Approaches to increase the validity of gene family identification using manual homology search tools.

Identifying homologs is an important process in the analysis of genetic patterns underlying traits and evolutionary relationships among species. Analysis of gene families is often used to form and support hypotheses on genetic patterns such as gene presence, absence, or functional divergence which underlie traits examined in functional studies. These analyses often require precise identification of all members in a targeted gene family. Manual pipelines where homology search and orthology assignment tools are used separately are the most common approach for identifying small gene families where accurate identification of all members is important. The ability to curate sequences between steps in manual pipelines allows for simple and precise identification of all possible gene family members. However, the validity of such manual pipeline analyses is often decreased by inappropriate approaches to homology searches including too relaxed or stringent statistical thresholds, inappropriate query sequences, homology classification based on sequence similarity alone, and low-quality proteome or genome sequences. In this article, we propose several approaches to mitigate these issues and allow for precise identification of gene family members and support for hypotheses linking genetic patterns to functional traits.

Legume-wide comparative analysis of pod shatter locus PDH1 reveals phaseoloid specificity, high cowpea expression, and stress responsive genomic context.

Pod dehiscence is a major source of yield loss in legumes, which is exacerbated by aridity. Disruptive mutations in "Pod indehiscent 1" (PDH1), a pod sclerenchyma-specific lignin biosynthesis gene, has been linked to significant reductions in dehiscence in several legume species. We compared syntenic PDH1 regions across 12 legumes and two outgroups to uncover key historical evolutionary trends at this important locus. Our results clarified the extent to which PDH1 orthologs are present in legumes, showing the typical genomic context surrounding PDH1 has only arisen relatively recently in certain phaseoloid species (Vigna, Phaseolus, Glycine). The notable absence of PDH1 in Cajanus cajan may be a major contributor to its indehiscent phenotype compared with other phaseoloids. In addition, we identified a novel PDH1 ortholog in Vigna angularis and detected remarkable increases in PDH1 transcript abundance during Vigna unguiculata pod development. Investigation of the shared genomic context of PDH1 revealed it lies in a hotspot of transcription factors and signaling gene families that respond to abscisic acid and drought stress, which we hypothesize may be an additional factor influencing expression of PDH1 under specific environmental conditions. Our findings provide key insights into the evolutionary history of PDH1 and lay the foundation for optimizing the pod dehiscence role of PDH1 in major and understudied legume species.

An SGSGeneloss-Based Method for Constructing a Gene Presence-Absence Table Using Mosdepth.

Presence-absence variants (PAV) are genomic regions present in some individuals of a species, but not others. PAVs have been shown to contribute to genomic diversity, especially in bacteria and plants. These structural variations have been linked to traits and can be used to track a species' evolutionary history. PAVs are usually called by aligning short read sequence data from one or more individuals to a reference genome or pangenome assembly, and then comparing coverage. Regions where reads do not align define absence in that individual, and the regions are classified as PAVs. The method below details how to align sequence reads to a reference and how to use the sequencing-coverage calculator Mosdepth to identify PAVs and construct a PAV table for use in downstream comparative genome analysis.

Expanding Gene-Editing Potential in Crop Improvement with Pangenomes.

Pangenomes aim to represent the complete repertoire of the genome diversity present within a species or cohort of species, capturing the genomic structural variance between individuals. This genomic information coupled with phenotypic data can be applied to identify genes and alleles involved with abiotic stress tolerance, disease resistance, and other desirable traits. The characterisation of novel structural variants from pangenomes can support genome editing approaches such as Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR associated protein Cas (CRISPR-Cas), providing functional information on gene sequences and new target sites in variant-specific genes with increased efficiency. This review discusses the application of pangenomes in genome editing and crop improvement, focusing on the potential of pangenomes to accurately identify target genes for CRISPR-Cas editing of plant genomes while avoiding adverse off-target effects. We consider the limitations of applying CRISPR-Cas editing with pangenome references and potential solutions to overcome these limitations.

Resources for image-based high-throughput phenotyping in crops and data sharing challenges.

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.