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<title>bioRxiv Subject Collection: Genomics Bioinformatics</title>
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This feed contains articles for bioRxiv Subject Collection "Genomics Bioinformatics"
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<link>https://www.biorxiv.org</link>
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<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.03.736294v1?rss=1">
<title>
<![CDATA[
Immunoinformatics-Guided Design and In Silico Evaluation of a Multi-Epitope Vaccine Against Influenza A H10N5 and H3N2 Strains Based on Hemagglutinin and Neuraminidase Proteins 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.03.736294v1?rss=1
</link>
<description><![CDATA[
Influenza A viruses H3N2 and H10N5 represent, respectively, a persistently dominant seasonal pathogen and a newly documented zoonotic threat with the latter strain variants responsible for the first confirmed human fatality in January 2024, yet no vaccine platform currently addresses co-protection against both subtypes within a unified immunogen. We report here the immunoinformatics based vaccine design and multi-layered computational validation of a 419-amino-acid multi-epitope subunit vaccine construct targeting conserved hemagglutinin (HA) and neuraminidase (NA) antigens identified through multiple sequence alignment of the avian H10N5 (A/swine/Hubei/10/2008) and H3N2 human reference strain sequences to identify viral agents undergoing mammalian adaptations. Linear B-cell, cytotoxic T lymphocyte (CTL), and helper T lymphocyte (HTL) epitopes were predicted using ABCpred, BCEpred, BepiPred 2.0, NetMHCpan 2.1, and NetMHCpan 4.0, then filtered through VaxiJen 3.0, AllerTOP v2.1, and ToxinPred to retain only antigenic, non-allergenic, non-toxic candidates. The final construct, incorporating an avian {beta}-defensin N-terminal adjuvant with GPGPG, AAY, and EAAAK linkers, exhibited a molecular weight of 43.9 kDa, instability index of 31.15, and SOLPro solubility probability of 0.763. Tertiary structure modeling via I-TASSER and GalaxyRefine achieved 84.4% Ramachandran-favored residues. Molecular docking against TLR3 and TLR7 yielded binding free energies of -16.1 and -16.8 kcal/mol with picomolar dissociation constants. Molecular dynamics simulations confirmed complex stability over extended trajectories. Furthermore, codon optimization produced a Codon Adaptation Index of 1.0 for E. coli K12 expression. In silico immune simulation demonstrated robust activation of humoral and cellular immunity including elevated IgG1, IgM, IFN-{gamma}, IL-2, rapid NK cell expansion, and broad B-cell clonal diversity. These findings establish a computationally validated candidate capable of providing protection against influenza in multiple host organisms, warranting experimental advancement.
]]></description>
<dc:creator><![CDATA[ Shabbir, M. Z., Kumar, P., Rehman, M. A. U., Kumar, J., Urooj, U., Batool, S. I., Sourav, C., Ghazanfar, R., Nagari, Z., Hameed, D., Wahid, A., Atique, A., Siddique, M. D. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.03.736294</dc:identifier>
<dc:title><![CDATA[Immunoinformatics-Guided Design and In Silico Evaluation of a Multi-Epitope Vaccine Against Influenza A H10N5 and H3N2 Strains Based on Hemagglutinin and Neuraminidase Proteins]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.736062v1?rss=1">
<title>
<![CDATA[
PredHLM: quantitative and interpretable prediction of metabolic half-life in human liver microsomes 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.736062v1?rss=1
</link>
<description><![CDATA[
Motivation: Human liver microsome (HLM)-based metabolic stability assays are fundamental in early drug discovery, shaping pharmacokinetic profiles and oral bioavailability. However, these experimental assays are labor-intensive and time-consuming, limiting their application in large-scale virtual screening. Computational models can prioritize compounds at scale, yet most are classification-based, leaving quantitative and interpretable prediction of HLM half-life limited. Results: In this study, we developed a quantitative machine learning model for the direct prediction of HLM half-life (T1/2) by integrating 11,790 compounds combining in-house and curated public data. Among various combinations of molecular features and learning algorithms, the XGBoost model with RDKit 2D descriptors achieved the best predictive performance, with an RMSE of 0.507 and an R2 of 0.431 on an independent test set. Shapley Additive Explanations (SHAP) analysis identified lipophilicity and known metabolic soft-spot features as the primary contributors to the predictions. These results suggest that this quantitative approach provides a practical framework for defining metabolic stability margins, thereby supporting rapid Go/No-go decisions in preclinical drug discovery. Availability: The source code, data, and trained model are available at https://github.com/joshua-416/PredHLM.
]]></description>
<dc:creator><![CDATA[ Jang, J., Cho, N.-C., Oh, K.-S. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.736062</dc:identifier>
<dc:title><![CDATA[PredHLM: quantitative and interpretable prediction of metabolic half-life in human liver microsomes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.736184v1?rss=1">
<title>
<![CDATA[
FEATMAP: Targeted Correction of Acquisition Signatures Harmonizes Medical Foundation Model Embeddings and Enables Robust Task Generalization 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.736184v1?rss=1
</link>
<description><![CDATA[
Medical foundation models compress biomedical data into embeddings that support diverse downstream clinical tasks. However, successful model deployment is hampered by performance degradation on external data. It is recognized that embeddings capture acquisition signatures, such as hardware and technical differences, in addition to biology. Effective harmonization must remove the acquisition signature while preserving biological signals, a trade-off that current methods fail to balance adequately. Input-level normalization fails to eliminate acquisition signatures from embeddings, whereas embedding-level methods adjust features in an untargeted manner. We present FEATMAP, a harmonization approach that models acquisition signatures as geometric distortions between manifolds of similarly arranged embeddings. Using paired data that isolate the effect of acquisition signatures, FEATMAP fits a single global affine transformation per foundation model to correct acquisition signatures directly in the embedding space. This targeted, reusable correction aims to preserve biological and demographic variation while harmonizing across acquisition signatures. Across scanner and foundation-model harmonization in digital pathology and field-strength harmonization in brain MRI, FEATMAP improves cross-condition embedding similarity, reduces performance gaps without retraining, and suggests potential for the alignment of disparate embedding spaces.
]]></description>
<dc:creator><![CDATA[ Donle, L., Phillips, M., Gaber, F., Ramesh, S., Sacco, M., Hautaniemi, S., Virtanen, A., Bressem, K., Adams, L., Goon, K., Nevins, E., Robinett, R. A., Kochanny, S., Hassan, S., Dolezal, J., Pearson, A. T., Lengyel, E. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.736184</dc:identifier>
<dc:title><![CDATA[FEATMAP: Targeted Correction of Acquisition Signatures Harmonizes Medical Foundation Model Embeddings and Enables Robust Task Generalization]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.736099v1?rss=1">
<title>
<![CDATA[
Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.736099v1?rss=1
</link>
<description><![CDATA[
Recent advances in spatially resolved transcriptomics (SRT) enabled measurement of sets of pathway genes activity within tissues. However, existing gene set activity scoring methods overlook spatial dependencies among tissue locations, restricting their ability to capture region-specific pathway activities associated with disease pathology or cellular communication. Moreover, these methods lack significance-level inference for activity scores, provide limited interpretability of gene-level contribution to a pathway, and scale poorly to advanced large-size SRT datasets. To address these limitations, we present GESSO (Gene sEt activity Score analysis with Spatial lOcation), a spatially informed gene set scoring method adaptable to diverse SRT platforms. GESSO models gene set activity levels through a graph-regularized matrix decomposition algorithm, jointly inferring spatially coherent gene set activity scores (GASs) and interpretable metagene weights that capture gene-level contributions. It further implements a permutation-based local significance test and a stratified low-resolution approximation that scales to high-resolution SRT datasets such as Visium HD, Stereo-seq, and Xenium Prime. Across 13 datasets from five SRT platforms, GESSO outperformed all existing methods in accuracy, calibration, interpretability, and scalability. Applications revealed novel biological programs, including spatially confined EMT activation within tumor-stroma interfaces, developmental signaling gradients across embryonic tissues, and coordinated B-cell, T-cell, and signaling pathways within germinal centers of human lymph node tissue, revealing the spatial organization of immune function at subregional resolution.
]]></description>
<dc:creator><![CDATA[ Yang, A. J., Tan, C., Ma, Y. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.736099</dc:identifier>
<dc:title><![CDATA[Interpretable and scalable spatial gene set activity analysis with GESSO uncovers functional tissue architecture]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.735966v1?rss=1">
<title>
<![CDATA[
CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.735966v1?rss=1
</link>
<description><![CDATA[
Intratumoral heterogeneity drives cancer recurrence and metastasis, yet single-cell drug response prediction faces severe "cross-domain" challenges, such as applying in vitro models to in vivo tissues or inferring metastatic resistance from primary tumors. These scenarios trigger distribution shifts arising from heterogeneous sequencing platforms, distinct tissue microenvironments, and metastatic evolution - problems rarely addressed by existing methods. We introduce CSGDA, a cell state-guided graph domain adaptation framework designed to predict drug responses across these biological heterogeneities. CSGDA incorporates biological priors to map gene expression into functional cell states, guiding a structure learning module to construct robust cell topology. To conquer distribution shifts, the model employs graph domain adaptation combined with a novel overlap penalty mechanism. Extensive benchmarks on five scRNA-seq datasets demonstrate that CSGDA outperforms state-of-the-art methods, achieving an average gain of ~6% in ACC and AUPR. Beyond prediction accuracy, we employed integrated gradients to effectively pinpoint key genes involved in drug resistance within a challenging cross-metastasis cisplatin dataset. These findings underscore CSGDA's superior performance in single-cell drug response prediction and its potential in resolving single-cell heterogeneity, paving the way for precision medicine.
]]></description>
<dc:creator><![CDATA[ Yan, F., Cao, X., Mao, F., You, Z., Chen, Y., Du, Z., Huang, Y.-A. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.735966</dc:identifier>
<dc:title><![CDATA[CSGDA: A Cell State-Guided Graph Domain Adaptation Network for Single-Cell Drug Response Prediction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.06.28.734076v1?rss=1">
<title>
<![CDATA[
A systematic analysis of machine learning pipelines for robust antimicrobial resistance prediction 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.06.28.734076v1?rss=1
</link>
<description><![CDATA[
Motivation: Antimicrobial resistance (AMR) has been identified as a top global public health threat. Accurate AMR phenotype prediction from whole-genome sequencing data is an essential tool for accelerating clinical decision-making and mitigating resistance spread. Although many previous works have explored the use of tree-based machine learning (ML) models to predict resistance, the field lacks a systematic evaluation of the training pipeline across a variety of pathogenic species and antibiotics. Results: Using nine clinically relevant species-antibiotic combinations from the NCBI antimicrobial susceptibility testing database, we present a detailed analysis of the ML pipeline and identify key factors affecting model performance and evaluation. We begin by relabelling all isolates using current CLSI minimum inhibitory concentration breakpoints to resolve inconsistencies and increase available data, resulting in up to a 19% label swap and 56% data enlargement per species-antibiotic combination. We identify several key training parameters including k-mer length, which can increase classification F1 scores by over 20 points compared to commonly used k-values, feature matrix truncation, which can induce polynomial time reductions with limited performance reduction, and ML model class. By comparing 5-fold cross-validation with evaluation on an unseen clinical dataset, we show that random cross-validation splits--often criticized as overly optimistic--can act as a strong proxy for downstream clinical performance, yielding closer F1 scores than phylogeny-aware splits in all cases. We finally present an interpretability study which shows that over 95% of k-mers used by our models are associated with identifiable genomic features. Our results highlight the importance of feature design, evaluation protocol, and biological analysis in genomic AMR prediction, and support tree-based models as a robust and interpretable method.
]]></description>
<dc:creator><![CDATA[ Aselstyne, A., Karthik, E. N., El Azami, M., Pogorelcnik, R., Fournier, Q., Chandar, S. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.06.28.734076</dc:identifier>
<dc:title><![CDATA[A systematic analysis of machine learning pipelines for robust antimicrobial resistance prediction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.03.736387v1?rss=1">
<title>
<![CDATA[
An Integrated Knowledge Graph and Network Medicine Pipeline for Drug Repurposing: Benchmarking Across Human Diseases and Application to Amyotrophic Lateral Sclerosis 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.03.736387v1?rss=1
</link>
<description><![CDATA[
Drug repurposing offers a practical strategy to identify new therapeutic uses for approved drugs, potentially reducing the time and cost associated with conventional drug development. We present a novel three-stage drug repurposing pipeline that integrates knowledge graph-based gene prediction, network-based drug-disease association analysis, and systematic classification of candidate drugs by therapeutic class. The pipeline integrates DGLinker to predict novel disease-associated genes, SAveRUNNER to identify drug repurposing candidates, and ATC Category Enrichment Analysis (ATCEA) to prioritise candidates by pharmacological class. We benchmarked the pipeline across twelve diseases using DrugBank and MEDI2-HPS as validation resources. Utilising DGLinker-expanded disease-gene sets as input increased the number of predicted repurposed drugs, while overall discriminative performance remained stable across diseases (AUROC 0.71-0.77). Application of ATCEA consistently improved precision, F1-score, and specificity, while reducing recall, reflecting a conservative prioritisation strategy that contracts the candidate space while retaining pharmacologically coherent drug-disease candidates. We further applied the pipeline to amyotrophic lateral sclerosis (ALS), a neurodegenerative disease with limited therapeutic options, and performed a deeper literature-based validation of the results. Incorporation of DGLinker-predicted genes substantially increased the number of significant candidate drugs and uncovered enriched ATC categories not identified using known ALS genes alone, including antidepressants and antipsychotics. Moreover, several drugs with supporting evidence available in the literature were identified only when DGLinker-predicted genes were used. Overall, 77 candidate drugs were prioritised within significantly enriched ATC categories, several of which are supported by previously published studies. To provide exploratory real-world support for these findings, we further evaluated candidate drugs in a longitudinal electronic health record (EHR) dataset of 2361 patients with ALS from King's College Hospital. Although the number of evaluable drugs was limited due to sample size, the EHR analysis provided additional clinically relevant context for selected prioritised drugs and pharmacological classes. Our pipeline demonstrates potential to accelerate drug repurposing by integrating complementary computational approaches to each step of the process, providing an end-to-end framework that showed robust performance across benchmarking experiments and use cases.
]]></description>
<dc:creator><![CDATA[ Jiang, A., Hu, J., Abdulle, Y., Pain, O., Iacoangeli, A. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.03.736387</dc:identifier>
<dc:title><![CDATA[An Integrated Knowledge Graph and Network Medicine Pipeline for Drug Repurposing: Benchmarking Across Human Diseases and Application to Amyotrophic Lateral Sclerosis]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.06.736715v1?rss=1">
<title>
<![CDATA[
Beyond infinite sites: Generalized ABBA-BABA statistic for deeper phylogenies 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.06.736715v1?rss=1
</link>
<description><![CDATA[
The Patterson's D statistic detects gene flow from ABBA-BABA site patterns, but its biallelic site patterns fail under deeper divergences where multiple hits cause false positives. We propose two extensions, D+ and D*. Both incorporate multiallelic site patterns to reduce saturation bias under JC and F84 model. Simulations show that D+ and D* both remain correctly null under all conditions and detect gene flow effectively, with distinct advantages: D+ guarantees non-negativity of the denominator, while D* provides greater robustness when mutation rates vary across genomic regions. The source code and binary files are publicly available at https://github.com/chaoszhang/ASTER.
]]></description>
<dc:creator><![CDATA[ Zhang, C., Nielsen, R. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.06.736715</dc:identifier>
<dc:title><![CDATA[Beyond infinite sites: Generalized ABBA-BABA statistic for deeper phylogenies]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.06.736502v1?rss=1">
<title>
<![CDATA[
EpiBinder: a multimodal framework for cell-type-specific prediction and interpretation of transcription factor binding 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.06.736502v1?rss=1
</link>
<description><![CDATA[
Transcription factor (TF) occupancy in vivo depends not only on the underlying DNA sequence but also on the local epigenetic environment, which varies across cell types and strongly influences whether sequence-encoded binding potential becomes functional. Here we present EpiBinder, a multimodal deep-learning framework for cell-type-specific prediction of TF binding that jointly models DNA sequence with base-resolution epigenetic information, including cytosine methylation from whole-genome bisulfite sequencing and chromatin accessibility from DNase I hypersensitivity data. Across multiple human cell lines, EpiBinder consistently outperforms strong sequence-only baselines, improving TF-binding prediction by up to 10% in area under the precision-recall curve. Beyond predictive performance, EpiBinder provides base-level attribution maps that enable systematic interrogation of regulatory context, including candidate methylation-sensitive loci, contextual motif dependencies, and putative TF-TF interactions. These results position EpiBinder as a practical framework for modeling and exploring the local regulatory grammar underlying cell-type-specific TF occupancy.
]]></description>
<dc:creator><![CDATA[ Solozabal, R., Baichorov, A., Miodownik, I., Avioz, T., Song, L., Matabuena, M., Takac, M., Afek, A. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.06.736502</dc:identifier>
<dc:title><![CDATA[EpiBinder: a multimodal framework for cell-type-specific prediction and interpretation of transcription factor binding]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.03.736295v1?rss=1">
<title>
<![CDATA[
Gene regulatory co-expression networks decipher potential lncRNA-miRNA-mRNA interactions modulating transcription regulation in neurodegeneration 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.03.736295v1?rss=1
</link>
<description><![CDATA[
Neurodegenerative diseases are complex disorders characterised by progressive neuronal loss and widespread transcriptomic dysregulation; however, the coordinated interactions among coding and non-coding RNAs that contribute to disease progression remain incompletely understood. In this study, RNA-seq datasets from disease-relevant neuronal populations and brain regions representing Alzheimer's disease (AD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS) were analysed using an integrative network-based framework. Differential expression analysis coupled with weighted gene co-expression network analysis identified modules significantly correlated with disease and prioritised highly connected hub genes. Integration of these hub genes with curated RNA interaction database enabled the construction of candidate lncRNA-miRNA-mRNA regulatory networks. Functional enrichment analysis revealed Gene Ontology biological processes associated with synaptic signalling, mitochondrial function, RNA metabolism and neuroinflammatory responses across neurodegenerative conditions. The inferred regulatory networks suggested both disease-specific and shared post-transcriptional regulatory modules involving key hub genes and non-coding RNAs. Additionally, putative sequence variants were identified within untranslated regions of selected hub genes, suggesting potential alterations in miRNA-mediated regulations. Therefore, this study provides a systems-level view of transcriptomic dysregulation across major neurodegenerative diseases and identifies candidate regulatory interactions and molecular targets for future functional investigation
]]></description>
<dc:creator><![CDATA[ Venkatesan, A., Sinha, P., Basak, J., Bahadur, R. ]]></dc:creator>
<dc:date>2026-07-08</dc:date>
<dc:identifier>doi:10.64898/2026.07.03.736295</dc:identifier>
<dc:title><![CDATA[Gene regulatory co-expression networks decipher potential lncRNA-miRNA-mRNA interactions modulating transcription regulation in neurodegeneration]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.05.736569v1?rss=1">
<title>
<![CDATA[
A foundation model enables prediction of natural product molecular properties, bioactivity, and structural similarity from biosynthetic gene cluster sequence 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.05.736569v1?rss=1
</link>
<description><![CDATA[
Genome mining is a powerful technique in natural product discovery, where biosynthetic gene clusters that are likely to produce novel or desirable natural products are identified through bioinformatic analysis. There are many more predicted biosynthetic gene clusters than can easily be experimentally characterized. Additional computational methods to prioritize biosynthetic gene clusters by the bioactivity, structural properties, or novelty of the product would make genome mining more efficient. Multiple machine learning/artificial intelligence models have been developed to predict product properties from biosynthetic gene cluster sequence, but they are limited by small quantities of training data. Model pretraining with unlabeled data is a powerful technique to develop models that can learn on a limited amount of labeled training data. Biosynthetic gene clusters are well suited to this strategy because there are many predicted clusters with only a small percentage being characterized. This paper reports BGC-MLM, a foundation model that is pretrained with a masked language task on predicted biosynthetic gene clusters and then fine-tuned for downstream applications including prediction of product structural class, bioactivity, chemical properties, counts of functional groups, and chemical fingerprint. Comparison to a model trained without pretraining shows that pretraining generally improves performance. BGC-MLM shows better or similar performance to existing specialized methods for these tasks, demonstrating its utility as a foundation model for natural product genome mining.
]]></description>
<dc:creator><![CDATA[ Walker, A. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.05.736569</dc:identifier>
<dc:title><![CDATA[A foundation model enables prediction of natural product molecular properties, bioactivity, and structural similarity from biosynthetic gene cluster sequence]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.06.736794v1?rss=1">
<title>
<![CDATA[
Region-Level Design and Analysis of CRISPR Perturbation Screens with FRACTEL 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.06.736794v1?rss=1
</link>
<description><![CDATA[
We present FRACTEL, a statistical framework for region-level analysis of CRISPR perturbation screens. FRACTEL aggregates gRNA p-values using a bounded minimum across order statistics, preserving scale and enabling adaptive sensitivity to sparse or diffuse effects. Region-level null distributions are estimated via simulation, ensuring precise type I error control. Simulations and real CRISPRi/a datasets demonstrate improved power and replication rate over gRNA-level analyses. FRACTEL also informs experimental design, revealing trade-offs between gRNA redundancy and efficacy and identifying inherent limits in single-cell repression screens of lowly expressed genes. The method integrates with existing pipelines and supports diverse CRISPR screening applications.
]]></description>
<dc:creator><![CDATA[ Doty, R. W., ter Weele, M. A., Barrera, A., Bounds, L. R., Gersbach, C. A., Allen, A. S. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.06.736794</dc:identifier>
<dc:title><![CDATA[Region-Level Design and Analysis of CRISPR Perturbation Screens with FRACTEL]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.736203v1?rss=1">
<title>
<![CDATA[
Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.736203v1?rss=1
</link>
<description><![CDATA[
Allosteric regulation represents a fundamental mechanism of protein function, yet distinguishing allosteric from orthosteric protein binding sites remains a persistent computational challenge. While multimodal protein foundation models offer the potential to integrate complementary biological signals including sequence, structure, functional annotations, and conformational dynamics, their performance determinants in allosteric binding site detection remain poorly understood. We introduce a unified computational framework for profiling multimodal protein foundation models across distinct binding-site separability regimes. Rather than evaluating models solely by predictive accuracy, the framework combines systematic modality embedding ablations, encoder architecture comparisons, and variance decomposition to characterize how evolutionary, structural, functional, and dynamical information contribute to allosteric site discrimination. Using the OneProt multimodal model, we evaluate two complementary levels of multimodal integration: (a) encoder architectures that differ in the modalities incorporated during pretraining, and (b) downstream combinations of pocket, sequence, and text embeddings used for classification. To systematically probe the determinants of model performance, we benchmark these configurations across four assembled datasets of protein complexes representing a spectrum of biological complexity and a range of structural, dynamic, and evolutionary context for orthosteric and allosteric binding sites. Through comprehensive embedding ablations, encoder architecture comparisons, and variance decomposition, we demonstrate that model performance is governed primarily by intrinsic dataset properties rather than architectural complexity, with dataset identity accounting for 63.7% of explainable variance. Across all examined datasets, we identify three distinct separability regimes: a low-separability regime where current representations fail to reliably distinguish the two classes; an intermediate regime where multimodal integration substantially improves performance; and a high-separability regime where most architectures converge to near-ceiling performance. Critically, embedding contributions are regime-dependent: pocket geometry dominates when regulatory classes share structural contexts, while text and sequence embeddings become essential when evolutionary constraint distinguishes them. At the encoder level, structural and molecular dynamics encoders provide the greatest benefit in intermediate- and high-separability settings. Structure-functional analysis of correctly classified binding sites reveals that prediction success reflects the underlying biological organization of each regime. These findings establish that the success of multimodal foundation models depends critically on alignment between available modalities and the biological signatures that distinguish regulatory classes in each dataset.
]]></description>
<dc:creator><![CDATA[ Bazarova, A., Verkhivker, G. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.736203</dc:identifier>
<dc:title><![CDATA[Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.06.736857v1?rss=1">
<title>
<![CDATA[
Tracing the regulatory atlas of non-coding RNA in human labour 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.06.736857v1?rss=1
</link>
<description><![CDATA[
The early onset of labour increases mortality and developmental risks for a human newborn. Key genes in human labour have been investigated using multiple modalities, but their regulation by non-coding RNA (e.g. lncRNA and miRNA) remains incomplete. This study explores the three-way relationship between labour-associated transcription factors (TFs), miRNA and lncRNA suggested by the competing endogenous RNA (ceRNA) hypothesis, to understand the underlying regulatory framework. Experimentally validated miRNA-lncRNA interactions are modelled using five distinct machine learning (ML) architectures to predict 20469 labour-linked miRNA-lncRNA interactions. Known mRNA-ncRNA interactions from databases were included to construct a tripartite network, and a subset of 9989 labour-linked network motifs containing TFs were isolated and analysed. Gene enrichment of nodes in TF-lncRNA-miRNA network, as well as validation from public myometrial datasets indicate high significance in contractile pathways including immune signalling. Experimentally unconfirmed tripartite network motifs have been found, and we elaborate on their potential regulation in labour using 8 TF-lncRNA-miRNA network motifs. A unified ncRNA-TF regulatory atlas in labour has been synthesized, and a complete summary of the tripartite network motifs can be accessed and visualised using the user-friendly, public database.
]]></description>
<dc:creator><![CDATA[ Magateshvaren Saras, M. A., Ahmad, S., Smith, R., Mitra, M. K., Tyagi, S. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.06.736857</dc:identifier>
<dc:title><![CDATA[Tracing the regulatory atlas of non-coding RNA in human labour]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.736020v1?rss=1">
<title>
<![CDATA[
netPCF: Geometry-Aware Pair Correlation Functions for Spatial Biology 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.736020v1?rss=1
</link>
<description><![CDATA[
Spatial organisation is a defining feature of biological systems, underpinning cellular interactions, tissue function, disease progression and therapeutic response. Identifying and quantifying spatial organisation may require methods that resolve relationships across spatial scales. The pair correlation function (PCF) quantifies spatial dependence between points across multiple length scales, but its standard Euclidean formulation is poorly suited to data defined on irregular, curved or otherwise structured domains, where tissue geometry may constrain biological organisation and distort Euclidean distances. Here, we introduce netPCF, a geometry-aware extension of the PCF for quantifying spatial organisation on complex biological domains. By representing tissue structures, anatomical surfaces and other constrained geometries as spatial networks, netPCF generalises the PCF beyond extrinsic Euclidean settings. The framework derives the expected behaviour of the statistic under complete spatial randomness using interpretable finite-support kernels, provides bootstrap-based uncertainty quantification, and includes practical criteria for assessing domain discretisation adequacy. We further extend netPCF to marked (labelled) biological data using feature kernels for categorical and continuous attributes, enabling unified analysis of cell identities, marker intensities, phenotypic states, gene expression and other quantitative features on structured domains in any spatial dimension. All methods are implemented in the open-source Python package spacenet. Synthetic studies show that netPCF recovers classical Euclidean behaviour on sufficiently resolved networks and is robust to common imaging noise. We demonstrate its utility in two biological applications. In three-dimensional imaging mass cytometry data from HER2+ breast carcinoma, netPCF separates tissue architecture-driven proximity from biologically meaningful endothelial and immune cell organisation. In reconstructed surfaces of developing murine embryos, netPCF identifies a transition in the Wnt1-Wnt6 relationship from short-range co-localisation at E9.5 to spatial exclusion at E11.5, a pattern of ectodermal boundary refinement not captured by prior voxel-wise co-expression analysis. Overall, netPCF provides a statistically grounded and practical framework for quantifying spatial organisation on complex biological domains.
]]></description>
<dc:creator><![CDATA[ Moore, J. W., Bull, J. A., Byrne, H. M. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.736020</dc:identifier>
<dc:title><![CDATA[netPCF: Geometry-Aware Pair Correlation Functions for Spatial Biology]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.735197v1?rss=1">
<title>
<![CDATA[
MetaboCensoR: A Shiny Application for Data Filtering in Untargeted LC-MS Metabolomics to Enhance Interpretability 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.735197v1?rss=1
</link>
<description><![CDATA[
Untargeted LC-MS metabolomics datasets often contain large numbers of redundant and non-informative features arising from background contaminants, multiple ion forms, poorly integrated peaks, and other low-quality signals. These features complicate downstream analysis by inflating feature space, degrading molecular networks, impeding pathway analysis, and obscuring statistically meaningful changes. Here, we present MetaboCensoR, an input-versatile Shiny application and local R package for analyte-centric peak table filtering. The workflow integrates four complementary modules for blank filtering, redundant ion-species filtering, quality-control filtering, and peak-based filtering. MetaboCensoR also provides interactive threshold optimization, exportable annotation tables, and synchronized filtering of associated .mgf files. The approach was evaluated across three independent datasets covering plant extracts, human cell lines, and bacterial interactions. Across these case studies, data filtering reduced feature redundancy and improved downstream interpretation in feature-based molecular networking, pathway-level functional analysis, and differential abundance testing, while preserving known target metabolites. These results show that systematic peak table filtering can substantially improve the interpretability and analytical value of untargeted metabolomics data.
]]></description>
<dc:creator><![CDATA[ Plyushchenko, I. V., Luzzatto-Knaan, T. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.735197</dc:identifier>
<dc:title><![CDATA[MetaboCensoR: A Shiny Application for Data Filtering in Untargeted LC-MS Metabolomics to Enhance Interpretability]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.735756v1?rss=1">
<title>
<![CDATA[
Application of class-balancing algorithms to diverse plasma metabolomics datasets using brain tumor as an example 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.735756v1?rss=1
</link>
<description><![CDATA[
Class imbalance remains a challenge in metabolomics research, where biological and technical variability can affect statistical inference and machine learning (ML) performance. Class-balancing algorithms address this issue by either increasing minority-class observations or reducing the number of majority-class samples. This study evaluated the impact of oversampling and undersampling algorithms on targeted and untargeted metabolomics datasets derived from LC-MS and GC-MS analyses of plasma samples from patients with glioblastoma, meningioma, and controls. Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling (RUS) were applied to balance the datasets, and their effects on data distribution, inter-feature correlations, and machine learning model performance were compared. RUS preserved the original feature distributions but reduced representativeness by removing the majority-class samples. In contrast, SMOTE introduced synthetic samples that altered covariance structures, increasing the risk of overfitting, particularly in small datasets (n=10). These effects diminished with larger groups (n=30), partially restoring correlations between metabolites. Model performance varied across the class-balancing algorithms. Random Forest classifiers benefited from both balancing methods, with undersampling often yielding higher F1 scores, whereas Support Vector Machine models showed reduced classification performance. These findings highlight the importance of selecting class-balancing strategies based on dataset size, analytical platform, and ML algorithm in metabolomics studies.
]]></description>
<dc:creator><![CDATA[ Godlewski, A., Solowiej, K., Mojsak, P., Godzien, J., Zelkowska, J., Kretowski, A., Lyson, T., Burdukiewicz, M., Kaminski, K., Ciborowski, M. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.735756</dc:identifier>
<dc:title><![CDATA[Application of class-balancing algorithms to diverse plasma metabolomics datasets using brain tumor as an example]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.736034v1?rss=1">
<title>
<![CDATA[
Deep-Interact Studio: An Interactive Deep Learning Model Building Platform for Biomolecular Interaction Prediction 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.736034v1?rss=1
</link>
<description><![CDATA[
Motivation: Deep learning has rapidly become essential for predicting biomolecular interactions; however, most web-tools expose only a single, pre-built model with a fixed, non-configurable architecture that users cannot redesign, retrain on their own data, or compare; they are typically dedicated to one interaction type and often one species, and report prediction scores with little interpretability. These constraints force researchers across several disconnected, single-purpose tools and limit the flexibility, reproducibility, and long-term usability of existing platforms. Results: We present Deep-Interact Studio, a unified, web-based deep-learning platform that addresses these limitations by shifting interaction prediction from a model-centric to a user-driven, comparative, and interpretable paradigm. Within a single interface spanning all four interaction classes, namely protein-protein, drug-target, RNA-protein, and protein-DNA, users design their own model architectures layer by layer, configure training hyperparameters, and train them on their own data, including custom, species-specific datasets. Multiple user-built models can then be trained under identical conditions and compared side by side at both the training and inference levels, while integrated interpretability, including SHAP-based feature attribution, embedding-space visualization, and interaction hub analysis, turns predictions into auditable, mechanistically grounded results. Deep-Interact Studio is, to our knowledge, the only such platform to combine fine-grained per-layer model customization with multi-model comparison and interpretability, offering a flexible and transparent alternative to fixed, single-purpose tools.
]]></description>
<dc:creator><![CDATA[ Sarkar, D., Bardhan, K., Sarkar, C. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.736034</dc:identifier>
<dc:title><![CDATA[Deep-Interact Studio: An Interactive Deep Learning Model Building Platform for Biomolecular Interaction Prediction]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.736001v1?rss=1">
<title>
<![CDATA[
BRAID: RT-PCR-calibrated conformal intervals for splicing ΔPSI 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.736001v1?rss=1
</link>
<description><![CDATA[
Differential splicing workflows usually report a {Delta}PSI point estimate and a statistical score, but these outputs do not directly state whether the RNA-seq estimate is close enough to an orthogonal validation measurement. We developed BRAID as a post-processing calibration step for splicing analyses. BRAID estimates RNA-seq {Delta}PSI from rMATS inclusion and skipping counts, retains the upstream caller evidence, and adds a 95% interval whose width is calibrated from empirical RNA-seq-to-RT-PCR residuals using split conformal prediction. The packaged differential-splicing calibrator uses a residual half-width of q = 0.341, estimated from 162 RT-PCR-validated skipped-exon events. We evaluated BRAID on three RT-PCR validation datasets covering TRA2 knockdown, mouse cerebellum versus liver, and a prostate epithelial-to-mesenchymal comparison. On the pooled common set of 139 cassette-exon events, BRAID reached 0.971 RT-PCR coverage, whereas MAJIQ, betAS, and rMATS-derived intervals reached 0.518, 0.734, and 0.633, respectively. BRAID also had the lowest pooled interval score, 0.720, compared with 2.040 for MAJIQ, 1.414 for betAS, and 1.625 for rMATS. Applying the same residual calibration to other caller outputs brought MAJIQ, betAS, rMATS, and SUPPA2 {Delta}PSI estimates close to nominal RT-PCR coverage, indicating that the gain came from interval calibration rather than from a caller-specific point estimate. In a TRA2 positive-negative validation panel, using q as a hard rMATS effect-size cutoff reduced recall, whereas using q as an interval half-width improved RT-PCR coverage. Applied to a public DM1 skeletal-muscle rMATS table, BRAID reduced 967 large-effect significant events to 68 high-confidence interval-supported events and retained known DM1 and muscle-splicing signals. BRAID provides a practical calibrated reliability layer for RNA-seq splicing studies where downstream follow-up depends on the precision of reported {Delta}PSI estimates.
]]></description>
<dc:creator><![CDATA[ Park, J., Kang, K. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.736001</dc:identifier>
<dc:title><![CDATA[BRAID: RT-PCR-calibrated conformal intervals for splicing ΔPSI]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.02.735979v1?rss=1">
<title>
<![CDATA[
Cross-architecture ensembling of DNA foundation models improves the precision and stability of chimera detection in long-read metagenomic bins 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.02.735979v1?rss=1
</link>
<description><![CDATA[
Motivation: Chimeric metagenome-assembled genomes (MAGs) that pool DNA from multiple organisms contaminate downstream analyses. Marker-gene tools such as CheckM2 miss low-level chimerism, and DNA foundation models have been proposed as a sequence-composition alternative, but whether large autoregressive models (Evo2, 7B parameters) outperform smaller contrastive models (DNABERT-S, 117M) has not been rigorously tested.
]]></description>
<dc:creator><![CDATA[ MinSeo, K., Jae-Ho, S. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.02.735979</dc:identifier>
<dc:title><![CDATA[Cross-architecture ensembling of DNA foundation models improves the precision and stability of chimera detection in long-read metagenomic bins]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735932v1?rss=1">
<title>
<![CDATA[
UVfinder: a tool to extract bryophyte sex-linked gene copies from the GoFlag408 probe set 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735932v1?rss=1
</link>
<description><![CDATA[
Target enrichment sequencing using probe sets like GoFlag 408 has revolutionized phylogenetics, yet recent genomic data indicate that some probes may be sex-linked, potentially introducing topological conflict while also allowing studies of sex-specific evolutionary processes. To test for sex-linkage across the bryophytes, we developed UVfinder, a pipeline designed to identify sex-linked GoFlag loci across published moss genomes and enable sex-aware downstream analyses. Applying UVfinder to 50 dioicous moss genomes, we identified 93 probes that exhibit sex-linkage in one or more lineages, providing genomic evidence for neo-sex chromosome formation via autosome-sex chromosome fusion and gene translocation. Furthermore, by comparing species trees derived from sex-linked versus autosomal loci in Hypnales and Dicranidae, we demonstrate that sex-linked loci harbor phylogenetic information that is distinct from that in autosomes. We also discovered a pervasive female sampling bias in the genomic data, perhaps reflecting a preference among collectors for plants with sporophytes. Ultimately, our findings highlight the dynamism in sex linkage across bryophytes and suggest that sex-aware phylogenomics can be used to reconstruct ancestral karyotypes and potentially resolve topological conflict. We expect that UVfinder will facilitate the further study of sex-specific evolutionary processes, particularly with improved genome assemblies and increased sampling in males.
]]></description>
<dc:creator><![CDATA[ Kim, S., Bowman, J., Braun, E. L., McDaniel, S. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735932</dc:identifier>
<dc:title><![CDATA[UVfinder: a tool to extract bryophyte sex-linked gene copies from the GoFlag408 probe set]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735863v1?rss=1">
<title>
<![CDATA[
Genomic compartmentalization of pervasive sex-biased gene expression in the vine mealybug Planococcus ficus 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735863v1?rss=1
</link>
<description><![CDATA[
The vine mealybug, Planococcus ficus, is a globally invasive pest of grapevine and a vector of leafroll viruses. Like other mealybugs, it reproduces through paternal genome elimination, a sex-determination system that operates without sex chromosomes and is associated with extreme sexual dimorphism. To characterize genome organization and sex-biased expression in this species, we generated a long-read reference genome spanning 369 Mb with 23,489 annotated genes and macrosynteny conserved with the citrus mealybug, Planococcus citri. Resequencing of four California field individuals yielded a first whole-genome estimate of nucleotide diversity and 132 microsatellite markers for population monitoring. Among 2,129 candidate secreted proteins, a conserved core is shared with P. citri, but each species carries a distinct set of lineage-specific effectors. Comparing adult male and female transcriptomes, we found sex-biased expression to be pervasive and skewed toward females: 41% of tested genes differed between the sexes, with female-biased genes both more numerous and showing larger fold changes. These female-biased genes were not randomly distributed but concentrated in discrete blocks of coordinately expressed, tandemly duplicated gene families, a pattern not previously described in a mealybug. Male- and female-biased secreted proteins also differed in origin, with male-biased proteins drawn from a conserved repertoire shared with P. citri and female-biased proteins spanning a more lineage-specific pool. Together, these results reveal a female-skewed, spatially clustered architecture of sex-biased expression in a mealybug that lacks sex chromosomes, and provide genomic resources for managing an invasive vineyard pest.
]]></description>
<dc:creator><![CDATA[ Cantu, D., Figueroa-Balderas, R., Sisterson, M., Minio, A., Cochetel, N., Naegele, R., Burbank, L. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735863</dc:identifier>
<dc:title><![CDATA[Genomic compartmentalization of pervasive sex-biased gene expression in the vine mealybug Planococcus ficus]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735870v1?rss=1">
<title>
<![CDATA[
ORBIT: Annotation-Aware Empirical Enrichment and Semantic Reranking for Interpretable Functional-Class Recovery 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735870v1?rss=1
</link>
<description><![CDATA[
Gene-set interpretation workflows are widely used to summarize transcriptomic and proteomic experiments, yet standard enrichment tools often return long, redundant result tables that require substantial manual consolidation. We developed ORBIT (Ontology-Ranked Biological Interpretation Tool), an annotation-aware interpretation workflow that combines empirical enrichment, semantic reranking, and redundancy-aware representative-term selection to prioritize interpretable functional summaries from gene sets. We evaluated ORBIT on a curated tiered benchmark of human functional-class gene sets spanning clean reference sets, size-ladder variants, and mixed-difficulty cases. On the 45-set core benchmark, ORBIT semantic achieved higher expected-class recovery than Enrichr and PANTHER Gene Ontology molecular-function baselines, with a mean reciprocal rank of 0.916 and top-1 recovery of 0.889. Bootstrap confidence intervals and paired permutation testing supported the robustness of this advantage, and supplemental analyses extended the comparison to g:Profiler. In a GPCR mixed-function case study, ORBIT compressed redundant enriched terms into semantic representative neighborhoods, illustrating how long enrichment outputs can be converted into reviewable biological summaries. We then used ORBIT to interpret immune-cell identity, interferon-response biology, and breast-cancer subtype programs. ORBIT linked PBMC3K markers to cytotoxic, antigen-presentation, and innate-immune cell states; prioritized antiviral, cytokine-response, RNA-binding, and secreted-factor biology after IFNB stimulation; and separated TCGA-BRCA basal-like proliferative chromosome/cell-cycle programs from luminal transporter and receptor-associated biology while retaining gene-level support.
]]></description>
<dc:creator><![CDATA[ Kidder, B. L. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735870</dc:identifier>
<dc:title><![CDATA[ORBIT: Annotation-Aware Empirical Enrichment and Semantic Reranking for Interpretable Functional-Class Recovery]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735284v1?rss=1">
<title>
<![CDATA[
SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735284v1?rss=1
</link>
<description><![CDATA[
Single cell RNA-seq (scRNA) has provided unprecedented resolution into cellular and clonal heterogeneity. Computational approaches have enabled recovery of differentiation dynamics, yet current approaches do not evaluate discontinuous differentiation processes present in malignant leukemia. To address these gaps, we developed SupeRJump: a jump-drift-diffusion based supervised cell-fate model (https://github.com/namwob44/SupeRJump/). We deploy this approach in human bone marrow, murine aging hematopoiesis, and lentivirally barcoded mouse models of acute myeloid leukemia. Our framework introduces a semi-supervised pseudotime strategy to fit a jump-drift-diffusion model and batch correction for lineage fate predictions from absorbing Markov chains. We introduce metrics to quantify cell skewness toward particular lineages, transitions through intermediate progenitor states toward terminally differentiated states, and discontinuous transition dynamics. We use these metrics to identify cells preferentially biased for differentiation, their underlying transcriptional networks, and gene programs responsible for differentiation discontinuity.
]]></description>
<dc:creator><![CDATA[ Bowman, M., Bandopadhyay, R., Singh, V., Telpoukhovskaia, M., Vander Velde, R., Shaffer, S. M., Trowbridge, J. J., Bowman, R. L. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735284</dc:identifier>
<dc:title><![CDATA[SupeRJump: Determining normal and leukemic differentiation fate through semi-supervised jump diffusion modeling]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735824v1?rss=1">
<title>
<![CDATA[
Determinants of Blood Group Antigen Expression and Prediction of Phenotypes by Machine Learning 
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</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735824v1?rss=1
</link>
<description><![CDATA[
Blood group antigens, defined by epitopes on the erythrocyte surface, are central to transfusion safety and maternal-fetal compatibility. While the genetic basis of many clinically relevant blood group antigens is well established, which structural and biophysical parameters determine whether a single-nucleotide variant gives rise to an antigenic phenotype remains unclear. Here, we integrate structural, biophysical, and evolutionary analyses to systematically evaluate features associated with single amino acid substitutions across 24 human protein-based blood group systems. We analyse 319 variants with curated phenotypic annotations alongside 481 control variants, identifying key determinants of null and antigenic phenotypes. Null variants are characterized by high evolutionary conservation, burial within the protein core, loss of hydrophobicity, increased polarity, and a propensity for arginine substitutions. Antigenic variants are also enriched in arginine; however, in contrast to null variants, they tend to occur at less conserved, more solvent-accessible, and structurally flexible sites. Supervised machine learning models trained on structural and biophysical descriptors were applied to distinguish (i) null and (ii) antigenic variants from controls, achieving balanced accuracies of 0.82 and 0.63, respectively. Feature importance analysis identified predicted pathogenicity, solvent accessibility, and evolutionary conservation as the most predictive determinants of null variants, whereas hydrophobicity, conservation, and flexibility dominated antigen prediction. This work establishes a framework linking molecular variation to blood group phenotypes and provides a foundation for predicting the impact of novel missense mutations in transfusion medicine and beyond.
]]></description>
<dc:creator><![CDATA[ Kranz, A.-C., Schneider, J., Gassner, C., Bublitz, M. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735824</dc:identifier>
<dc:title><![CDATA[Determinants of Blood Group Antigen Expression and Prediction of Phenotypes by Machine Learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735762v1?rss=1">
<title>
<![CDATA[
TrustPGS: When can a polygenic score be trusted? A per-individual reliability framework across ancestries 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735762v1?rss=1
</link>
<description><![CDATA[
Polygenic scores summarise genetic predisposition to a trait, but a population-level accuracy figure cannot tell a clinician whether a given prediction is reliable for the person in front of them. This gap is most consequential for individuals whose ancestry is under-represented in the discovery cohort, precisely the patients for whom a wrong trust call carries the highest clinical cost. We present TrustPGS, a framework that tells clinicians and downstream models which individual predictions can be trusted and which cannot, so that polygenic scores can inform clinical decisions rather than being acted on uniformly regardless of how well-supported each prediction is. The framework rests on two axes calibrated on a discovery cohort, the consensus of a Bayesian posterior-sample ensemble and the directional agreement of the top-magnitude linkage-disequilibrium blocks. We computed SBayesRC posterior-sample scores for ten polygenic traits in the 1000 Genomes Project phase-3 cohort and tested whether the resulting trust labels transfer, without recalibration, to the ancestrally diverse Simons Genome Diversity Project, comparing strict application of the European cutoffs, percentile-rank rescaling, and within-cohort recalibration. Percentile-rank rescaling preserved an enrichment factor above one in non-European populations for five of ten traits (Alzheimer disease, breast cancer, body mass index, LDL cholesterol, and systolic blood pressure), traits whose European and target-cohort distributions were shifted but comparable in shape. Three traits (coronary artery disease, height, and schizophrenia) carried distributions that differed in shape rather than location, a pattern traceable to discovery-cohort bias that recalibration could not repair either, and two further traits (type 2 diabetes and educational attainment) showed intermediate behaviour, present but never enriched in one case, and an apparent success that rank-mapping correctly unmasked as artefactual in the other. Because each of these patterns is detectable before any individual-level claim is made, TrustPGS gives clinicians and downstream models a falsifiable, per-trait basis for deciding when a reliability label can be trusted on a new population, rather than a single portability promise that holds or fails silently.
]]></description>
<dc:creator><![CDATA[ Onawole, A., Adegoke, R. A., Amoo, O. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735762</dc:identifier>
<dc:title><![CDATA[TrustPGS: When can a polygenic score be trusted? A per-individual reliability framework across ancestries]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735830v1?rss=1">
<title>
<![CDATA[
Genome editing of marsupial RSX reveals conserved and divergent principles in mammalian X-inactivation 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735830v1?rss=1
</link>
<description><![CDATA[
X-chromosome inactivation balances X-gene dosage between the sexes in eutherians and marsupials. The lncRNA Xist mediates X-inactivation in eutherians but is absent from marsupials. An evolutionarily unrelated lncRNA, RSX, has been identified in marsupials, but its role in X-inactivation is unresolved because genome editing in these mammals has only recently become feasible. Using CRISPR-Cas9 in the opossum, we show that RSX is required for the initiation of X-inactivation in marsupial embryos. RSX deletion later, in differentiated cells, causes modest X-gene de-repression, with repressive chromatin marks H3K27me3 and H3K9me3 retained. H2AK119ub is not enriched on the opossum inactive X, revealing divergent Polycomb-mediated regulation in therians. Our findings reveal stage-specific roles of RSX in marsupial X-inactivation and identify conserved and divergent features of X-dosage compensation in mammals.
]]></description>
<dc:creator><![CDATA[ Courtois, A., Menchero, S., Ogushi, S., Varsally, W., Wood, S., Decarpentrie, F., Yoshimi, R., Shiraishi, A., Inoue, K., Abe, T., VandeBerg, J. L., Snell, D. M., Kiyonari, H., Turner, J. M. A. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735830</dc:identifier>
<dc:title><![CDATA[Genome editing of marsupial RSX reveals conserved and divergent principles in mammalian X-inactivation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.01.735542v1?rss=1">
<title>
<![CDATA[
PACMOS: an R package for Projection And Classification of Multi-Omic Samples 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.01.735542v1?rss=1
</link>
<description><![CDATA[
Motivation: Integrated multi-omic analyses have transformed our understanding of cancer biology, giving rise to data-driven molecular classifications that capture disease heterogeneity beyond conventional histopathology. Among these approaches, multi-omic factor analysis (MOFA), a multimodal extension of principal component analysis, has been widely used to identify sources of molecular variation across omic layers and classify samples into molecular groups. However, classifying query samples according to an existing MOFA-based classification remains challenging, as there is no validated computational method for projecting samples into pretrained MOFA latent factor spaces. Results: We present PACMOS, an R package that provides a generalizable approach to project query samples into pretrained MOFA latent factor spaces. We validate PACMOS using two cancer datasets with published MOFA-based classifications - lung neuroendocrine neoplasms and pleural mesothelioma - showing that PACMOS preserves the existing MOFA latent factor space while allowing to classify query samples. Availability and implementation: PACMOS is an open-source R package available on the IARC bioinformatics GitHub organization (submitted to Bioconductor) at https://github.com/IARCbioinfo/PACMOS and DOI in Zenodo: https://doi.org/10.5281/zenodo.20933824, along with installation instructions and a vignette with an application. Supplementary information: Supplementary data are available in separate files.
]]></description>
<dc:creator><![CDATA[ Kalson, L., Sexton-Oates, A., Drevet, G., Fernandez-Cuesta, L., Foll, M., Alcala, N. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.01.735542</dc:identifier>
<dc:title><![CDATA[PACMOS: an R package for Projection And Classification of Multi-Omic Samples]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.06.736754v1?rss=1">
<title>
<![CDATA[
The genome organizer SMC1A mediates the gene expression response in inflammation by dissociating from nuclear-speckles and redistributing to the nuclear periphery 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.06.736754v1?rss=1
</link>
<description><![CDATA[
Nuclear architectural proteins are increasingly recognized as multifunctional regulators whose roles extend well beyond static genome organization. Here we report that SMC1A, a core subunit of the cohesin complex, undergoes a striking redistribution upon inflammatory stimulation in human monocytes, dissociating from nuclear speckles and accumulating at genomic regions enriched for stress-response genes with distinct exon-intron architectural properties. Through integrative analysis of RNA-seq, chromatin organization, and nuclear spatial data, we demonstrate that this redistribution has functional consequences at multiple levels of gene expression. SMC1A dissociation from speckles is accompanied by a reduction in intron retention events -consistent with a transition from a poised, pre-loaded transcriptional state toward active, efficient co-transcriptional processing- and by selective engagement with genes whose exon-intron architecture favors exon definition splicing, shorter nuclear mRNA residence times, and peripheral radial positioning. Genes affected by SMC1A silencing, by contrast, occupy more central nuclear positions and display fundamentally different structural properties, demonstrating that the two modes of SMC1A perturbation -stress-induced redistribution and depletion- are functionally and spatially non-equivalent. These findings suggest a genome compartmentalization in which DNA compositional preferences, gene architecture, radial positioning, and splicing mode converge to define gene sets capable of rapid, precise activation. SMC1A navigates this pre-existing landscape upon inflammatory cues, coordinating transcriptional and posttranscriptional responses simultaneously. We propose that stress-induced redistribution of architectural proteins within a largely invariant nuclear compartmental framework represents a general regulatory mechanism, one whose logic is encoded in the structural organization of the genome itself.
]]></description>
<dc:creator><![CDATA[ Papanikolaou, S., Marouli, M., Katifori, A., Kosmara, D., Tsapara, D., Mantis, C., Stavropoulou, A., Bertsias, G., Nikolaou, C. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.06.736754</dc:identifier>
<dc:title><![CDATA[The genome organizer SMC1A mediates the gene expression response in inflammation by dissociating from nuclear-speckles and redistributing to the nuclear periphery]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.64898/2026.07.04.736494v1?rss=1">
<title>
<![CDATA[
ThermoFusion: A Multimodal Deep Learning Framework for Generalizable Prediction of Enzyme Thermostability 
]]>
</title>
<link>
https://www.biorxiv.org/content/10.64898/2026.07.04.736494v1?rss=1
</link>
<description><![CDATA[
Protein thermostability is a critical property for both industrial and biomedical enzyme applications, yet experimental evaluation of mutation-induced stability changes remains laborious and costly. Here, we present ThermoFusion, a hybrid deep learning framework that integrates 3D protein structure embeddings from ThermoMPNN with sequence-based embeddings from the pretrained protein language model ESM2 to predict the effects of single-point mutations on protein stability ({Delta}{Delta}G). ThermoFusion exhibits robust generalization, maintaining high predictive accuracy across out of distribution sequences with low identity to the training set -- a scenario where many other machine learning models, including ThermoMPNN and state-of-the-art tools, perform poorly due to reliance on memorization. Benchmarking on a curated enzyme dataset comprising of 105 enzymes and 3144 mutations shows that ThermoFusion reliably identifies stabilizing mutations while accurately predicting stability for enzymes beyond its training set. These results establish ThermoFusion as a powerful tool for rational enzyme design beyond its training set.
]]></description>
<dc:creator><![CDATA[ Wei, Y., Eberini, I., Meyer, F. ]]></dc:creator>
<dc:date>2026-07-07</dc:date>
<dc:identifier>doi:10.64898/2026.07.04.736494</dc:identifier>
<dc:title><![CDATA[ThermoFusion: A Multimodal Deep Learning Framework for Generalizable Prediction of Enzyme Thermostability]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory</dc:publisher>
<prism:publicationDate>2026-07-07</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
