Hyper Recent

Predicting phenotypic properties of a virus directly from its sequence data is an attractive goal for viral epidemiology. Here, we focus narrowly on the Orthocoronavirinae clade and demonstrate models that are powerfully predictive for a human-pathogen phenotype with >86% average precision and >99% average recall on the withheld test set groups, using only Orf1ab codon frequencies. We show alternative examples for other viral coding sequences and feature representations that do not perform well and discuss what distinguishes the models that are performant. These models point to a small subset of features, specifically 5 codons, that are critical to the success of the models. We discuss and contextualize how this observation may fit within a larger model for the role of translation in virus-host agreement.

ORF1ab Codon Frequency Model Predicts Host-Pathogen Relationship in Orthocoronavirinae

Similar Papers

Similar Papers

biorxiv
Wed Jan 22 2025
STExplorer: Navigating the Micro-Geography of Spatial Omics Data
Spatial transcriptomics (ST) has the potential to provide unprecedented insights into gene expression across tissue architecture, but existing analytical methods often overlook the full complexity of the spatial dimension. We present STExplorer, an R package that adapts well-established computational geography (CG) methods to explore the micro-geography of spatial omics data. By incorporating tech...
Zormpas, E.
•
Vlachogiannis, N. I.
•
Resteu, A.
•
Unsworth, A.
...•
Cockell, S. J.

biorxiv
Wed Jan 22 2025
SpatialFormer: Universal Spatial Representation Learning from Subcellular Molecular to Multicellular Landscapes
Spatial transcriptomics quantifies gene expression within its spatial context, significantly advancing biomedical research. Understanding gene spatial expression and the organization of multicellular systems is vital for disease diagnosis and studying biological processes. However, existing models often struggle to integrate gene expression data with cellular spatial information effectively. In th...
Wang, J.
•
Huang, Y.
•
Winther, O.

biorxiv
Wed Jan 22 2025
A deep learning method for predicting interactions for intrinsically disordered regions of proteins
Intrinsically disordered proteins or regions (IDPs/IDRs) adopt diverse binding modes with different partners, ranging from ordered to multivalent to fuzzy conformations in the bound state. Characterizing IDR interfaces is challenging experimentally and computationally. Alphafold-multimer and Alphafold3, the state-of-the-art structure prediction methods, are less accurate at predicting IDR binding ...
Majila, K.
•
Ullanat, V.
•
Viswanath, S.

biorxiv
Wed Jan 22 2025
Comparative evaluation of computational methods for reconstruction of human viral genomes
The increasing availability of viral sequences has led to the emergence of many optimized viral genome reconstruction tools. Given that the number of new tools is steadily increasing, it is complex to identify functional and optimized tools that offer an equilibrium between accuracy and computational resources as well as the features that each tool provides. In this paper, we surveyed open-source ...
Sousa, M. J. P.
•
Toppinen, M.
•
Pyöriä, L.
•
Hedman, K.
...•
Pratas, D.

biorxiv
Wed Jan 22 2025
Accurate tiling of spatial single-cell data with Tessera
Single-cell spatial transcriptomics reveals how cells organize in healthy and diseased tissues. From these data, tissue segmentation analysis defines discrete compartments that organize cells into functional multicellular units. Existing methods for automated tissue segmentation rely on spatial smoothing to define spatially coherent regions but often blur the boundaries between adjacent tissue com...
Stein, D. J.
•
Tran, M.
•
Korsunsky, I.

biorxiv
Wed Jan 22 2025
Breaking the Phalanx: Overcoming Bacterial Drug Resistance with Quorum Sensing Inhibitors that Enhance Therapeutic Activity of Antibiotics
Antibiotic-resistant bacterial infections loom over humanity as an increasing deadly threat. There exists a dire need for new treatments, especially those that synergize with our existing arsenal of antibiotic drugs to help overcome the gap in antibiotic efficacy and attenuate the development of new antibiotic-resistance in the most dangerous pathogens. Quorum sensing systems in bacteria drive the...
Beasley, J.-M.
•
Dorjsuren, D.
•
Jain, S.
•
Rath, M.
...•
Muratov, E.

biorxiv
Wed Jan 22 2025
Quantification of spliced and unspliced transcripts by velocyto is inaccurate for 5'-sequencing data
RNA velocity allows for the prediction of future states of individual cells based on their current transcriptional activity. The technique depends on the separate quantification of spliced and unspliced transcripts in single-cell RNA-sequencing data, first introduced by velocyto in 2018. Since its introduction, significant advances have been made in the field, including new protocols by 10x Genomi...
Klingler, D.
•
Schleicher, J. T.
•
Claassen, M.

biorxiv
Wed Jan 22 2025
Quantifying the ~75-95% of Peptides in DIA-MS Datasets that were not Previously Quantified
We demonstrate an algorithm termed GoldenHaystack (GH) that, compared to the leading DIA-MS algorithm, (a) quantifies and identifies with better FDR accuracy the peptides found in FASTA search spaces (~5-25% of analytes in DIA-MS datasets), (b) quantifies the remaining ~75-95% of analytes that were previously unquantified, and (c) runs ~40-200x faster (or ~1-10x faster than the LC-MS). Specificall...
Saxena, G.
•
Fu, Q.
•
Binek, A.
•
Van Eyk, J.

biorxiv
Wed Jan 22 2025
Hybrid Generative Model: Bridging Machine Learning and Biophysics to Expand RNA Functional Diversity
The RNA world hypothesis suggests that RNA once catalyzed reactions now performed by proteins. Rediscovering these functions requires exploring sequence spaces beyond natural RNAs. While machine learning (ML)-based RNA design shows promise, it struggles to extrapolate beyond training data. In contrast, biophysics-based approaches leveraging RNA secondary (2D) structure operate independently of tra...
Opuu, V.