Biology subjects

Portwood, J. L.

Publications and source records attributed to Portwood, J. L..

6 recordsLinked to original sources

bioRxiv2026

Visualizing and integrating linear and graph pangenomes at the Maize Genetics and Genomics Database

Genome visualization has existed for as long as assembled genomes, with an array of tools and approaches to meet researchers' needs. The genome browser is one of these tools, and it has become a critical analytical resource for geneticists, genome biologists, and breeders. The evolution of genome browser capabilities is ongoing, and the USDA-ARS Maize Genetics and Genomics Database (MaizeGDB) has implemented JBrowse2, the most recent iteration of JBrowse. It includes multiple genome browser and alignment views, demonstrating pangenome visualization capability and permitting sophisticated functional characterization of maize loci. Described here also is MaizeGDB's new Pangenome Viewer, which integrates pangenome graph views with linear browser visualization to obtain an on-the-fly, interactive visual snapshot of structural variation across a pangenome at user-selected loci, with zoom capabilities and statistical and variant information for each subgraph. Finally, we demonstrate how different MaizeGDB pangenome pipelines complement one another to help guide accurate analyses. This functionality can lead to more precise characterization of loci that confer important agronomic traits, resulting in better outcomes for farmers and the public.

genomics↗

bioRxiv2025

Why do some predicted protein structures fold poorly? Benchmarking AlphaFold, ESMFold, and Boltz in maize

Protein structure prediction tools have significantly reduced the time and cost to generate protein structures and accelerated protein discovery and design. However, plant proteins are underrepresented in sequence and structural datasets used to train these programs. To quantify the downstream impact of this deficiency, we benchmarked five structure-prediction programs (AlphaFold 2, AlphaFold 3, ESMFold, Boltz-1, and Boltz-2) across 417 well-characterized Zea mays genes. These "classical" genes represent a set of well-studied genes with known genetic and phenotypic effects. We generated structures for each gene using these programs and compared how sequence, structural, and evolutionary conservation impacted the structures confidence and geometric features. Proteins lacking conserved sequence and/or structural domains had on average 25% to 43% lower confidence scores than proteins having both domains. Proteome-wide phylostratigraphy revealed that species-specific proteins had substantially lower confidence scores than proteins conserved amongst angiosperms and Eukaryotes. Boltz-1 and ESMFold structures had the highest occurrence of structures with severe geometry issues, including overlapping atoms and unlikely bond angles. We also compared computational and experimental alignments of Arabidopsis, maize, rice, wheat, and soybean proteins from the Protein Data Bank and identified structures showing incongruences with experimental data. This study challenges the assertion that protein folding has been completely solved, and urges more investigation into benchmarking and standardized evaluation frameworks to improve model performance and assessment in agricultural crops.

bioinformatics↗

bioRxiv2024

Fusarium Protein Toolkit: AI-powered tools to combat fungal threats to agriculture

BackgroundThe fungal genus Fusarium poses significant threats to food security and safety worldwide because it consists of numerous species that cause destructive diseases in crops, as well as mycotoxin contamination. The adverse effects of climate change are exacerbating some existing threats and causing new problems. These challenges highlight the need for innovative solutions, including the development of advanced tools to identify targets to control crop diseases and mycotoxin contamination incited by Fusarium. DescriptionIn response to these challenges, we developed the Fusarium Protein Toolkit (FPT, https://fusarium.maizegdb.org/), a web-based tool that allows users to interrogate the structural and variant landscape within the Fusarium pan-genome. FPT offers a comprehensive approach to understanding and mitigating the detrimental effects of Fusarium on agriculture. The tool displays both AlphaFold and ESMFold-generated protein structure models from six Fusarium species. The structures are accessible through a user-friendly web portal and facilitate comparative analysis, functional annotation inference, and identification of related protein structures. Using a protein language model, FPT predicts the impact of over 270 million coding variants in two of the most agriculturally important species, Fusarium graminearum, which causes Fusarium head blight and trichothecene mycotoxin contamination of cereals, and F. verticillioides, which causes ear rot and fumonisin mycotoxin contamination of maize. To facilitate the assessment of naturally occurring genetic variation, FPT provides variant effect scores for proteins in a Fusarium pan-genome constructed from 22 diverse species. The scores indicate potential functional consequences of amino acid substitutions and are displayed as intuitive heatmaps using the PanEffect framework. ConclusionFPT fills a knowledge gap by providing previously unavailable tools to assess structural and missense variation in proteins produced by Fusarium, the most agriculturally important group of mycotoxin-producing plant pathogens. FPT will deepen our understanding of pathogenic mechanisms in Fusarium, and aid the identification of genetic targets that can be used to develop control strategies that reduce crop diseases and mycotoxin contamination. Such targets are vital to solving the agricultural problems incited by Fusarium, particularly evolving threats affected by climate change. By providing a novel approach to interrogate Fusarium-induced crop diseases, FPT is a crucial step toward safeguarding food security and safety worldwide.

bioinformatics↗

bioRxiv2023

PanEffect: A pan-genome visualization tool for variant effects in maize

Understanding the effects of genetic variants is crucial for accurately predicting traits and phenotypic outcomes. Recent advances have utilized protein language models to score all possible missense variant effects at the proteome level for a single genome, but a reliable tool is needed to explore these effects at the pan-genome level. To address this gap, we introduce a new tool called PanEffect. We implemented PanEffect at MaizeGDB to enable a comprehensive examination of the potential effects of coding variants across 51 maize genomes. The tool allows users to visualize over 550 million possible amino acid substitutions in the B73 maize reference genome and also to observe the effects of the 2.3 million natural variations in the maize pan-genome. Each variant effect score, calculated from the Evolutionary Scale Modeling (ESM) protein language model, shows the log-likelihood ratio difference between B73 and all variants in the pan-genome. These scores are shown using heatmaps spanning benign outcomes to strong phenotypic consequences. Additionally, PanEffect displays secondary structures and functional domains along with the variant effects, offering additional functional and structural context. Using PanEffect, researchers now have a platform to explore protein variants and identify genetic targets for crop enhancement. Availability and implementation: The PanEffect code is freely available on GitHub (https://github.com/Maize-Genetics-and-Genomics-Database/PanEffect). A maize implementation of PanEffect and underlying datasets are available at MaizeGDB (https://www.maizegdb.org/effect/maize/).

bioinformatics↗

bioRxiv2021

De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes

We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The data indicate that the number of pan-genes exceeds 103,000 and that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres further reveal the locations and internal structures of major cytological landmarks. We show that combining structural variation with SNPs can improve the power of quantitative mapping studies. Finally, we document variation at the level of DNA methylation, and demonstrate that unmethylated regions are enriched for cis-regulatory elements that overlap QTL and contribute to changes in gene expression. One sentence summaryA multi-genome analysis of maize reveals previously unknown variation in gene content, genome structure, and methylation.

genomics↗

bioRxiv2019

GenomeQC: A quality assessment tool for genome assemblies and gene structure annotations

BackgroundGenome assemblies are foundational for understanding the biology of a species. They provide a physical framework for mapping additional sequences, thereby enabling characterization of, for example, genomic diversity and differences in gene expression across individuals and tissue types. Quality metrics for genome assemblies gauge both the completeness and contiguity of an assembly and help provide confidence in downstream biological insights. To compare quality across multiple assemblies, a set of common metrics are typically calculated and then compared to one or more gold standard reference genomes. While several tools exist for calculating individual metrics, applications providing comprehensive evaluations of multiple assembly features are, perhaps surprisingly, lacking. Here, we describe a new toolkit that integrates multiple metrics to characterize both assembly and gene annotation quality in a way that enables comparison across multiple assemblies and assembly types.\n\nFindingsOur application, named GenomeQC, is an easy-to-use and interactive web framework that integrates various quantitative measures to characterize genome assemblies and annotations. GenomeQC provides researchers with a comprehensive summary of these statistics and allows for benchmarking against gold standard reference assemblies.\n\nConclusionsThe GenomeQC web application is implemented in R/Shiny version 1.5.9 and Python 3.6 and is freely available at https://genomeqc.maizegdb.org/ under the GPL license.\n\nAll source code and a containerized version of the GenomeQC pipeline is available in the GitHub repository https://github.com/HuffordLab/GenomeQC.

bioinformatics↗