Biology subjects

Krannich, T.

Publications and source records attributed to Krannich, T..

6 recordsLinked to original sources

bioRxiv2025

Anyone can be the best: Impact of diverse methodologies on the evaluation of structural variant callers

Structural variants (SVs) are medium and large-scale genomic alterations that shape phenotypic diversity and disease risk. Numerous methods have been proposed for discovering SVs, however their benchmarking has been inconsistent across studies, often resulting in contradictory findings. One of the main sources of conflicting evaluation re-sults is the lack of consistency in the SV callsets used as ground truth, ranging from curated callsets released by consortia to more recent approaches that construct callsets from high-quality telomere-to-telomere de novo haplotype assemblies. The discrepancies between benchmarks are further compounded by the choice of the reference genome (GRCh37, GRCh38, and T2T-CHM13), where using T2T-CHM13reveals a different deletion/insertion profile, indicating reduced reference bias. We evaluated the performance of several state-of-the-art SV discovery methods from long-read whole-genome sequencing data and observed substantial variation in their performance and rankings, depending on the choice of ground truth, reference genome, and genomic regions used for evaluation. Counter-intuitively, the more complete reference genome T2T-CHM13does not inherently solve the problem of SV benchmarking; instead it reveals the limitations of each detection method in complex genomic regions. The substantial variation in detection accuracy across different genomic regions calls for additional caution in downstream analyses and in drawing conclusions based on predicted SVs. These findings underscore the complexity of evaluating SV detection methods and highlight the need for careful consideration and, ideally, field-standard best practices when reporting performance metrics.

bioinformatics↗

bioRxiv2025

Pangenome graph augmentation from unassembled long reads

Pangenomes are becoming increasingly popular data structures for genomics analyses due to their ability to compactly represent the genetic diversity within populations. Constructing a pangenome graph, however, is still a time-consuming and expensive process. A promising approach for pangenome construction consists of progressively augmenting a pangenome graph with additional high-quality assemblies. Currently, there is no method for augmenting a pangenome graph with unassembled reads from newly sequenced samples without first aligning the reads to a reference genome and performing variant calling and genotyping on the new individuals. In this work, we present the first assembly-free and mapping-free approach for augmenting an existing pangenome graph using unassembled long reads from an individual not already present in the pangenome. Our approach consists of finding sample specific sequences in reads using efficient indexes, clustering reads corresponding to the same novel variant(s), and then building a consensus sequence to be added to the pangenome graph for each variant separately. Using simulated reads based on Human Pangenome Reference Consortium (HPRC) assemblies, we demonstrate the effectiveness of the proposed approach for progressively augmenting the pangenome with long reads, without the need for de novo assembly or predicting genetic variants of the new sample. The software is freely available at https://github.com/ldenti/palss.

bioinformatics↗

bioRxiv2024

CIEVaD: a lightweight workflow collection for rapid and on demand deployment of end-to-end testing of genomic variant detection

The identification of genomic variants has become a routine task in the thriving age of genome sequencing. Particularly small genomic variants of single or few nucleotides are routinely investigated for their impact on an organisms phenotype. Hence, precise and robust detection of the variants exact genomic location and change in nucleotide composition is vital in many biological applications. Although a plethora of methods exist for the many key steps of variant detection, thoroughly testing the detection process and evaluating its results is still a cumbersome procedure. In this work, we present a collection of trivial to apply and highly modifiable workflows to facilitate the generation of synthetic test data as well as to evaluate the accordance of a user-provided set of variants with the test data. AvailabilityThe workflows are implemented in Nextflow and are freely available and open-source at https://github.com/rki-mf1/cievad under the GPL-3.0 license.

bioinformatics↗

bioRxiv2024

varVAMP: automated pan-specific primer design for tiled full genome sequencing and qPCR of highly diverse viral pathogens.

Time- and cost-saving surveillance of viral pathogens is achieved by tiled sequencing in which a viral genome is amplified in overlapping PCR amplicons and qPCR. However, designing pan-specific primers for viral pathogens that have high genomic variability represents a major challenge. Here, we present a bioinformatics command-line tool, called varVAMP (variable virus amplicons). It relies on multiple sequence alignments of highly variable virus sequences and enables automatic pan-specific primer design for qPCR or tiled amplicon whole genome sequencing. The varVAMP software guarantees pan-specificity by two means: it designs primers in regions with minimal variability and introduces degenerate nucleotides into primer sequences to compensate for common sequence variations. We demonstrate varVAMPs utility by designing and evaluating novel pan-specific primer schemes suitable for sequencing the genomes of SARS-CoV-2, Hepatitis E virus, rat Hepatitis E virus, Hepatitis A virus, Borna-disease-virus-1, and Poliovirus. Moreover, we established highly sensitive and specific Poliovirus qPCR assays that could potentially simplify current Poliovirus surveillance. Importantly, wet-lab and bioinformatic techniques established for SARS-CoV-2 tiled amplicon sequencing were readily transferable to these new primer schemes and will allow sequencing laboratories to extend their established methodology to other human pathogens.

bioinformatics↗

bioRxiv2022

Bcmap: fast alignment-free barcode mapping for linked-read sequencing data

MotivationThe bottleneck for genome analysis will soon shift from sequencing cost to computationally expensive read alignment. When only portions of the genome are of interest, read alignment on whole-genome sequencing (WGS) data can be replaced by a fast mapping step. ResultsWe propose an approach that can map both long reads or linked-read molecules, as a pre-processing step for downstream targeted analyses. Our "molecule mapping" approach, implemented in the tool MoleMap, uses a minimized open-addressing k-mer index of the reference genome and a fast k-mer clustering procedure. We demonstrate that MoleMaps accuracy is competitive with standard alignment and mapping tools and its running time outperforms other mapping tools on 32 threads by a factor of 3 to 8 and read alignment tools by a factor of 10 to 60. Its low memory footprint allows us to analyze whole genomes on a standard laptop computer. As proofs of concept, we use MoleMap to filter reads for local assembly of a known variant region that involves non-reference sequence and showcase its use in diagnosing a patient with a rare disease. Our work contributes to more scalable genome analysis and promotes WGS for targeted analyses. Availability and ImplementationSource code of MoleMap is available at https://github.com/kehrlab/molemap.

bioinformatics↗

bioRxiv2021

Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs

MotivationWith the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across tens of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared to other types of SVs due to the computational complexity of detecting them. When using short-read data, the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes. ResultsWe introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the PopIns2 workflow and highlight our novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets. AvailabilityThe source code of PopIns2 is available from https://github.com/kehrlab/PopIns2. Contactthomas.krannich@bihealth.de or birte.kehr@klinik.uni-regensburg.de

bioinformatics↗