Biology subjects

Main, D.

Publications and source records attributed to Main, D..

6 recordsLinked to original sources

bioRxiv2025

Development of Rosaceae Crop-Specific Nanopore Models for Community Use Through the Genome Database for Rosaceae

Oxford Nanopore Technologies (ONT) sequencing platforms have enabled scientists to generate sequence reads of 20 kilobase or more, which has facilitated the assembly of complete genomes. Nanopore sequencing involves recording the change in electrical signals as the DNA molecule transverses the pore. Converting this signal information into bases - a process called basecalling - is challenging and require the implementation of machine learning. The models developed for ONT basecallers have continually improved. However, the mean quality of the reads generated fall below a Phred score of 20 (99% accuracy), the target quality for high-quality genome assembly, when used on challenging plant samples. These low-quality base calls limit the ability to conduct de novo genome assembly, accurate phasing of haplotypes, and long-read genotyping. To overcome these shortcomings, we fine-tuned ONTs high accuracy basecalling model using Bonito, an AI-based deep learning basecaller, to develop crop-specific models for five important species in the Rosaceae family. These species include highly valuable tree crops such as apple, peach, pear, plum, and sweet cherry. Through the development of an automated training pipeline, we were able to achieve [~]14% higher mean and median basecall quality, and a [~]20% increase in total reads with Phred scores >15. These results were achieved without significantly affecting the read length N50s and total reads called. As a result, these new crop-specific models will enable members of the Rosaceae genomics community to produce higher quality ONT sequencing data. Furthermore, we are releasing our automated pipeline to facilitate others to train their own organism or crop-specific models.

genomics↗

bioRxiv2025

Improved genome assembly of double haploid Prunus persica siblings Lovell 2D and Lovell 5D and the peach NLRome

Prunus persica (peach) has long served as a model fruit tree for studying phenological events. It has a relatively small genome and exhibits tremendous plasticity in climate tolerances due to the high variation of chill requirements, bloom times, and fruit ripening times. The peach variety Lovell 2D was used to generate one of the first high-quality genome assemblies for a tree species, using Sanger sequencing of genetic-map ordered BAC clones. A key to the high quality of this early assembly was the use of a doubled haploid variety, which eliminates the challenges posed by mixed haplotypes. Here, we re-sequenced and assembled the Lovell 2D genome along with a doubled haploid sibling Lovell 5D using 3rd generation technologies. The resulting genomes were significantly more contiguous than the current Lovell 2D reference genome (ver2.0 updated in 2017) and are closer to the estimated total genome size for peach (265Mb). In addition, new gene, transposable element (TE), and Nucleotide-binding domain and Leucine-rich repeat receptor (NLR) annotations were performed to enhance the integrity and utility of the genome. These updated peach doubled-haploid reference assemblies will provide the research community with an improved reference genome for genomics-guided studies and breeding efforts.

genomics↗

bioRxiv2023

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures) as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium - a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

genomics↗

bioRxiv2021

Genomic rearrangements have consequences for introgression breeding as revealed by genome assemblies of wild and cultivated lentil species

Understanding the genomic relationship between wild and cultivated genomes would facilitate access to the untapped variability found in crop wild relatives. We developed genome assemblies of a cultivated lentil (Lens culinaris) as well as a wild relative (L. ervoides). Comparative analyses revealed large-scale structural rearrangements and additional repetitive DNA in the cultivated genome, resulting in regions of reduced recombination, segregation distortion and permanent heterozygosity in the offspring of a cross between the two species. These novel findings provide plant breeders with better insight into how best to approach accessing the novel variability available in wild relatives.

plant biology↗

bioRxiv2021

Harnessing genetic diversity in the USDA pea (Pisum sativum L.) germplasm collection through genomic prediction

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeders toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic selections potential to a set of 482 pea accessions - genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components - for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, proteomics, metabolomics, physiological traits, etc.) into the genomic prediction framework could enhance prediction accuracy.

genomics↗

bioRxiv2019

Citrusgreening.org: An open access and integrated systems biology portal for the Huanglongbing (HLB) disease complex

We have created an open access web portal with pathosystem-wide resources and bioinformatics tools for the host citrus, the vector Asian citrus psyllid (ACP) and multiple pathogens including Ca. Liberibacter asiaticus. To the best of our knowledge, this is the first example of a database to use the pathosystem as a holistic framework to understand an insect transmitted plant disease. This endeavor integrates and enables the analysis of data sets generated by the community to study the citrus greening disease complex. Users can submit relevant data sets to enable sharing and allow the community to better analyze their data within an integrated system. The portal contains a variety of tools for omics data. Metabolic pathway databases, CitrusCyc and DiaphorinaCyc provide organism specific pathways that can be used to mine metabolomics, transcriptomics and proteomics results to identify pathways and regulatory mechanism involved in disease response. Psyllid Expression Network (PEN) contains expression profiles of ACP genes from multiple life stages, tissues, conditions and hosts. Citrus Expression Network (CEN) contains public expression data from multiple tissues and conditions for various citrus hosts. All tools like Apollo/JBrowse, Biocyc, Blast, CEN and PEN connect to a central database containing gene models for citrus, ACP and multiple Liberibacter pathogens. The portal also includes electrical penetration graph (EPG) recordings of ACP feeding on citrus, information about citrus rootstock trials and metabolomics data in addition to traditional omics data types with a goal of combining and mining all information related to a pathosystem. The portal includes user-friendly manual curation tools to allow the research community to continuously improve this knowledge base as more experimental research is published. Bulk downloads are available for all genome and annotation datasets from the FTP site (ftp://ftp.citrusgreening.org). The portal can be accessed at https://citrusgreening.org/.

bioinformatics↗