: This work highlights HP1 crosslinking as an essential component of domain maturation, as HP1 loss disrupts domain geometry and inhibits efficient transcriptional reprogramming. Our findings suggest an intriguing perspective on the potential relationship between chromatin architecture and gene regulation that could inform chromatin engineering-based treatments.
]]></description>
<dc:creator><![CDATA[ Kuo, T., Carrillo Gonzalez, P., Acosta, N., Carter, L. M., Pujadas Liwag, E., Arriaga, C. V., Johnson, B., Dunton, C. L., Almassalha, L. M., Szleifer, I., Backman, V. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.05.753292</dc:identifier>
<dc:title><![CDATA[Heterochromatin protein 1α acts as an essential stabilizer of chromatin packing domains]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.06.757170v1?rss=1">
<title>
<![CDATA[
A gene-centric analysis of copy number variation reveals tumour suppressor modulation of cancer risk and novel associations with common diseases 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.06.757170v1?rss=1
</link>
<description><![CDATA[
Copy number variants (CNVs) are a significant source of genomic variation in humans. They can span large genomic regions that encompass whole genes and their flanking regions, having the potential to alter the number of functional copies in an individual. Typically, CNVs are analysed individually or combined into regions without regard to the genes they span. Here, we employ a gene-centric approach to link changes in whole-gene copy number to phenotype. We first examined the relationship between CNVs spanning whole genes and gene expression using data from GTEx and found that predicted effects on gene expression are often realised. Applying this method, we identified 34 gene deletions associated with cancer incidence, including several novel candidate associations. Most notably, we discovered that gene deletions in the 13q14 region are linked to increased cancer risk, a novel extension of a known somatic association to the germline, which we did not find using region-based methods. Statistical associations were found between whole-gene loss of tumour suppressor genes and overall cancer risk, as hypothesized. It was additionally found that the total number of whole-gene losses was strongly associated with increased cancer risk, even when known cancer-related genes are not included in the count. Finally, we extended these methods to non-cancer phenotypes, uncovering significant associations with common diseases, including asthma and heart attack.
]]></description>
<dc:creator><![CDATA[ Matthews, S., Seoighe, C., Wheeler, G. L. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.06.757170</dc:identifier>
<dc:title><![CDATA[A gene-centric analysis of copy number variation reveals tumour suppressor modulation of cancer risk and novel associations with common diseases]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.755883v1?rss=1">
<title>
<![CDATA[
Quantifying performance inflation from hidden sample dependence in biomedical image classification benchmarks 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.755883v1?rss=1
</link>
<description><![CDATA[
Machine learning benchmarks often treat image files as independent observations, although multiple files may represent repeated views or other related samples. Prior work has shown that non-independent partitioning can inflate model performance, but the sensitivity of a biomedical benchmark to progressively increasing train-test relatedness is less often quantified. Here, we quantified how apparent classification performance changes when recoverable sample groups are progressively allowed to cross training, validation, and test partitions. Using source structure and filenames, we reconstructed analysis groups in a microscopy corpus compiled from multiple sources and generated five class stratified partitioning conditions, ranging from an intact group reference to random assignment of individual files. We applied the same training recipe within each model family to 129,173 images spanning 14 classes of blood cells and hematopoietic cell states. For a pretrained Vision Transformer, random assignment of individual files increased the mean test macro F1 from 0.853 to 0.977 and accuracy from 0.851 to 0.990 relative to the intact group reference. Apparent performance increased progressively across the intermediate conditions. The endpoint pattern also appeared with a ResNet-50 backbone, and the largest apparent gains occurred in immature granulocytic and blast classes. A descriptive analysis within the random assignment condition further showed higher scores among test images that had another member of the same group in the training set, although the exposed and unexposed subsets were not exchangeable. The grouping workflow, exact split definitions, code, and compact result files are released as HSD-Bench to support reproducible evaluation of hidden sample dependence.
]]></description>
<dc:creator><![CDATA[ Vasinkova, M., Jochymek, L., Gajdos, P. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.755883</dc:identifier>
<dc:title><![CDATA[Quantifying performance inflation from hidden sample dependence in biomedical image classification benchmarks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.755911v1?rss=1">
<title>
<![CDATA[
D-plus: Beyond the D-statistic -- Accurate Detection and Characterization of Introgression Despite Among-Lineage Rate Variation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.755911v1?rss=1
</link>
<description><![CDATA[
Ubiquitous in phylogenomic and population-genomic introgression work, the D-statistic has fallen victim to its own success. Its reliability is compromised by two major confounding factors: among-lineage substitution rate heterogeneity, which generates spurious signals of introgression, and unsampled "ghost" lineages, which mislead the characterization of introgression by erroneously attributing ghost ancestry to extant species. Here, we developed D-plus, a maximum-likelihood tool that explicitly accounts for both confounding factors to unpack the sources of significant D-statistic signals. Simulations demonstrate that D-plus effectively mitigates false introgression calls under among-lineage rate variation. Meanwhile, it retains high sensitivity for genuine introgression signals and correctly assigns detected signals to introgression involving ghost lineages, or solely between extant non-sister lineages, or a combination of the two. Our D-plus re-analysis of phylogenomic data from Jaltomata (Solanaceae) overturns prior interpretations of significant D-statistic signals, which were conventionally regarded as evidence for recurrent non-sister introgression. Instead, our results favor alternative explanations invoking artefacts driven by lineage-specific rate heterogeneity as well as two ghost introgression events. Collectively, D-plus addresses major methodological limitations inherent to traditional D-statistic analyses and enables accurate detection and characterization of introgression events.
]]></description>
<dc:creator><![CDATA[ Pang, X.-X., Zhang, D.-Y. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.755911</dc:identifier>
<dc:title><![CDATA[D-plus: Beyond the D-statistic -- Accurate Detection and Characterization of Introgression Despite Among-Lineage Rate Variation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.10.01.755876v1?rss=1">
<title>
<![CDATA[
scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.10.01.755876v1?rss=1
</link>
<description><![CDATA[
Single-cell RNA sequencing (scRNA-seq) studies often profile only one or a few biological samples, limiting assessment of discovery stability under between-sample variation. We present scStable, a framework for assessing scRNA-seq analysis stability by generating synthetic samples that preserve cell-level heterogeneity from a single-sample reference while injecting bulk-derived between-sample variation. In gastric cancer data comprising 21 matched scRNA-seq and bulk RNA-seq profiles, scStable better recapitulates observed between-sample variation than ZINB-WaVE, subsampling, and Gaussian noise injection. Using synthetic sam-ples, scStable enables stability-aware analysis, including prioritization of stable differentially expressed genes and selection of clustering methods and hyperparameters.
]]></description>
<dc:creator><![CDATA[ Jiang, C., Li, J. J. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.10.01.755876</dc:identifier>
<dc:title><![CDATA[scStable: assessing scRNA-seq analysis stability under bulk-derived between-sample variation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755802v1?rss=1">
<title>
<![CDATA[
msengine: Adaptive Open-Source Signal Extraction for HDX MS 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755802v1?rss=1
</link>
<description><![CDATA[
Hydrogen-deuterium exchange mass spectrometry (HDX-MS) requires extraction of broad, time-dependent peptide isotope envelopes from noise and overlapping signals. The ExMS/ExMS2 lineage established a retention-time-aware framework for raw-data extraction and integrated HDX-MS analysis. Building on this foundation, msengine is an open-source Python engine that adapts mass-selection windows and retention-time boundaries to the observed data. Spectra are reconstructed as nonnegative combinations of the theoretical undeuterated (all-H) isotope distribution shifted across allowed deuteration states, without prescribing a population shape. The fitted weights, termed deuteration coefficients, describe each state's contribution and guide refinement of mass-selection windows. Spectral matches establish an initial retention interval, which is then expanded using chromatographic intensity to include weaker portions of the elution profile. Interference screening and final quality checks assess the recovered interval. In a 1:1 mixture of undeuterated and deuterated material, interval expansion changed the relative population intensities within the extracted envelope resulting in closer agreement to the 1:1 target, illustrating the importance of boundary selection. Across a four-timepoint experiment with the SET1/SWD2 complex collected in duplicate, msengine accepted an average of 901 peptide-charge targets per measurement versus 636 for ExMS2, a 41.7% increase, while improving replicate-centroid disagreement and retention-window widths between replicates. In a same-workstation benchmark, total wall time decreased from 71.9 to 7.3 min, with higher peak memory use. Together, these results demonstrate adaptive signal extraction across mass and retention time as a means to retain multimodal envelopes while increasing accepted-target yield, persistence, accuracy, and throughput.
]]></description>
<dc:creator><![CDATA[ Patapoff, A., Patapoff, N., Walters, B. T. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755802</dc:identifier>
<dc:title><![CDATA[msengine: Adaptive Open-Source Signal Extraction for HDX MS]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755742v1?rss=1">
<title>
<![CDATA[
HyperCom: a hypergraph-based method to infer cell-resolved cell-cell communication 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755742v1?rss=1
</link>
<description><![CDATA[
Cell-cell communication underpins intercellular responses allowing cells to dynamically change states across conditions; however current methods typically aggregate signals by cell type annotations which may introduce biases. We developed HyperCom, a hypergraph-based method to infer cell-resolved cell-cell communication from single cell transcriptomics data. HyperCom constructs a hypergraph from known ligand-receptor interactions and the expression of those ligands and receptors in individual cells and performs graph diffusion on the hypergraph to score cells on a continuous spectrum from sender to receiver for each LR pair. HyperCom then generates a cell-type agnostic ranking of LR pairs by communication either over the whole dataset or in a condition-aware manner. Validation of HyperCom on physical cell-to-cell contact, spatial, and multicondition datasets highlight its flexibility to capture subtle per cell communication information. HyperCom is available as a species-agnostic, scalable, and open source R package: https://github.com/pawelab/HyperCom.
]]></description>
<dc:creator><![CDATA[ Moy, J. K., Przytycki, P. F. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755742</dc:identifier>
<dc:title><![CDATA[HyperCom: a hypergraph-based method to infer cell-resolved cell-cell communication]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755805v1?rss=1">
<title>
<![CDATA[
Transfer Matrix and Tensor Approaches to Nucleic Acid Secondary Structure Thermodynamics 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755805v1?rss=1
</link>
<description><![CDATA[
Nucleic acid secondary structure thermodynamic models have great utility for the analysis and design of nucleic acid structures, devices, and systems, yet their complexity and piecemeal development over decades pose challenges to further innovation. Employing different functional forms for each loop type within a structure, existing nearest neighbor (NN) models necessitate numerous loop-specific dynamic programming recursions in order to calculate physical quantities of interest. Here, we derive and implement a new class of models based on transfer matrices, yielding a single functional form for the free energy of all loop types (as the trace of a matrix product) and a single dynamic programming recursion for calculating physical quantities of interest. This new class of models has additional attractive properties: 1) incorporating a subensemble of states describing proximal interactions within each loop, 2) approximating logarithmic entropic scaling for all closed loops, 3) offering the opportunity to model correlations between the subensembles of neighboring loops by generalizing transfer matrices to tensors, and 4) providing a basis for systematically adjusting the number of model parameters to enhance accuracy as new empirical data become available for parameter regression. We use literature experimental data to regress parameters for representative new transfer matrix models, observing accuracy as good as or better than that of existing NN models, yet using dramatically fewer parameters (e.g., dozens vs hundreds or thousands). For complex ensembles containing an arbitrary number of interacting nucleic acid strands totaling N nucleotides, the new single recursion leads to exact dynamic programs for calculating diverse physical quantities (including the partition function, equilibrium base-pairing probabilities, minimum free energy and suboptimal proxy structures, Boltzmann-sampled secondary structures, and gradient of the partition function) with O(N3) time complexity. The simplicity of the new partition function dynamic program enables overflow-safe partition function calculations using renormalization to achieve scalability for large complexes (e.g., 50,000 nt) as well as formulation using a global matrix multiplication primitive that facilitates GPU acceleration, yielding speedups of an order of magnitude relative to NN partition function algorithms for large complexes. The new class of transfer matrix and tensor models provides an elegant framework for future efforts to advance the accuracy, speed, and scope of computational nucleic acid analysis and design.
]]></description>
<dc:creator><![CDATA[ Fornace, M. E., Pierce, N. A. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755805</dc:identifier>
<dc:title><![CDATA[Transfer Matrix and Tensor Approaches to Nucleic Acid Secondary Structure Thermodynamics]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.09.30.755547v1?rss=1">
<title>
<![CDATA[
PaleoRigor: a bounded research agent with a reproducible evidence trail for paleomicrobiome data processing 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.09.30.755547v1?rss=1
</link>
<description><![CDATA[
Background: Ancient microbial evidence informs research on past health, diet, and environments, but its scarcity and susceptibility to contamination demand care in selecting and processing data. Language models can now turn a research request into an analysis plan, but an unconstrained planner may reference files that do not exist, apply operations a format forbids, or answer a question the data cannot settle. A planner used on such evidence therefore needs to be bounded, and its decisions recoverable afterwards. Results: We developed PaleoRigor around three commitments. Agent: a language model plans the analysis. Constraint: a validator checks every file reference, format and step dependency before anything runs, execution is restricted to predefined local modules, and a planning contract directs the model to refuse out-of-scope requests with a named reason code rather than answer them. Reproducibility: each run is exported with its plan, intermediate files, checksums and decisions. In a preregistered, frozen held-out evaluation, PaleoRigor passed 23 of 24 runs (95.8%; Wilson 95% confidence interval, 79.8-99.3%): 11 of 12 supported runs and all 12 prespecified boundary decisions. An ablation that removed this planning contract from the same model passed 19 of 24 (79.2%). Repeating the frozen design with a second model configuration yielded 24 of 24 successes for PaleoRigor and 19 of 24 for the ablated arm. Pooled post hoc across both configurations, PaleoRigor passed 47 of 48 runs (97.9%), compared with 38 of 48 (79.2%) for the ablated arms. All 96 run labels were recomputed from retained records and matched. Dataset-level tests matched read counts and compressed-file sizes for six public sequencing records and documented the removal of 114 duplicate sequence entries. A retraction-associated audit illustrated how expert review limited conclusions to file-level evidence. Conclusions: For paleomicrobiome data, pairing a language-model agent with explicit constraints and a reproducible record makes the agent's proposed analysis reviewable before its outputs support a biological claim. PaleoRigor delivers checked inputs, a recorded transformation history and a named reason for each request it refuses, and runs as a local desktop application. It prepares data for specialist analysis; ancient-DNA authentication and biological interpretation remain with the paleobiologist.
]]></description>
<dc:creator><![CDATA[ Liu, T., Zhang, H., Jia, Y., Ning, K. ]]></dc:creator>
<dc:date>2026-10-08</dc:date>
<dc:identifier>doi:10.64898/2026.09.30.755547</dc:identifier>
<dc:title><![CDATA[PaleoRigor: a bounded research agent with a reproducible evidence trail for paleomicrobiome data processing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-10-08</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
