Thomas Abeel

This study evaluates STUNT, a meta-learning framework for microbiome-based disease classification, finding that its benefits are limited under data scarcity and diminish with more samples.

This study uses in-silico DBTL cycles to evaluate how process parameters affect strain optimization outcomes in metabolic engineering.

The paper predicts other meta-omics data from metagenomics using machine learning, showing reliable predictions for transcript and metabolite abundances but challenges in protein profiles, and demonstrates utility for inflammatory bowel disease classification.

This study benchmarks plasmid detection and reconstruction tools on short-read assemblies from diverse bacterial species, finding performance varies with database coverage and assembly quality.

This paper applies a machine learning-assisted recommendation strategy with exploration-exploitation balance to optimize p-coumaric acid production in yeast, achieving a 137% increase over parent strains.

This study uses long-term multi-meta-omics to identify the ecophysiological controls of seasonal N2O emissions in wastewater treatment systems.

This study compares the fecal microbiome of pigs with and without antibiotic exposure to assess changes in microbial communities and antibiotic resistance genes.

POASTA is an optimal algorithm for partial order alignment that improves speed and memory efficiency, enabling larger alignments in bacterial gene datasets.

The paper introduces a framework for training metabolic kinetic models using neural ODEs, demonstrating efficient parameterization on SBML models and glycolysis data.

This study evaluates portable in-field DNA sequencing for pathogen and antimicrobial resistance detection, finding it feasible and comparable to traditional lab methods.

This study analyzes antibiotic resistance patterns in soil metagenomes across different types and locations, identifying key resistance genes and their environmental influences.

SAP improves bacterial gene function prediction using protein embeddings and conserved synteny, identifying novel toxins in enterococci.

This study evaluates the impact of removing repeat-induced overlaps on de novo genome assembly performance using simulated datasets.

The paper benchmarks long-read de novo assemblers for eukaryotic genomes, finding that Flye performs best overall, and longer reads can improve assembly quality depending on genome complexity.

HAT reconstructs haplotypes using short and long reads with a reference genome, outperforming existing tools on yeast and simulated polyploid data.

StrainGE identifies low-abundance microbial strains from metagenomic data with higher sensitivity and resolution than existing tools.

QuantTB classifies mixed Mycobacterium tuberculosis infections in whole genome sequencing data, accurately quantifying strains and detecting resistance mutations.

The paper uses nanopore sequencing and a new method to show lager-brewing yeasts originated from a single hybridization event, with Group 1 and 2 strains diverging after this event.

Alpaca identifies mosaic structures in microbial genomes by analyzing kmer similarity across sequencing datasets to map evolutionary origins.

This study evolves a Saccharomyces cerevisiae x S. eubayanus hybrid under brewing conditions, showing phenotypic diversity and limited aneuploidy, with specific mutations linked to industrial traits.

The paper analyzes RNA-seq data from Schizophyllum commune to show that spontaneous mutations accumulate during strain preservation, with a mutation rate of 1.923×10^-8 per haploid genome per base per generation.

This paper presents a near-complete genome assembly of Saccharomyces cerevisiae strain CEN.PK113-7D using Nanopore sequencing, revealing new genes and a chromosomal translocation.