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Khabre desk
Recent reporting, analysis and explainers from the Bioinformatics desk.
science
A bioRxiv preprint describes BEACON, a method that learns background count patterns to assign single cells to their samples in multiplexed sequencing. The authors report improved classification across human datasets and applications in cancer-cell experiments and protein–transcriptome profiling.
science
A bioRxiv preprint describes MCseg, a no-code tool whose segmentation workflow was selected through AI-agent-guided searches. In a lung adenocarcinoma dataset, its fixed-parameter workflow scored higher on panoptic quality than an Optuna-tuned Cellpose baseline.
science
A bioRxiv preprint introduces PLABench, a benchmark comparing nine deep-learning methods for predicting protein–ligand binding affinity. Its authors report that strong overall accuracy did not translate into consistent performance across targets or protein families.
science
A bioRxiv preprint presents Sma3s v3, a scalable workflow for assigning functions to proteins in proteomes, pangenomes and metagenomes. In tests on a Vibrio cholerae pangenome and a metagenomic catalogue, it reported higher annotation coverage than the cited comparator tools.
science
A bioRxiv preprint presents Rsearch, an R package that integrates VSEARCH with visualisation, parameter optimisation and conversion tools for metabarcoding analysis. In a mock-community comparison with DADA2, the two pipelines produced abundance profiles that closely matched the expected composition, with differences in OTU counts and computational performance.
science
A bioRxiv preprint reports that incomplete reference profiles can leave bulk RNA deconvolution targets non-identifiable even when an algorithm returns precise estimates. The authors propose measuring coverage and false-certification risk, and estimate the measurement precision needed for a decisive result in peripheral blood mononuclear cells.
science
A bioRxiv preprint presents POME, a self-supervised model that represents incomplete mixed-type biomedical data as a graph. The authors report improved missing-data imputation and useful sample representations across three biomedical datasets.
science
A bioRxiv preprint finds that randomly dividing single-cell data can leak information between training and test sets when related cells share labels. The authors propose eakcheck, a tool that estimates this leakage before a predictive model is trained.