Biology subjects

Wittler, R.

Publications and source records attributed to Wittler, R..

6 recordsLinked to original sources

bioRxiv2025

SANS ambages: phylogenomics with abundance-filter, multi-threading, and bootstrapping on amino-acid or genomic sequences

BackgroundThe increasing amount of available genome sequence data enables large-scale comparative studies. A common task is the inference of phylogenies -- a challenging task if close reference sequences are not available, genome sequences are incompletely assembled, or the high number of genomes precludes multiple sequence alignment in reasonable time. SANS is an alignment-free, whole-genome based approach for phylogeny estimation. ResultsHere we present a new implementation SANS ambages with a significantly increased application spectrum. It offers additional types of input data, parallelized processing, and bootstrapping. The source code (C++), documentation, and example data are freely available for download at: https://github.com/gi-bielefeld/sans. SANS can also be launched via the webinterface of the CloWM platform--free of charge, with a standard Life Science account: https://clowm.bi.denbi.de. ConclusionsThe new version not only shortens processing time on large datasets immensely by parallelization. Being able to also process amino acid sequences and offering a filter for low-abundant DNA read segments also enables new application cases. Bootstrapping and integrated visualization ease and enrich the interpretation of the resulting phylogenies.

bioinformatics↗

bioRxiv2024

Applying rearrangement distances to enable plasmid epidemiology with pling

Plasmids are a key vector of antibiotic resistance, but the current bioinformatics toolkit is not well suited to tracking them. The rapid structural changes seen in plasmid genomes present considerable challenges to evolutionary and epidemiological analysis. Typical approaches are either low resolution (replicon typing) or use shared k-mer content to define a genetic distance. However this distance can both overestimate plasmid relatedness by ignoring rearrangements, and underestimate by over-penalising gene gain/loss. Therefore a model is needed which captures the key components of how plasmid genomes evolve structurally - through gene/block gain or loss, and rearrangement. A secondary requirement is to prevent promiscuous transposable elements (TEs) leading to over-clustering of unrelated plasmids. We choose the "Double Cut and Join Indel" model, in which plasmids are studied at a coarse level, as a sequence of signed integers (representing genes or aligned blocks), and the distance between two plasmids is the minimum number of rearrangement events or indels needed to transform one into the other. We show how this gives much more meaningful distances between plasmids. We introduce a software workflow pling (https://github.com/iqbal-lab-org/pling), which uses the DCJ-Indel model, to calculate distances between plasmids and then cluster them. In our approach, we combine containment distances and DCJ-Indel distances to build a TE-aware plasmid network. We demonstrate superior performance and interpretability to other plasmid clustering tools on the "Russian Doll" dataset and a hospital transmission dataset. Impact statementStudying plasmid transmission is a necessary component of understanding antibiotic resistance spread, but identifying recently related plasmids is difficult and often requires manual curation. Pling simplifies this by leveraging a combination of containment distances and rearrangement distances to cluster plasmids. The outcome are clusters of recently related plasmids with a clear backbone and relatively large core genomes, in contrast to other tools which sometimes overcluster. Additionally the network constructed by pling provides a framework with which to spot evolutionary events, such as potential fusions of plasmids and spread of transposable elements. Data summarySupplementary information and figures are available as an additional PDF. The tool presented in this paper is available under https://github.com/iqbal-lab-org/pling. Additional computational analysis and scripts are described and provided under https://github.com/babayagaofficial/pling_paper_analyses. The sequence data used can be found under BioProject no. PRJNA246471 in the National Center for Biotechnology Information for the "Russian doll" dataset (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA246471), and under Project no. PRJEB31034 in European Nucleotide Archive for the "Addenbrookes" dataset (https://www.ebi.ac.uk/ena/browser/view/PRJEB30134). All other genome sequences used were sourced from PLSDB (https://ccb-microbe.cs.uni-saarland.de/plsdb/), and lists of accession numbers can be found in the additional analysis github.

bioinformatics↗

bioRxiv2023

A general minimal perfect hash function for canonical k-mers on arbitrary alphabets with an application to DNA sequences

To index or compare sequences efficiently, often k-mers, i.e., substrings of fixed length k, are used. For efficient indexing or storage, k-mers are often encoded as integers, e.g., applying some bijective mapping between all possible{sigma} k k-mers and the interval [0,{sigma} k -1], where{sigma} is the alphabet size. In many applications, e.g., when the reading direction of a DNA-sequence is ambiguous, canonical k-mers are considered, i.e., the lexicographically smaller of a given k-mer and its reverse (or reverse complement) is chosen as a representative. In naive encodings, canonical k-mers are not evenly distributed within the interval [0,{sigma} k -1]. We present a minimal encoding of canonical k-mers on alphabets of arbitrary size, i.e., a mapping to the interval [0,{sigma} k/2-1]. The approach is introduced for canonicalization under reversal and extended to canonicalization under reverse complementation. We further present a space and time efficient bit-based implementation for the DNA alphabet.

bioinformatics↗

bioRxiv2022

Revisiting pangenome openness with k-mers

Pangenomics is the study of related genomes collectively, usually from the same species or closely related taxa. Originally, pangenomes were defined for bacterial species. After the concept was extended to eukaryotic genomes, two definitions of pangenome evolved in parallel: the gene-based approach, which defines the pangenome as the union of all genes, and the sequence-based approach, which defines the pangenome as the set of all nonredundant genomic sequences. Estimating the total size of the pangenome for a given species has been subject of study since the very first mention of pangenomes. Traditionally, this is performed by predicting the ratio at which new genes are discovered, referred to as the openness of the species. Here, we abstract each genome as a set of items, which is entirely agnostic of the two approaches (gene-based, sequence-based). Genes are a viable option for items, but also other possibilities are feasible, e.g., genome sequence substrings of fixed length k (k-mers). In the present study, we investigate the use of k-mers to estimate the openness as an alternative to genes, and compare the results. An efficient implementation is also provided.

bioinformatics↗

bioRxiv2021

SANS serif: alignment-free, whole-genome based phylogenetic reconstruction

SummarySANS serif is a novel software for alignment-free, whole-genome based phylogeny estimation that follows a pangenomic approach to efficiently calculate a set of splits in a phylogenetic tree or network. Availability and ImplementationImplemented in C++ and supported on Linux, MacOS, and Windows. The source code is freely available for download at https://gitlab.ub.uni-bielefeld.de/gi/sans. Contactandreas.rempel@uni-bielefeld.de

bioinformatics↗

bioRxiv2020

Detecting High Scoring Local Alignments in Pangenome Graphs

MotivationIncreasing amounts of individual genomes sequenced per species motivate the usage of pangenomic approaches. Pangenomes may be represented as graphical structures, e.g. compacted colored de Bruijn graphs, which offer a low memory usage and facilitate reference-free sequence comparisons. While sequence-to-graph mapping to graphical pangenomes has been studied for some time, no local alignment search tool in the vein of BLAST has been proposed yet. ResultsWe present a new heuristic method to find maximum scoring local alignments of a DNA query sequence to a pangenome represented as a compacted colored de Bruijn graph. Our approach additionally allows a comparison of similarity among sequences within the pangenome. We show that local alignment scores follow an exponential-tail distribution similar to BLAST scores, and we discuss how to estimate its parameters to separate local alignments representing sequence homology from spurious findings. An implementation of our method is presented, and its performance and usability are shown. Our approach scales sublinearly in running time and memory usage with respect to the number of genomes under consideration. This is an advantage over classical methods that do not make use of sequence similarity within the pangenome.

bioinformatics↗