Biology subjects

Ulrich, J.-U.

Publications and source records attributed to Ulrich, J.-U..

3 recordsLinked to original sources

bioRxiv2023

Taxor: Fast and space-efficient taxonomic classification of long reads with hierarchicalinterleaved XOR filters

Metagenomic long-read sequencing is gaining popularity for various applications, including pathogen detection and microbiome studies. To analyze the large data created in those studies, software tools need to taxonomically classify the sequenced molecules and estimate the relative abundances of organisms in the sequenced sample. Due to the exponential growth of reference genome databases, the current taxonomic classification methods have large computational requirements. This issue motivated us to develop a new data structure for fast and memoryefficient querying of long reads. Here we present Taxor as a new tool for long-read metagenomic classification using a hierarchical interleaved XOR filter data structure for indexing and querying large reference genome sets. Taxor implements several k-mer-based approaches such as syncmers for pseudoalignment to classify reads and an Expectation-Maximization algorithm for metagenomic profiling. Our results show that Taxor outperforms competing shortand long-read tools regarding precision, while having a similar recall. Most notably, Taxor reduces the memory requirements and index size by more than 50% and is among the fastest tools regarding query times. This enables real-time metagenomics analysis with large reference databases on a small laptop in the field. Taxor is available at https://gitlab.com/dacs-hpi/taxor.

bioinformatics↗

bioRxiv2022

In-silico enrichment of bacterial plasmids by chromosomal depletion using nanopore adaptive sampling

Bacterial plasmids play a major role in the spread of antibiotic resistance genes. However, their characterization via DNA sequencing suffers from the low abundance of plasmid DNA in those samples. Although sample preparation methods can enrich the proportion of plasmid DNA before sequencing, these methods are expensive and laborious, and they might introduce a bias by enriching only for specific plasmid DNA sequences. Nanopore adaptive sampling could overcome these issues by rejecting uninteresting DNA molecules during the sequencing process. In this study, we assess the application of adaptive sampling for the enrichment of low-abundant plasmids in known bacterial isolates using two different adaptive sampling tools. We show that a significant enrichment can be achieved even on expired flow cells. By applying adaptive sampling, we also improve the quality of de novo plasmid assemblies and reduce the sequencing time. However, our experiments also highlight issues with adaptive sampling if target and non-target sequences span similar regions.

genomics↗

bioRxiv2022

ReadBouncer: Precise and Scalable Adaptive Sampling for Nanopore Sequencing

Nanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast GPU base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications. Here we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters (IBF). Read-Bouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without graphical processing units (GPUs), making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background. AvailabilityThe C++ source code of ReadBouncer is available at (https://gitlab.com/dacs-hpi/readbouncer).

bioinformatics↗