Biology subjects

Reiter, T. E.

Publications and source records attributed to Reiter, T. E..

4 recordsLinked to original sources

bioRxiv2022

Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn's disease

Microbial strains have closely related genomes but may have different phenotypes in the same environment. Shotgun metagenomic sequencing can capture the genomes of all strains present in a community but strain-resolved analysis from shotgun sequencing alone remains difficult. We developed an approach to identify and interrogate strain-level differences in groups of metagenomes. We use this approach to perform a meta-analysis of stool microbiomes from individuals with and without inflammatory bowel disease (IBD; Crohns disease, ulcerative colitis; n = 605), a disease for which there are not specific microbial biomarkers but some evidence that microbial strain variation may stratify by disease state. We first developed a machine learning classifier based on compressed representations of complete metagenomes (FracMinHash sketches) and identified genomes that correlate with IBD subtype. To rescue variation that may not have been present in the genomes, we then used assembly graph genome queries to recover strain variation for correlated genomes. Lastly, we developed a novel differential abundance framework that works directly on the assembly graph to uncover all sequence variants correlated with IBD. We refer to this approach as dominating set differential abundance analysis and have implemented it in the spacegraphcats software package. Using this approach, we identified five bacterial strains that are associated with Crohns disease. Our method captures variation within the entire sequencing data set, allowing for discovery of previously hidden disease associations.

bioinformatics↗

bioRxiv2022

Protein k-mers enable assembly-free microbial metapangenomics

An estimated 2 billion species of microbes exist on Earth with orders of magnitude more strains. Microbial pangenomes are created by aggregating all genomes of a single clade and reflect the metabolic diversity of groups of organisms. As de novo metagenome analysis techniques have matured and reference genome databases have expanded, metapangenome analysis has risen in popularity as a tool to organize the functional potential of organisms in relation to the environment from which those organisms were sampled. However, the reliance on assembly and binning or on reference databases often leaves substantial portions of metagenomes unanalyzed, thereby underestimating the functional potential of a community. To address this challenge, we present a method for metapangenomics that relies on amino acid k-mers (kaa-mers) and metagenome assembly graph queries. To enable this method, we first show that kaa-mers estimate pangenome characteristics and that open reading frames can be accurately predicted from short shotgun sequencing reads using the previously developed tool orpheum. These techniques enable pangenomics to be performed directly on short sequencing reads. To enable metapangenome analysis, we combine these approaches with compact de Bruijn assembly graph queries to directly generate sets of sequencing reads for a specific species from a metagenome. When applied to stool metagenomes from an individual receiving antibiotics over time, we show that these approaches identify strain fluctuations that coincide with antibiotic exposure.

bioinformatics↗

bioRxiv2022

Compendium-wide analysis of P. aeruginosa core and accessory genes reveal more nuanced transcriptional patterns

Strains of Pseudomonas aeruginosa, an opportunistic pathogen that causes difficult to treat infections, have significant genomic heterogeneity including the presence of diverse accessory genes that are only present in some strains or clades. Both core genes, which are conserved across strains, and accessory genes have been associated with traits such as biofilm formation and virulence. Much of what we know about core and accessory gene content comes from genome analyses. Here, we use a newly assembled transcriptome compendium to analyze the transcriptional patterns of core and accessory gene expression in PAO1 and PA14 strains across thousands of samples from hundreds of distinct experiments. We found that a subset of core genes were stable, having consistent correlated expression patterns across samples regardless of strain background, with a focus on strains PAO1 and PA14. These stable core genes had fewer co-expressed neighbors that were accessory genes. SignificancePseudomonas aeruginosa is a ubiquitous pathogen. There is a lot of diversity amongst P. aeruginosa strains, some which are clinically relevant. Understanding how these different strain-level traits manifest is important for identifying targets that regulate different traits of interest. With the availability of a PAO1-mapped and PA14-mapped RNA-seq compendium, which contain hundreds of strains, it is now possible to examine the effect of different strains on expression, which can mediate different traits. In this study we developed an approach to compare expression profiles across different P. aeruginosa gene groups - core and accessory genes. This approach revealed a subset of core genes with different transcriptional patterns across strains, which could contribute to trait differences.

bioinformatics↗

bioRxiv2022

Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers

The identification of reference genomes and taxonomic labels from metagenome data underlies many microbiome studies. Here we describe two algorithms for compositional analysis of metagenome sequencing data. We first investigate the FracMinHash sketching technique, a derivative of modulo hash that supports Jaccard containment estimation between sets of different sizes. We implement FracMinHash in the sourmash software, evaluate its accuracy, and demonstrate large-scale containment searches of metagenomes using 700,000 microbial reference genomes. We next frame shotgun metagenome compositional analysis as the problem of finding a minimum collection of reference genomes that "cover" the known k-mers in a metagenome, a minimum set cover problem. We implement a greedy approximate solution using FracMinHash sketches, and evaluate its accuracy for taxonomic assignment using a CAMI community benchmark. Finally, we show that the minimum metagenome cover can be used to guide the selection of reference genomes for read mapping. sourmash is available as open source software under the BSD 3-Clause license at github.com/dib-lab/sourmash/.

bioinformatics↗