Biology subjects

Clevenger, J.

Publications and source records attributed to Clevenger, J..

8 recordsLinked to original sources

bioRxiv2026

A chromosome-level genome assembly of Thecaphora frezzii, cause of peanut smut, reveals the largest genome among the true smut fungi

Peanut smut, caused by the fungus Thecaphora frezzii, is a significant disease of peanuts in Argentina. Infected plants have seeds replaced by a mass of dark teliospores, reducing yield and seed quality. To prevent the spread of the pathogen, several countries have limited import of raw peanuts from Argentina, a major grower and exporter. Following successful in vitro culture of the fungus in its haploid stage, we produced a chromosome-level genome assembly of the species for the first time. We compare this genome with those of 49 other species of true smut fungi, or Ustilaginomycetes, including species of medical, agricultural, and industrial importance, some of which are known as pathogens and others only as saprotrophic yeasts. At almost 39 Mb, T. frezzii has the largest genome of the smut fungi sequenced to date and the highest repetitive content. While it shares some core effectors with species of the distantly related and better studied Ustilago and related fungi, predicted effectors only found in T. frezzii or in Thecaphora suggest unique infection strategies. Comparisons among the 50 smut genomes also show that the 14 smut fungi observed only as yeasts share genomic traits such as low repeat content and generally smaller genomes. This supports the hypothesis that some smut fungi are adapted to saprotrophic growth as yeasts. The high-quality, annotated genome for T. frezzii will be a valuable resource for investigating the population dynamics and evolution of an economically important pathogen, as well as illuminating an understudied clade of smut fungi. Article summaryPeanut smut is a destructive and costly disease of peanuts in Argentina. For the first time, a high quality, annotated genome is presented for the causal agent, Thecaphora frezzii. This fungus has the largest and most repetitive genome of the true smut fungi, thus prompting comparison with 49 other species of smut fungi with available genomes, including non-pathogenic ones. While it shares some likely pathogenicity factors with well-studied smuts, it has many unique genes, a trait reflective of its evolutionary distance and likely novel mechanism for infection. This important genomic resource will benefit research regarding the evolution and adaption of T. frezzii, the development of diagnostic tools that enable rapid detection of it, and the study of smut fungi broadly.

genomics↗

bioRxiv2025

KhufuEnv, an auxiliary toolkit for building computational pipelines for plant and animal breeding

In the era of short and long read sequencing, vast amounts of DNA sequencing data are being generated. While a variety of tools exist for analyzing and manipulating genomics data, many have a finite number of tools, and thus, require users to depend on multiple sources for conducting analyses and processing data. An integrative environment of tools which is accessible to users of different computational backgrounds would facilitate more efficient data processing and level the playing field for researchers whose research depends on analyzing genomic data. We developed the KhufuEnv, an open-source, flexible, auxiliary environment for manipulating and analyzing genomic data, among other datasets, in the Unix environment. The KhufuEnv provides a buildable platform for constructing custom pipelines for several genomic analyses across different species. As a proof of concept, we demonstrate rapid de novo identification of previously characterized quantitative trait loci (QTL) and calculate the proportion of the genome containing runs of homozygosity (ROH) with in-house tools on published datasets. Additionally, we introduce our custom HapMap and PanMap file formats. The KhufuEnv can be exploited for a variety of applications and implemented for quick analysis, supporting users with minimal computational experience.

genomics↗

bioRxiv2025

Long-Read Low-Pass Sequencing for High-Resolution Trait Mapping

Accelerating crop improvement is critical to meeting food security demands in a changing climate. Long-read sequencing offers advantages over short-reads in resolving structural variations (SVs) and aligning to complex genomes, but its high cost has limited adoption in breeding programs. Here we develop a high-throughput, scalable approach for long-read low-pass (LRLP) sequencing and variant analysis with PacBio HiFi reads, and apply it to trait mapping in a complex tetraploid peanut (Arachis hypogaea) genome multi-parent advanced generation intercross. We analyze LRLP using both a single reference genome and a pangraph, using both proprietary and open-source tools to analyze SVs and coverage. An increased number of variants are consistently called for LRLP data compared to short-read data. At 1.63x average depth, LRLP sequencing covered 55% of the genome and 58% of gene space, outperforming 1.68x depth short-read low-pass sequencing, which achieved only 17% and 11%, respectively. Enhanced data retention after filtering for probabilistic misalignment and an [~]8.5x decrease in cost per value further demonstrated LRLPs efficacy. Our results highlight LRLP sequencing as a scalable, cost-effective tool for high-resolution trait mapping, with transformative potential for plant breeding and broader genomic applications.

genetics↗

bioRxiv2023

A haplotype-resolved chromosome-level assembly and annotation of European hazelnut (C. avellana cv. Jefferson) provides insight into mechanisms of eastern filbert blight resistance

European hazelnut (Corylus avellana L.) is an important tree nut crop. Hazelnut production in North America is currently limited in scalability due to Anisogramma anomala, a fungal pathogen that causes Eastern Filbert Blight (EFB) disease in hazelnut. Successful deployment of EFB resistant cultivars has been limited to the state of Oregon, where the breeding program at Oregon State University (OSU) has released cultivars with a dominant allele at a single resistance locus identified by classical breeding, linkage mapping, and molecular markers. Jefferson is resistant to the predominant EFB biotype in Oregon and has been selected by the OSU breeding program as a model for hazelnut genetic and genomic research. Here, we present a near complete, haplotype-resolved chromosome-level hazelnut genome assembly for C. avellana Jefferson. This new assembly is a significant improvement over a previously published genome draft. Analysis of genomic regions linked to EFB resistance and self-incompatibility confirmed haplotype splitting and identified new gene candidates that are essential for downstream molecular marker development, thereby facilitating breeding efforts.

genomics↗

bioRxiv2021

Accurate analysis of short read sequencing in complex genomes: A case study using QTL-seq to target blanchability in peanut (Arachis hypogaea)

Next Generation sequencing was a step change for molecular genetics and genomics. Illumina sequencing in particular still provides substantial value to animal and plant genomics. A simple yet powerful technique, referred to as QTL sequencing (QTL-seq) is susceptible to high levels of noise due to ambiguity of alignment of short reads in complex regions of the genome. This noise is particularly high when working with polyploid and/or outcrossing crop species, which impairs the efficacy of QTL-seq in identifying functional variation. By filtering loci based on the optimal alignment of short reads, we have developed a pipeline, named Khufu, that substantially improves the accuracy of QTL-seq analysis in complex genomes, allowing de novo variant discovery directly from bulk sequence. We first demonstrate the pipeline by identifying and validating loci contributing to blanching percentage in peanut using lines from multiple related populations. Using other published datasets in peanut, Brassica rapa, Hordeum volgare, Lactua satvia, and Felis catus, we demonstrate that Khufu produces more accurate results straight from bulk sequence. Khufu works across species, genome ploidy level, and data types. In cases where identified QTL were fine mapped, the fine mapped region corresponds to the top of the peak identified by Khufu. The accuracy of Khufu allows the analysis of population sequencing at very low coverage (<3x), greatly decreasing the amount of sequence needed to genotype even the most complex genomes.

genomics↗

bioRxiv2021

AhNGE : A Database for Arachis hypogaea Nodule Developmental Gene Expression

Symbiotic nitrogen fixation (SNF) inside root-nodules is a primary and sustainable source of soil nitrogen. Understanding nodule development and metabolism in crop legumes may lead to more effective SNF in agriculture. Peanut (Arachis hypogaea) is an economically important allotetraploid legume with non-canonical nodule developmental features. Recent genome sequencing of peanut has opened the possibility of making peanut a model for studying atypical nodule development. To help the community of nodule biologists, we have developed a database called AhNGE (Arachis hypogaea Nodule Developmental Gene Expression: http://nipgr.ac.in/AhNGE/index.php). AhNGE contains RNAseq data from six data points of nodule development in A. hypogagea cv. Tifrunner. This data represents a dynamic view of gene expression during peanut nodule development. Research in model legumes has generated a huge knowledgebase in the last twenty years. To streamline comparative genomics among legumes, we performed ortholog analysis among four legumes (Cicer, Glycine, Lotus, and Medicago) and one non-legume (Arabidopsis). This will facilitate the integration of existing knowledge in nodule development with the Arachis transcriptome. The available data can be retrieved using a single or batch query or searching using gene ID, from above mentioned five species. The output displays the gene expression pattern in graphical as well as tabular form, along with further options to download the sequence data. The database is linked with PeanutBase, the main genomic resource of peanut. Additionally, the expression level of different splicing variants can be retrieved from the database. In summary, AhNGE serves as an important resource for the scientific community working on nodule development.

bioinformatics↗

bioRxiv2019

An advantage for targets located horizontally to the cued location

Over the years a number of researchers have reported enhanced performance of targets located horizontally to a cued location relative to those located vertically. However, many of these reports could stem from a known meridian asymmetry in which stimuli on the horizontal meridian show a performance advantage relative to those on the vertical meridian. Here we show a horizontal advantage for target and cue locations that reside outside the zone of asymmetry; that is, targets that appear horizontal to the cue, but above or below the horizontal meridian, are more accurate than those that appear vertical to the cue, but again either above or below the horizontal meridian (Experiments 1 and 4). This advantage does not extend to non-symmetrically located targets in the opposite hemifield (Experiment 2), nor horizontally located targets within the same hemifield (Experiment 3). These data raise the possibility that display designs in which the target and cue locations are positioned symmetrically across the vertical midline may be underestimating the cue validity effect.

neuroscience↗

bioRxiv2018

Machine learning as an effective method for identifying true SNPs in polyploid plants

Single Nucleotide Polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and co-dominant. However, the discovery of true SNPs especially in polyploid species is difficult. Peanut is an allopolyploid, which has a very low rate of true SNP calling. A large set of true and false SNPs identified from the Arachis 58k Affymetrix array was leveraged to train machine learning models to select true SNPs straight from sequence data. These models achieved accuracy rates of above 80% using real peanut RNA-seq and whole genome shotgun (WGS) re-sequencing data, which is higher than previously reported for polyploids. A 48K SNP array, Axiom Arachis2, was designed using the approach which revealed 75% accuracy of calling SNPs from different tetraploid peanut genotypes. Using the method to simulate SNP variation in peanut, cotton, wheat, and strawberry, we show that models built with our parameter sets achieve above 98% accuracy in selecting true SNPs. Additionally, models built with simulated genotypes were able to select true SNPs at above 80% accuracy using real peanut data, demonstrating that our model can be used even if real data are not available to train the models. This work demonstrates an effective approach for calling highly reliable SNPs from polyploids using machine learning. A novel tool was developed for predicting true SNPs from sequence data, designated as SNP-ML (SNP-Machine Learning, pronounced \"snip mill\"), using the described models. SNP-ML additionally provides functionality to train new models not included in this study for customized use, designated SNP-MLer (SNP-Machine Learner, pronounced \"snip miller\"). SNP-ML is freely available for public use.

bioinformatics↗