Alice Carolyn McHardy

BGC-QUAST evaluates and compares BGC predictions across samples, assemblies, and tools to facilitate biosynthetic potential analysis.

SeqDesk is a sequencing-facility management system that ensures FAIR-compliant metadata submission by integrating standards-compliant metadata capture into routine operations.

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

The paper shows that E. faecalis evolves to metabolize fructoselysine, reducing S. Typhimurium colonization by limiting a key nutrient.

Metax improves cross-domain taxonomic profiling by using coverage-informed probabilistic modeling, showing accuracy across bacteria, archaea, eukaryotes, and viruses in diverse samples.

This study evaluates LLMs for microbial phenotype annotation, finding varied performance across models and traits, with confidence scores correlating with accuracy.

CoVerage predicts and characterizes SARS-CoV-2 Variants of Interest by analyzing lineage frequency dynamics and antigenic changes from GISAID data.

This study uses long-read metagenomics to detect Paracoccus yeei in SBP patients, revealing its pathogenicity and antimicrobial resistance profile.

This study benchmarks ML and rule-based methods for predicting antimicrobial resistance from microbial genomes, finding significant variation in performance across species and antibiotics.

GO Bench provides a benchmarking hub for evaluating machine learning models in protein function annotation using Gene Ontology terms.

Scelestial infers single-cell lineage trees using a Steiner tree approximation algorithm, outperforming existing methods in accuracy and speed on simulated and real tumor data.

ProSolo improves variant calling accuracy in single-cell DNA sequencing by addressing amplification bias and errors through a probabilistic model and joint modeling with bulk data.

TripletProt improves protein functional annotation using Siamese networks, achieving better F1 scores than existing methods in sub-cellular localization and gene ontology prediction.

Haploflow assembles viral strain genomes from mixed samples, outperforming existing assemblers in speed and accuracy on benchmark datasets.

EpitopeVec improves linear epitope prediction using deep protein sequence embeddings, achieving better accuracy and AUC than existing methods.

This tutorial explains how to use the CAMI benchmarking toolkit to evaluate metagenomics software performance using standardized datasets and metrics.

This study benchmarks assemblers and variant callers for strain-resolved analysis of large DNA viruses, identifying best practices and challenges in handling low-divergent viral populations.

The paper uses air transportation data and the Sankoff algorithm to reconstruct the phylogeographic spread of the 2009 H1N1 influenza A pandemic, showing improved accuracy over geographic and Bayesian methods.

CAMITAX improves taxonomic labeling of microbial genomes using an ensemble method combining multiple approaches, outperforming individual methods across taxonomic ranks.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

This study develops a deep learning model for protein secondary structure prediction using primary sequences, achieving high accuracy on a challenging dataset.

The paper compares genome-wide adaptation of H3N2 and pH1N1 influenza A viruses, identifying regions under positive selection and linking them to host adaptation and drug resistance.

This paper introduces PPE segmentation for protein sequences, enabling discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX) with improved performance in motif detection and protein classification.

OPAL is a software package for benchmarking taxonomic metagenome profilers using performance metrics and visualizations, demonstrating its application on CAMI challenge datasets.

CAMISIM simulates microbial communities and metagenomes with truth standards, enabling benchmarking of metagenomic analysis tools.

This paper introduces DiTaxa, a method for analyzing 16S rRNA sequences using nucleotide-pair encoding to improve microbiome phenotype classification and biomarker detection.

AMBER evaluates genome binning tools using benchmark datasets and provides performance metrics and visualizations.

MicroPheno predicts environments and host phenotypes from 16S rRNA gene sequencing using k-mer features from shallow sub-samples, outperforming OTU-based methods in accuracy and efficiency.

The paper introduces SD plots to identify selective sweeps in influenza A virus genomes, showing their use in tracking adaptive changes in pH1N1 and sH3N2 viruses.

The paper benchmarks computational metagenomics software using complex datasets, showing strengths and limitations in assembly, binning, and taxonomic profiling.