MCRiceRepGP: a framework for the identification of genes associated with sexual reproduction in rice

DNABERT-based explainable lncRNA identification in plant genome assemblies

Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% for Z. mays and the lowest accuracy of 57.9% for B. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence.

Grain dispersal mechanism in cereals arose from a genome duplication followed by changes in spatial expression of genes involved in pollen development

KEY MESSAGE

Grain disarticulation in wild progenitor of wheat and barley evolved through a local duplication event followed by neo-functionalization resulting from changes in location of gene expression. One of the most critical events in the process of cereal domestication was the loss of the natural mode of grain dispersal. Grain dispersal in barley is controlled by two major genes, Btr1 and Btr2, which affect the thickness of cell walls around the disarticulation zone. The barley genome also encodes Btr1-like and Btr2-like genes, which have been shown to be the ancestral copies. While Btr and Btr-like genes are non-redundant, the biological function of Btr-like genes is unknown. We explored the potential biological role of the Btr-like genes by surveying their expression profile across 212 publicly available transcriptome datasets representing diverse organs, developmental stages and stress conditions. We found that Btr1-like and Btr2-like are expressed exclusively in immature anther samples throughout Prophase I of meiosis within the meiocyte. The similar and restricted expression profile of these two genes suggests they are involved in a common biological function. Further analysis revealed 141 genes co-expressed with Btr1-like and 122 genes co-expressed with Btr2-like, with 105 genes in common, supporting Btr-like genes involvement in a shared molecular pathway. We hypothesize that the Btr-like genes play a crucial role in pollen development by facilitating the formation of the callose wall around the meiocyte or in the secretion of callase by the tapetum. Our data suggest that Btr genes retained an ancestral function in cell wall modification and gained a new role in grain dispersal due to changes in their spatial expression becoming spike specific after gene duplication.

Pangenomics in crop improvement-from coding structural variations to finding regulatory variants with pangenome graphs

Since the first reported crop pangenome in 2014, advances in high-throughput and cost-effective DNA sequencing technologies facilitated multiple such studies including the pangenomes of oilseed rape (Brassica napus L.), soybean [Glycine max (L.) Merr.], rice (Oryza sativa L.), wheat (Triticum aestivum L.), and barley (Hordeum vulgare L.). Compared with single-reference genomes, pangenomes provide a more accurate representation of the genetic variation present in a species. By combining the genomic data of multiple accessions, pangenomes allow for the detection and annotation of complex DNA polymorphisms such as structural variations (SVs), one of the major determinants of genetic diversity within a species. In this review we summarize the current literature on crop pangenomics, focusing on their application to find candidate SVs involved in traits of agronomic interest. We then highlight the potential of pangenomes in the discovery and functional characterization of noncoding regulatory sequences and their variations. We conclude with a summary and outlook on innovative data structures representing the complete content of plant pangenomes including annotations of coding and noncoding elements and outcomes of transcriptomic and epigenomic experiments.

Pangenomes as a Resource to Accelerate Breeding of Under-Utilised Crop Species

Pangenomes are a rich resource to examine the genomic variation observed within a species or genera, supporting population genetics studies, with applications for the improvement of crop traits. Major crop species such as maize (Zea mays), rice (Oryza sativa), Brassica (Brassica spp.), and soybean (Glycine max) have had pangenomes constructed and released, and this has led to the discovery of valuable genes associated with disease resistance and yield components. However, pangenome data are not available for many less prominent crop species that are currently under-utilised. Despite many under-utilised species being important food sources in regional populations, the scarcity of genomic data for these species hinders their improvement. Here, we assess several under-utilised crops and review the pangenome approaches that could be used to build resources for their improvement. Many of these under-utilised crops are cultivated in arid or semi-arid environments, suggesting that novel genes related to drought tolerance may be identified and used for introgression into related major crop species. In addition, we discuss how previously collected data could be used to enrich pangenome functional analysis in genome-wide association studies (GWAS) based on studies in major crops. Considering the technological advances in genome sequencing, pangenome references for under-utilised species are becoming more obtainable, offering the opportunity to identify novel genes related to agro-morphological traits in these species.

Biological Parts for Engineering Abiotic Stress Tolerance in Plants

It is vital to ramp up crop production dramatically by 2050 due to the increasing global population and demand for food. However, with the climate change projections showing that droughts and heatwaves becoming common in much of the globe, there is a severe threat of a sharp decline in crop yields. Thus, developing crop varieties with inbuilt genetic tolerance to environmental stresses is urgently needed. Selective breeding based on genetic diversity is not keeping up with the growing demand for food and feed. However, the emergence of contemporary plant genetic engineering, genome-editing, and synthetic biology offer precise tools for developing crops that can sustain productivity under stress conditions. Here, we summarize the systems biology-level understanding of regulatory pathways involved in perception, signalling, and protective processes activated in response to unfavourable environmental conditions. The potential role of noncoding RNAs in the regulation of abiotic stress responses has also been highlighted. Further, examples of imparting abiotic stress tolerance by genetic engineering are discussed. Additionally, we provide perspectives on the rational design of abiotic stress tolerance through synthetic biology and list various bioparts that can be used to design synthetic gene circuits whose stress-protective functions can be switched on/off in response to environmental cues.

Long Intergenic Noncoding RNA (lincRNA) Discovery from Non-Strand-Specific RNA-Seq Data

Long noncoding RNAs (lncRNAs) are transcripts over 200 base pairs in length without discernible protein coding potential. Long intergenic noncoding RNAs (lincRNAs) constitute a subset of lncRNAs, which do not overlap protein coding genes. Here we describe a detailed pipeline for lincRNA discovery from publicly available non-stranded RNA-Seq datasets. The pipeline presented can be applied to any plant species for which RNA-Seq data and a reference genome sequence are available.

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics

Safeguarding crop yields in a changing climate requires bioinformatics advances in harnessing data from vast phenomics and genomics datasets to translate research findings into climate smart crops in the field. Climate change and an additional 3 billion mouths to feed by 2050 raise serious concerns over global food security. Crop breeding and land management strategies will need to evolve to maximize the utilization of finite resources in coming years. High-throughput phenotyping and genomics technologies are providing researchers with the information required to guide and inform the breeding of climate smart crops adapted to the environment. Bioinformatics has a fundamental role to play in integrating and exploiting this fast accumulating wealth of data, through association studies to detect genomic targets underlying key adaptive climate-resilient traits. These data provide tools for breeders to tailor crops to their environment and can be introduced using advanced selection or genome editing methods. To effectively translate research into the field, genomic and phenomic information will need to be integrated into comprehensive clade-specific databases and platforms alongside accessible tools that can be used by breeders to inform the selection of climate adaptive traits. Here we discuss the role of bioinformatics in extracting, analysing, integrating and managing genomic and phenomic data to improve climate resilience in crops, including current, emerging and potential approaches, applications and bottlenecks in the research and breeding pipeline.

Plant pan-genomes are the new reference

Recent years have seen a surge in plant genome sequencing projects and the comparison of multiple related individuals. The high degree of genomic variation observed led to the realization that single reference genomes do not represent the diversity within a species, and led to the expansion of the pan-genome concept. Pan-genomes represent the genomic diversity of a species and includes core genes, found in all individuals, as well as variable genes, which are absent in some individuals. Variable gene annotations often show similarities across plant species, with genes for biotic and abiotic stress commonly enriched within variable gene groups. Here we review the growth of pan-genomics in plants, explore the origins of gene presence and absence variation, and show how pan-genomes can support plant breeding and evolution studies.

Global Role of Crop Genomics in the Face of Climate Change

The development of climate change resilient crops is necessary if we are to meet the challenge of feeding the growing world's population. We must be able to increase food production despite the projected decrease in arable land and unpredictable environmental conditions. This review summarizes the technological and conceptual advances that have the potential to transform plant breeding, help overcome the challenges of climate change, and initiate the next plant breeding revolution. Recent developments in genomics in combination with high-throughput and precision phenotyping facilitate the identification of genes controlling critical agronomic traits. The discovery of these genes can now be paired with genome editing techniques to rapidly develop climate change resilient crops, including plants with better biotic and abiotic stress tolerance and enhanced nutritional value. Utilizing the genetic potential of crop wild relatives (CWRs) enables the domestication of new species and the generation of synthetic polyploids. The high-quality crop plant genome assemblies and annotations provide new, exciting research targets, including long non-coding RNAs (lncRNAs) and cis-regulatory regions. Metagenomic studies give insights into plant-microbiome interactions and guide selection of optimal soils for plant cultivation. Together, all these advances will allow breeders to produce improved, resilient crops in relatively short timeframes meeting the demands of the growing population and changing climate.

Pangenomics Comes of Age: From Bacteria to Plant and Animal Applications

The pangenome refers to a collection of genomic sequence found in the entire species or population rather than in a single individual; the sequence can be core, present in all individuals, or accessory (variable or dispensable), found in a subset of individuals only. While pangenomic studies were first undertaken in bacterial species, developments in genome sequencing and assembly approaches have allowed construction of pangenomes for eukaryotic organisms, fungi, plants, and animals, including two large-scale human pangenome projects. Analysis of the these pangenomes revealed key differences, most likely stemming from divergent evolutionary histories, but also surprising similarities.