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Khabre desk
Recent reporting, analysis and explainers from the Bioinformatics desk.
science
A bioRxiv preprint introduces LangAAI, a model that uses antibody and antigen sequences to model antibody hypervariable regions without requiring 3D structures. The authors report that changing the antigen input to a non-binding decoy particularly affects sequence recovery in the CDR3 loop.
science
A bioRxiv preprint introduces Chaperone, a framework for coordinating protein-design tools. Its authors report reduced token use and GPU costs across nine design tasks, alongside results from several protein-design campaigns.
science
A bioRxiv preprint reports that correcting ambient RNA before detecting doublets can leave more doublet-containing droplets in single-cell RNA-sequencing data. The authors recommend detecting doublets first, except when using CellBender.
science
A bioRxiv preprint introduces AuditPPI, a framework for examining the signals behind protein-interaction predictions. Its authors report that benchmark performance can reflect cellular context and other cues, with limited evidence that the predictive signals specifically capture interaction interfaces.
science
A bioRxiv preprint presents stEDGE, a computational framework that uses local tissue boundaries to reconstruct nested spatial domains and transition interfaces. The authors report results across 15 tissue sections and an application to cell-resolved profiling of fibrotic human lung.
science
A bioRxiv preprint presents Z-Hunt-DP, a dynamic-programming reformulation of a thermodynamic Z-DNA prediction method. The authors report that it preserved the original minimum-energy result while producing a 3.30 × 10^5 speedup at 20 dinucleotide positions.
science
A University of Gothenburg preprint models glycan motifs as a containment hierarchy rather than independent features. Across 45 glycomics datasets, the authors report more estimated true positives and identify motif redistributions that conventional marginal analysis misses.
science
A bioRxiv preprint reports that the protein language model ESM-2 retains meaningful biological information in intrinsically disordered protein regions, despite assigning them less attention. The authors also derive structural features of these proteins from the model’s internal signals.