Biology subjects

Barton, V.

Publications and source records attributed to Barton, V..

3 recordsLinked to original sources

bioRxiv2025

A Multiresolution Hierarchical Approach to Peak Picking for High Resolution Mass Spectrometry Data Analysis

MotivationAccurate peak detection is a critical first step in high-resolution mass spectrometry (HRMS) data analysis. Most existing tools rely on centroiding and grid-based assumptions, which simplify the raw profile data at the cost of information loss and reduced detection accuracy. ResultsWe present MRH, a novel peak detection method that operates directly on raw HRMS data using a hierarchical, multi-resolution decomposition of the (RT, m/z) space. Peaks are identified within resolution-dependent regions of interest using image-processing techniques, and an ambiguity score is introduced to quantify detection confidence. Benchmarking against widely used methods (apLCMS, centWave, and gridmass) shows that MRH consistently achieves higher F1 scores across concentrations, particularly excelling at low concentrations where competing tools fail. MRH also provides accurate localization of peaks and low ambiguity in detections, highlighting both robustness and precision. AvailabilityA proof-of-concept implementation of MRH and the evaluation dataset are freely available at https://github.com/VojtechBarton/MRH.

bioinformatics↗

bioRxiv2025

Evolutionary potential of the Escherichia coli antimutator ΔnudJ is reduced via altered mutational spectrum

The rate of spontaneous mutation is a key factor in determining the capacity of a population to adapt to a novel environment, for example a bacterial population exposed to antibiotics. Genetic and environmental factors controlling the mutation rate commonly also cause shifts in the relative rates of different mutational classes, i.e. the spectrum of spontaneous mutations. When the mutational spectrum is altered, the relatively enriched and depleted mutations may differ in their fitness effects. Here we aim to explore how a reduced mutation rate and altered mutational spectrum can contribute to adaptation in Escherichia coli. We measure mutation rates across a set of Nudix hydrolase deletants, finding multiple strains with an antimutator phenotype. We focus on the antimutator {Delta}nudJ which can cause a 6-fold mutation rate reduction relative to the wildtype with an altered mutational spectrum. Not only does nudJ deletion reduce the probability of antibiotic resistance arising but, in the case of rifampicin, the accessible resistance mutations under this altered mutation spectrum have greater fitness costs. While this effect is, so far, specific to this drug, it opens up the possibility of reducing the likelihood of resistance mutations establishing through simultaneous modification of mutation rate and spectrum. The identification and characterisation of antimutator alleles such as {Delta}nudJ provides potential targets for antievolution drugs which could be applied during antibiotic treatment to inhibit NudJ activity. Such an approach could suppress spontaneous resistance evolution both through fewer resistance mutations and by limiting access to the fittest mutations.

evolutionary biology↗

bioRxiv2021

Using Deep Learning for Gene Detection and Classification in Raw Nanopore Signals

Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals - squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analysed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.

bioinformatics↗