Biology subjects

Vorbrugg, S.

Publications and source records attributed to Vorbrugg, S..

4 recordsLinked to original sources

bioRxiv2024

Gfa2bin enables graph-based GWAS by converting genome graphs to pan-genomic genotypes

Variation graphs offer superior representation of genomic diversity compared to traditional linear reference genomes, capturing complex features that are otherwise inaccessible to analysis. It seems self-evident that integrating these graphs with genome-wide association studies (GWAS) should enable more comprehensive understanding of genetic landscapes, potentially uncovering novel associations between genetic variations and traits. This approach takes full advantage of rich genomic information, thereby providing deeper insights into the genetic base of complex traits. Our tool, gfa2bin, offers multiple methods to (i) genotype variation graphs and (ii) convert the genotypes to well-established data formats for genome-wide association studies (GWAS). We demonstrate that variation graphs are feasible alternatives to traditional linear references for GWAS. Our case study using Arabidopsis thaliana and 1,695 traits shows that our approach complements SNP-based approaches, often identifying additional associations, with all associations having on average higher significance compared to SNP-based approaches. gfa2bin is implemented in Rust. Commented source code is available under MIT license at https://github.com/MoinSebi/gfa2bin. Examples of how to run gfa2bin are provided in the documentation. We added several Python scripts and a Snakemake pipeline for easy processing of our tool using larger data sets. In addition, we recommend using packing (https://github.com/MoinSebi/packing) for reduced storage and preprocessing (normalization) of sequence-to-graph alignments coverage.

bioinformatics↗

bioRxiv2024

Towards an unbiased characterization of genetic polymorphism

Our view of genetic polymorphism is shaped by methods that provide a limited and reference-biased picture. Long-read sequencing technologies, which are starting to provide nearly complete genome sequences for population samples, should solve the problem--except that characterizing and making sense of non-SNP variation is difficult even with perfect sequence data. Here we analyze 27 genomes of Arabidopsis thaliana in an attempt to address these issues, and illustrate what can be learned by analyzing whole-genome polymorphism data in an unbiased manner. Estimated genome sizes range from 135 to 155 Mb, with differences almost entirely due to centromeric and rDNA repeats that are difficult to assemble. The completely assembled chromosome arms comprise roughly 120 Mb in all accessions, but are full of structural variants, largely due to transposable elements. Even with only 27 accessions, a pan-genome coordinate system that includes the resulting variation ends up being [~] 70% larger than the size of any one genome. Our analysis reveals an incompletely annotated mobile-ome: we not only detect several novel TE families, but also find that existing TE annotation is a poor predictor of elements that have recently been active. In contrast to this, the genic portion, or "gene-ome", is highly conserved. By annotating each genome using accession-specific transcriptome data, we find that 13% of all (non-TE) genes are segregating in our 27 accessions, but most of these are transcriptionally silenced. Finally, we show that with short-read data we previously massively underestimated genetic variation of all kinds, including SNPs--mostly in regions where short reads could not be mapped reliably, but also where reads were mapped incorrectly. We demonstrate that SNP-calling errors can be biased by the choice of reference genome, and that RNA-seq and BS-seq results can be strongly affected by mapping reads only to a reference genome rather than to the genome of the assayed individual. In conclusion, while whole-genome polymorphism data pose tremendous analytical challenges, they also have the potential to revolutionize our understanding of genome evolution.

genomics↗

bioRxiv2024

Gretl - Variation GRaph Evaluation TooLkit

Motivation: As genome graphs are powerful data structures for representing the genetic diversity within populations, they can help identify genomic variations that traditional linear references miss, but their complexity and size makes the analysis of genome graphs challenging. We sought to develop a genome graph analysis tool that helps these analyses to become more accessible by addressing the limitations of existing tools. Specifically, we improve scalability and user-friendliness, and we provide many new statistics for graph evaluation. Results: We developed an efficient, comprehensive, and integrated tool, gretl, to analyse genome graphs and gain insights into their structure and composition by providing a wide range of statistics. gretl can be utilised to evaluate different graphs, compare the output of graph construction pipelines with different parameters, as well as perform an in-depth analysis of individual graphs, including sample-specific analysis. With the assistance of gretl, novel patterns of genetic variation and potential regions of interest can be identified, for later, more detailed inspection. We demonstrate that gretl outperforms other tools in terms of speed, particularly for larger genome graphs. Availability and implementation: gretl is implemented in Rust. Commented source code is available under MIT licence at https://github.com/MoinSebi/gretl. Examples of how to run gretl are provided in the documentation. Several Jupyter notebooks are part of the repository and can help visualise gretl results.

bioinformatics↗

bioRxiv2023

Building pangenome graphs

Pangenome graphs can represent all variation between multiple reference genomes, but current approaches to build them exclude complex sequences or are based upon a single reference. In response, we developed the PanGenome Graph Builder (PGGB), a pipeline for constructing pangenome graphs without bias or exclusion. PGGB uses all-to-all alignments to build a variation graph in which we can identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships.

bioinformatics↗