Biology subjects

Hagmann, J.

Publications and source records attributed to Hagmann, J..

6 recordsLinked to original sources

bioRxiv2023

Pangenome graph layout by Path-Guided Stochastic Gradient Descent

MotivationThe increasing availability of complete genomes demands for models to study genomic variability within entire populations. Pangenome graphs capture the full genomic similarity and diversity between multiple genomes. In order to understand them, we need to see them. For visualization, we need a human readable graph layout: A graph embedding in low (e.g. two) dimensional depictions. Due to a pangenome graphs potential excessive size, this is a significant challenge. ResultsIn response, we introduce a novel graph layout algorithm: the Path-Guided Stochastic Gradient Descent (PG-SGD). PG-SGD uses the genomes, represented in the pangenome graph as paths, as an embedded positional system to sample genomic distances between pairs of nodes. This avoids the quadratic cost seen in previous versions of graph drawing by Stochastic Gradient Descent (SGD). We show that our implementation efficiently computes the low dimensional layouts of gigabase-scale pangenome graphs, unveiling their biological features. AvailabilityWe integrated PG-SGD in ODGI which is released as free software under the MIT open source license. Source code is available at https://github.com/pangenome/odgi. Contactegarris5@uthsc.edu

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↗

bioRxiv2022

MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant WGBS data

Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Many WGBS-based studies aim to identify biologically relevant loci that display differential methylation between genotypes, treatment groups, tissues, or developmental stages. Over the years, different tools have been developed to extract differentially methylated regions (DMRs) from whole-genome data. Often, such tools are built upon assumptions from mammalian data and do not consider the substantially more complex and variable nature of plant DNA methylation. Here, we present MethylScore, a pipeline to analyze WGBS data and to account for plant-specific DNA methylation properties. MethylScore processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. It uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation, substantially reducing the number of necessary statistical tests while increasing the signal-to-noise ratio and the statistical power. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the A. thaliana 1001 Genomes dataset to unveil known and unknown genotype-epigenotype associations. MethylScore is an accessible pipeline for plant WGBS data, with unprecedented features for DMR calling in small- and large-scale datasets; it is built as a Nextflow pipeline and its source code is available at https://github.com/Computomics/MethylScore.

plant biology↗

bioRxiv2019

RPW8/HR Repeats Predict NLR-dependent Hybrid Performance

Hybrid offspring can look very different from their parents, including having greatly increased or decreased fitness. In many plant species, conflicts between divergent elements of the immune system can cause hybrids to express autoimmunity, a generally deleterious syndrome known as hybrid necrosis. We are investigating multiple hybrid necrosis cases in Arabidopsis thaliana that are caused by allele-specific interactions between different variants at two unlinked resistance (R) gene clusters. One is the RESISTANCE TO PERONOSPORA PARASITICA 7 (RPP7) cluster, which encodes an intracellular nucleotide binding site-leucine rich repeat (NLR) immune receptors that confer strain-specific resistance to oomycetes. The other is the RESISTANCE TO POWDERY MILDEW 8 (RPW8)/HOMOLOG OF RPW8 (HR) locus, which encodes atypical resistance proteins that can confer broad-spectrum resistance to filamentous pathogens. There is extensive structural variation in the RPW8/HR cluster, both at the level of gene copy number and at the level of C-terminal protein repeats of unknown function. We demonstrate that the number of RPW8/HR repeats correlate, albeit in a complex manner, with the severity of hybrid necrosis when these alleles are combined with specific RPP7 variants. This observation suggests that gross structural differences, rather than individual amino acid polymorphisms, guide the genetic interaction between RPW8/HR and RPP7 alleles. We discuss these findings in light of the similarity of RPW8/HR proteins with pore-forming toxins, MLKL and HET-S, from mammals and fungi.

genetics↗

bioRxiv2018

Long-term balancing selection drives evolution of immunity genes in Capsella

Genetic drift is expected to remove polymorphism from populations over long periods of time, with the rate of polymorphism loss being accelerated when species experience strong reductions in population size. Adaptive forces that maintain genetic variation in populations, or balancing selection, might counteract this process. To understand the extent to which natural selection can drive the retention of genetic diversity, we document genomic variability after two parallel species-wide bottlenecks in the genus Capsella. We find that ancestral variation preferentially persists at immunity related loci, and that the same collection of alleles has been maintained in different lineages that have been separated for several million years. Our data point to long term balancing selection as an important factor shaping the genetics of immune systems in plants and as the predominant driver of genomic variability after a population bottleneck.

evolutionary biology↗

bioRxiv2017

A Role For The F-Box Protein HAWAIIAN SKIRT In Plant miRNA Function

As regulators of gene expression in multicellular organisms, microRNAs (miRNAs) are crucial for growth and development. While a plethora of factors involved in their biogenesis and action in Arabidopsis thaliana have been described, these processes and their fine-tuning are not fully understood in plants. Here, we used plants expressing an artificial miRNA target mimic (MIM) to screen for negative regulators of miR156 activity. We identified a new mutant allele of the F-box protein HAWAIIAN SKIRT (HWS; At3G61590), hws-5, as a suppressor of the MIM156-induced developmental and molecular phenotypes. In hws plants, levels of endogenous miRNAs are increased and their mRNA targets decreased. Plants constitutively expressing full-length HWS - but not a truncated version lacking the F-box domain - display morphological and molecular phenotypes resembling those of mutants defective in miRNA biogenesis and activity. In combination with such mutants, hws loses its delayed floral organ abscission ( skirt) phenotype, suggesting epistasis. Also, the overall hws transcriptome profile partially resembles well-known miRNA mutants hyl1-2 and se-3, indicating action in a common pathway. We thus propose HWS as a novel, F-box dependent regulator of miRNA biogenesis.\n\nSummary statementHAWAIIAN SKIRT is a regulator of Arabidopsis thaliana microRNA biogenesis and acts in an F-box-dependent manner.

plant biology↗