arXiv subjects

Lucas Czech

Publications and source records attributed to Lucas Czech.

4 recordsLinked to original sources

arXiv2026

Fast Iteration of Spaced k-mers

Background: Short sequence substrings of a fixed length k, called k-mers, are a ubiquitous computational primitive in bioinformatics, used across sequence indexing, read mapping, genome assembly, metagenomic classification, and comparative genomics. Spaced k-mers generalize this concept by selecting only a subset of positions within a k-mer, improving robustness to mismatches and sequencing errors. While k-mers are computationally highly efficient, spaced k-mers require additional work to be extracted from a sequence, which has slowed down existing methods. Results: We present a collection of efficient algorithms for extracting spaced k-mers from nucleotide sequences, optimized for different hardware architectures. They are based on bit manipulation instructions at CPU level, making them both simpler to implement and up to an order of magnitude faster than existing methods. We further evaluate common pitfalls in k-mer processing, which can cause substantial inefficiencies. Conclusions: Our approaches allow the utilization of spaced k-mers in high-performance bioinformatics applications without major performance degradation compared to regular k-mers, achieving a throughput of up to 750MB of sequence data per second per core. Availability: The implementation in C++20 is published under the MIT license, and freely available at https://github.com/lczech/fisk

q-bio.GN↗

arXiv2023

grenedalf: population genetic statistics for the next generation of pool sequencing

Pool sequencing is an efficient method for capturing genome-wide allele frequencies from multiple individuals, with broad applications such as studying adaptation in Evolve-and-Resequence experiments, monitoring of genetic diversity in wild populations, and genotype-to-phenotype mapping. Here, we present grenedalf, a command line tool written in C++ that implements common population genetic statistics such as $\theta$, Tajima's D, and FST for Pool sequencing. It is orders of magnitude faster than current tools, and is focused on providing usability and scalability, while also offering a plethora of input file formats and convenience options.

q-bio.PE↗

arXiv2022

Metagenomic Analysis using Phylogenetic Placement -- A Review of the First Decade

Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first ten years. In particular, the goals of this review are (i) to motivate the usage of phylogenetic placement and illustrate some of its use cases, (ii) to outline the full workflow, from raw sequences to publishable figures, including best practices, (iii) to introduce the most common tools and methods and their capabilities, (iv) to point out common placement pitfalls and misconceptions,(v) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data.

q-bio.PE↗

arXiv2021

grenepipe: A flexible, scalable, and reproducible pipeline to automate variant and frequency calling from sequence reads

Processing high-throughput DNA sequencing data of individuals or populations requires stringing together independent software tools with many parameters, often leading to non-reproducible pipelines and datasets. We developed grenepipe to streamline this data processing, an all-in-one Snakemake workflow from raw sequencing data to the end product of a table of individuals' genotypes or population frequencies. Our pipeline allows users to select among a range of popular software tools within a single configuration file, automatically downloads and installs software and dependencies, and runs with two command calls: to prepare and to run. It is highly optimized for scalability in cluster environments and parallel computing, splitting data tasks into manageable genomic sections and automatically consolidating the outputs. grenepipe is published under the GPL-3 license, and freely available at https://github.com/moiexpositoalonsolab/grenepipe.

q-bio.GN↗