Abstract Motivation Campylobacter jejuni is a major foodborne zoonotic pathogen associated with antimicrobial resistance (AMR), particularly in low- and middle-income countries with limited genomic surveillance. We analysed 225 publicly available C.jejuni genomes from Ethiopia, Kenya, and Tanzania using a harmonized whole-genome sequencing workflow within a One Health framework. Analyses included genome assembly, annotation, AMR and virulence gene screening, plasmid replicon and mobile genetic element detection, multilocus sequence typing (MLST), phylogenetic reconstruction, principal component analysis (PCA), and minimum spanning tree (MST) analysis. Results Using thresholds of ≥ 90% nucleotide identity and ≥60% gene coverage, blaOXA-193 was detected in 66.67% (150/225) of genomes, tet(O) in 18.22% (41/225), tet(O/32/O) in 4.00% (9/225), and blaOXA-61 in 1.33% (3/225). The gyrA T86I substitution occurred in 24.9% (56/225) of isolates. The applied screening approach detected no acquired macrolide resistance determinants. Virulence-associated gene profiles were broadly conserved, with variation in selected genes. Three known plasmid replicon types and prophage-associated intA hits were identified. Phylogenomic analysis revealed 65 sequence types and substantial genetic overlap between poultry- and human-associated isolates, indicating relatedness but not proving direct transmission. These findings highlight the importance of integrated One Health genomic surveillance for monitoring AMR and genomic diversity of C. jejuni in East Africa. Availability and implementation data underlying this article will be shared on reasonable request to the corresponding author
Abstract Motivation High-throughput biomedical data often contain missing values, which, if mismanaged, can distort downstream analysis. While various imputation methods exist, few account for systematic bias from batch effects. Moreover, many batch effect correction algorithms require complete data, making imputation a prerequisite. This sequence inadvertently creates confounding issues, as batch effects can hinder imputation performance. Despite this, standard imputation evaluation practices rarely consider imputation-batch interactions. Results To address this, we propose BRAID (Batch-effect Reconciliation and Anomaly-aware Imputation for Data), an evaluation framework that systematically assesses imputation techniques under assumed batch-free and batch-affected conditions, using ComBat as the primary correction algorithm and limma to assess generalization. Via controlled batch and missing value simulations applied to diverse real-world datasets, we find that many popular imputation methods exhibit non-negligible performance degradation or instability when batch effects are present, and that by incrementing batch effect intensities, some degrade with greater sensitivity than others. A case study involving K-Nearest Neighbours applied to samples further highlights how parameter choice can induce batch bias, underscoring the need for batch-conscious imputation. BRAID equips researchers with an evaluation framework to assess and compare imputation methods under controlled or simulated conditions of interest, informing reliability and reproducibility. Availability and implementation BRAID is available at https://github.com/HarvardHui/BRAID.
Abstract Resistance to reserve antibiotics such as carbapenems, glycopeptides, and oxazolidinones is the most consequential and least measured part of antimicrobial resistance. We asked whether resistance to these last-line agents can be predicted from the routine susceptibility panel that laboratories already produce, whether the predictive signal reflects shared resistance mechanisms, and whether it generalises across species. Using per-isolate susceptibility data for seven high-priority bacterial pathogens from a public bacterial bioinformatics resource (about 24,000 isolates), we built gradient-boosted models that predict resistance to each antibiotic from the rest of the panel, with every agent annotated for drug class and mechanism from a curated resistance ontology. Of 220 candidate pathogen-antibiotic pairs, 73 met a pre-specified eligibility rule and were evaluated by five-fold cross-validation. Resistance was broadly predictable (mean area under the curve 0.80 across the 73 models; 0.82 for reserve agents), reaching 0.90 for carbapenem resistance in Klebsiella pneumoniae and 0.97 for glycopeptide and oxazolidinone resistance in Enterococcus faecium. Performance was largely retained after same-class predictors were removed, indicating genuine cross-class co-resistance, and carbapenem resistance transferred across Gram-negative species. Routine panels therefore carry a strong, mechanism-aware signal for reserve-drug resistance.
Abstract Motivation The ability to accurately predict TCR-pMHC binding is crucial for the development of cancer immunotherapies, diagnostics, and new vaccines. Tetramer-based single-cell TCR sequencing techniques have provided information on TCR clonotypes and their cognate peptide antigens, which have been catalogued in databases such as VDJdb, IEDB, and McPAS-TCR. Recent advances in AI/ML techniques have opened up the opportunities for development of machine learning based methods for accurate predictions of TCR-pMHC binding. Results We have developed TERP (TCR-Epitope Recognition Prediction) based on a systematic analysis using different types of embeddings for CDR3β sequences of TCRs and putative peptide antigens, and a variety of different machine learning methods like CNN, ANN, random forest, XGBoost, logistic regression and naïve Bayes etc. Extensive benchmarking studies using different types of negative datasets (naïve TCRs and random non-cognate pairing) and different splits for test datasets revealed that the Random Forest algorithm and BLOSUM50 features have best predictive performance. TERP had superior performance compared to other published methods for TCR-Split (80:20 random split) and Strict-Split (less frequent peptide-TCR pairs in the test set) settings in both naïve and cross-combination negative sampling. Further, TERP demonstrated generalizability in predicting unseen TCR-peptide pairs, whereas other models failed to do so. TERP has also been implemented as a user-friendly web portal, accessible at http://www.nii.ac.in/TERP. The portal offers various functionalities, including TCR-peptide binding prediction, screening of TCRs for a given epitope, and prediction of epitopes for TCR clonotypes using CDR3β sequences or results from TCRSeq in 10x or immunoseq format. Availability and implementation Freely available online at http://www.nii.ac.in/TERP. The website is developed with the Django Python framework and supports all major browsers.
Abstract Motivation Long-read sequencing has made full-length small-subunit ribosomal RNA metabarcoding routine, but broadly conserved primers also amplify organellar and cross-domain sequences, which distort profiles when sent to a target-domain classifier. Results SSUplex detects small-subunit ribosomal RNA on both strands with conserved-region profile hidden Markov models, assigns each read to one of five origins (bacteria, archaea, eukaryota, mitochondria, chloroplast) and extracts the gene region. It reimplements Metaxa2’s extraction step but ranks origins by summed rather than mean per-region score. On 5,000 ZymoBIOMICS Nanopore reads, this raised accuracy from 54.9% for Metaxa2’s extraction step to 95.9%, against 99.96% for Metaxa2’s full pipeline with similarity-search classification. On reads simulated from sources outside the Metaxa2 database, SSUplex was 93 to 98% accurate for bacteria-dominated and plant-root-like compositions (Metaxa2’s full pipeline: 96 to 99%), with mitochondria the weakest origin. In a rice-root 16S sample, about 40% of reads were organellar. SSUplex ran 3.2× faster than Metaxa2’s extraction step from 200,000 reads upward, with about 45% less peak memory, and 65 to 134× faster than Metaxa2’s full pipeline. Availability and implementation SSUplex is implemented in Rust and freely available at https://github.com/ayobi/ssuplex and from Bioconda; version 0.2.0 is archived at https://doi.org/10.5281/zenodo.23246210.
