Werner Pieter Veldsman

SILO improves detection of disease susceptible variants in short IBD segments, outperforming existing models in simulated and real data.

The paper introduces a bacteria-centric AMP predictor that identifies more bacterial antimicrobial peptides from real genomes than fake ones, outperforming existing predictors.

The paper assesses how genome topology and viral selection affect biological interpretation in the human gut virome, highlighting the need for standardized reporting of circular-to-linear genome ratios and dereplication thresholds.

LRTK is a platform-agnostic toolkit for linked-read analysis of human genomes and metagenomes, offering simulation, alignment, variant calling, and taxonomic classification.

IGSimpute improves scRNA-seq data imputation with high accuracy and interpretability, outperforming existing methods on multiple tissues.

This paper benchmarks 15 de novo metagenome assembly tools on 32 datasets from various sequencing technologies, finding that long-read assemblers produce the highest contig continuity but miss some MAGs, while hybrid assemblers offer a balance of continuity and MAG completeness.

This study compares statistical and machine learning methods for predicting complex diseases using genomic variants, finding statistical methods generally outperform machine learning and ensemble models like Super Learner perform best.

Taxonbridge merges GBIF and NCBI taxonomies to create consistent, deduplicated custom taxonomies for extant and extinct species.

The paper uses GRID-seq data and deep learning to improve transcription factor binding motif prediction by detecting anomalies in aggregated scores.