Taylor E. Reiter

The paper identifies five bacterial strains associated with Crohn's disease using a novel method combining machine learning and assembly graphs for metagenomic analysis.

This paper introduces a method using protein k-mers to perform metapangenomics without assembly, identifying strain fluctuations in stool metagenomes during antibiotic treatment.

The paper analyzes transcriptional patterns of core and accessory genes in P. aeruginosa strains using a transcriptome compendium, revealing stable core gene expression patterns across strains.

Greedy minimum set cover over FracMinHash k-mer sketches selects a small set of reference genomes explaining a metagenome, supporting species-level taxonomic profiling and choice of genomes for read mapping.

The paper provides strategies for using workflow systems to streamline biological data analysis, emphasizing reproducibility and scalability in high-throughput sequencing projects.

The paper introduces a system to recover missing genomic content from metagenomic data by analyzing subgraphs in assembly graphs.

The paper introduces sourmash, a software package that uses MinHash to create compressed sequence signatures for efficient large-scale sequence comparison and database searching.

The paper identifies microbial contamination in the domesticated olive genome, primarily from Aureobasidium pullulans, and notes its potential impact on downstream analyses.