Biology subjects

Michelmore, R. W.

Publications and source records attributed to Michelmore, R. W..

6 recordsLinked to original sources

bioRxiv2025

MoGAAAP: A modular Snakemake workflow for automated genome assembly and annotation with quality assessment

With the current speed of sequencing, there is a desire for standardised and automated genome assembly and annotation to produce high-quality genomes as input for comparative (pan)genomics. Therefore, we created a convenience pipeline using existing tools that creates annotated genome assemblies from HiFi (and optionally ultra-long ONT and/or Hi-C) reads for a set of related accessions as well as a related reference genome. Our pipeline is species-agnostic and generates an extensive quality assessment report that can be used for manual filtering and refinement of the assembly and annotation. It includes statistics for individual completeness and contamination assessments as well as a concise pangenome view. The pipeline is implemented in Snakemake and available with a GPLv3 license at GitHub under github.com/dirkjanvw/MoGAAAP and at Zenodo under doi.org/10.5281/zenodo.14833021.

bioinformatics↗

bioRxiv2022

Genome assembly and association tests identify interacting loci associated with vigor, precocity, and sex in interspecific pistachio rootstocks

Understanding the basis of hybrid vigor remains a key question in crop breeding and improvement, especially for rootstock development where F1 hybrids are extensively utilized. Full-sibling UCB-1 F1 seedling rootstocks are widely planted in commercial pistachio orchards that are generated by crossing two highly heterozygous outbreeding parental trees of Pistacia atlantica (female) and P. integerrima (male). This results in extensive phenotypic variability, prompting costly removal of low-yielding small trees. To identify the genetic basis of this variability, we assembled chromosome-scale genome assemblies of the parental trees of UCB-1. We genotyped 960 UCB-1 trees in an experimental orchard for which we also collected multi-year phenotypes. We genotyped an additional 1,358 rootstocks in six commercial pistachio orchards and collected single-year tree size data. Genome-wide single marker association tests identified loci associated with tree size and shape, sex, and precocity. In the experimental orchard, we identified multiple trait-associated loci and a strong candidate for ZZ/ZW sex chromosomes. We found significant marker associations unique to different traits and to early vs. late phenotypic measures of the same trait. We detected two loci strongly associated with rootstock size in commercial orchards. Pseudo-testcross classification of markers demonstrated that the trait-associated alleles for each locus were segregating in the gametes of opposite parents. These two loci interact epistatically to generate the bimodal distribution of tree size with undesirable small trees observed by growers. We identified candidate genes within these regions. These findings provide a foundational resource for marker development and genetic selection of vigorous pistachio UCB-1 rootstock.

genetics↗

bioRxiv2021

EffectorO: motif-independent prediction of effectors in oomycete genomes using machine learning and lineage specificity

Oomycete plant pathogens cause a wide variety of diseases, including late blight of potato, sudden oak death, and downy mildews of plants. These pathogens are major contributors to loss in numerous food crops. Oomycetes secrete effector proteins to manipulate their hosts to the advantage of the pathogen. Plants have evolved to recognize effectors, resulting in an evolutionary cycle of defense and counter-defense in plant-microbe interactions. This selective pressure results in highly diverse effector sequences that can be difficult to computationally identify using only sequence similarity. We developed a novel effector prediction tool, EffectorO, that uses two complementary approaches to predict effectors in oomycete pathogen genomes: (1) a machine learning-based pipeline that predicts effector probability based on the biochemical properties of the N-terminal amino acid sequence of a protein and (2) a pipeline based on lineage-specificity to find proteins that are unique to one species or genus, a sign of evolutionary divergence due to adaptation to the host. We tested EffectorO on Bremia lactucae, which causes lettuce downy mildew, and Phytophthora infestans, which causes late blight of potato and tomato, and predicted many novel effector candidates, while recovering the majority of known effector candidates. EffectorO will be useful for discovering novel families of oomycete effectors without relying on sequence similarity to known effectors.

plant biology↗

bioRxiv2020

Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce

Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences comprised of multiple florets that open only once. Different accessions were observed to flower at different times of day. An F6 recombinant inbred line population (RIL) had been derived from accessions of L. serriola x L. sativa that originated from different environments and differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored over a seven-hour period using time-course image series obtained by drone-based remote phenotyping on two occasions, one week apart. Floral pixels were identified from the images using a support vector machine (SVM) machine learning algorithm with an accuracy above 99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent QTLs, qDFO2.1 (Daily Floral Opening 2.1) and qDFO8.1, were discovered. Together, they explained more than 30% of the phenotypic variation in floral opening time. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote imaging, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes such as floral opening time. One sentence summaryMachine learning and Bayesian analyses of drone-mediated remote phenotyping data revealed two genetic loci regulating differential daily flowering time in lettuce (Lactuca spp.).

genetics↗

bioRxiv2019

The alternative reality of plant mitochondrial DNA

Plant mitochondrial genomes are usually assembled and displayed as circular maps based on the widely-held assumption that circular genome molecules are the primary form of mitochondrial DNA, despite evidence to the contrary. Many plant mitochondrial genomes have one or more pairs of large repeats that can act as sites for inter- or intramolecular recombination, leading to multiple alternative genomic arrangements (isoforms). Most mitochondrial genomes have been assembled using methods that were unable to capture the complete spectrum of isoforms within a species, leading to an incomplete inference of their structure and recombinational activity. To document and investigate underlying reasons for structural diversity in plant mitochondrial DNA, we used long-read (PacBio) and short-read (Illumina) sequencing data to assemble and compare mitochondrial genomes of domesticated (Lactuca sativa) and wild (L. saligna and L. serriola) lettuce species. This allowed us to characterize a comprehensive, complex set of isoforms within each species and to compare genome structures between species. Physical analysis of L. sativa mtDNA molecules by fluorescence microscopy revealed a variety of linear, branched linear, and circular structures. The mitochondrial genomes for L. sativa and L. serriola were identical in sequence and arrangement, and differed substantially from L. saligna, indicating that the mitochondrial genome structure did not change during domestication. From the isoforms evident in our data, we inferred that recombination occurs at repeats of all sizes at variable frequencies. The differences in genome structure between L. saligna and the two other lettuce species can be largely explained by rare recombination events that rearrange the structure. Our data demonstrate that representations of plant mitochondrial DNA as simple, genome-sized circular molecules are not accurate descriptions of their true nature and that in reality plant mitochondrial DNA is a complex, dynamic mixture of forms. Data AvailabilityBioProject: Organellar genomes of cultivated and wild lettuce (Lactuca) varieties PRJNA508811 https://www.ncbi.nlm.nih.gov/bioproject/508811 and other accessions as indicated through the text and supplemental data. FundingNSF grant MCB-1413152 to ACC and support from UC Davis to RWM.

genomics↗

bioRxiv2018

Linked-read sequencing of gametes allows efficient genome-wide analysis of meiotic recombination

Meiotic crossovers (COs) ensure proper chromosome segregation and redistribute the genetic variation that is transmitted to the next generation. Existing methods for CO identification are challenged by large populations and the demand for genome-wide and fine-scale resolution. Taking advantage of linked-read sequencing, we developed a highly efficient method for genome-wide identification of COs at kilobase resolution in pooled recombinants. We first tested this method using a pool of Arabidopsis F2 recombinants, and obtained results that recapitulated those identified from the same plants using individual whole-genome sequencing. By applying this method to a pool of pollen DNA from a single F1 plant, we established a highly accurate CO landscape without generating or sequencing a single recombinant plant. The simplicity of this approach now enables the simultaneous generation and analysis of multiple CO landscapes and thereby allows for efficient comparison of genotypic and environmental effects on recombination, accelerating the pace at which the mechanisms for the regulation of recombination can be elucidated.

bioinformatics↗