Progress in genome sequencing, assembly and analysis allows for a deeper study of agricultural plants’ chromosome structures, gene identif ication and annotation. The published genomes of agricultural plants proved to be a valuable tool for studing gene functions and for marker-assisted and genomic selection. However, large structural genome changes, including gene copy number variations (CNVs) and gene presence/absence variations (PAVs), prevail in crops. These genomic variations play an important role in the functional set of genes and the gene composition in individuals of the same species and provide the genetic determination of the agronomically important crops properties. A high degree of genomic variation observed indicates that single reference genomes do not represent the diversity within a species, leading to the pangenome concept. The pangenome represents information about all genes in a taxon: those that are common to all taxon members and those that are variable and are partially or completely specif ic for particular individuals. Pangenome sequencing and analysis technologies provide a large-scale study of genomic variation and resources for an evolutionary research, functional genomics and crop breeding. This review provides an analysis of agricultural plants’ pangenome studies. Pangenome structural features, methods and programs for bioinformatic analysis of pangenomic data are described.

Long non-coding RNAs (lncRNAs) are RNA molecules longer than 200 nucleotides that do not encode proteins. Experimental studies have shown the diversity and importance of lncRNA functions in plants. To expand knowledge about lncRNAs in other species, computational pipelines that allow for standardised data-processing steps in a mode that does not require user control up until the final result were actively developed recently. These advancements enable wider functionality for lncRNA data identification and analysis. In the present work, we propose the ICAnnoLncRNA pipeline for the automatic identification, classification and annotation of plant lncRNAs in assembled transcriptomic sequences. It uses the LncFinder software for the identification of lncRNAs and allows the adjustment of recognition parameters using genomic data for which lncRNA annotation is available. The pipeline allows the prediction of lncRNA candidates, alignment of lncRNA sequences to the reference genome, filtering of erroneous/noise transcripts and probable transposable elements, lncRNA classification by genome location, comparison with sequences from external databases and analysis of lncRNA structural features and expression. We used transcriptomic sequences from 15 maize libraries assembled by Trinity and Hisat2/StringTie to demonstrate the application of the ICAnnoLncRNA pipeline.

Peach (Prunus persica (L.)) is one of the major commercial stone fruit crops. A genetic analysis of peach collections around the world is essential for effective breeding programmes, and the development of genomic and marker-assisted selection. This study focuses on research on peach collection at the Nikita Botanical Garden and aims to identify single-nucleotide polymorphisms (SNPs) at the genome level and analyse the genetic diversity, population structure, and the linkage disequilibrium (LD) pattern among 161 cultivars and hybrids. A total of 288,784 SNPs were identified using the genotyping-by-sequencing (GBS) approach and, after filtering, 7803 high-quality SNPs were used in the analyses. The 161 accessions were clustered into two groups using principal component analyses (PCoA) and seven populations by ADMIXTURE v.1.3 software, which was confirmed using phylogenetic analyses. The distribution of the genotypes within subpopulations reflected any fruit-related traits. A low level of genetic diversity and medium linkage disequilibrium was detected in peach cultivars. The observed heterozygosity was lower than expected and varied from 0.11 to 0.22 in genotypes with different origins. Our results based on 7803 SNPs were compared with those based on 12 microsatellite markers and differences in clustering, observed heterozygosity, and phylogeny were identified. This highlights the need to analyse collections using whole-genome approaches.

Breeding of cereals with anthocyanin-rich grains is promising for health food production. Here, we transferred genes responsible for activation of anthocyanin biosynthesis in the grain pericarp (Ant1 and Ant2) and aleurone (HvMyc2) to elite barley cultivars Aley, Tanay, and Vorsinsky-2 by marker-assisted backcrossing. Donors were Bowman lines BW648 and BW418. Three pairs of near-isogenic lines (NILs) with purple or blue colored grains were chosen in generation BC₆F₂ and propagated up to BC₆F₆. Genotyping-by-sequencing of resulting NILs and parental lines showed that the NILs carry 4.6–17.6% of donor SNPs including those at target loci. Unexpected big blocks of donor SNPs were revealed in some NILs on chromosomes 1H and 5H that do not carry target loci. The NILs possessed 274% and 12% higher anthocyanin (40.0–170.4 μg/g) and total phenolic content (2367.2–4396.8 μg/g), respectively, compared to original cultivars (18.3–33.1 μg/g and 2319.6–3768.5 μg/g). On average, antioxidant activity was 14% higher, although some lines showed reduced activity. NILs’ productivity depended on growth conditions and was similar to that of the recurrent cultivars. Hence, the applied breeding strategy is an effective approach to enrichment of grain anthocyanin content in barley.

Intraspecific classification of cultivated plants is necessary for the conservation of biological diversity, study of their origin and their phylogeny. The modern cultivated wheat species originated from three wild diploid ancestors as a result of several rounds of genome doubling and are represented by di-, tetra- and hexaploid species. The identification of wheat ploidy level is one of the main stages of their taxonomy. Such classification is possible based on visual analysis of the wheat spike traits. The aim of this study is to investigate the morphological characteristics of spikes for hexa- and tetraploid wheat species based on the method of high-performance phenotyping. Phenotyping of the quantitative characteristics of the spike of 17 wheat species (595 plants, 3348 images), including eight tetraploids (Triticum aethiopicum, T. dicoccoides, T. dicoccum, T. durum, T. militinae, T. polonicum, T. timopheevii, and T. turgidum) and nine hexaploids (T. compactum, T. aestivum, i:ANK-23 (near-isogenic line of T. aestivum cv. Novosibirskaya 67), T. antiquorum, T. spelta (including cv. Rother Sommer Kolben), T. petropavlovskyi, T. yunnanense, T. macha, T. sphaerococcum, and T. vavilovii), was performed. Wheat spike morphology was described on the basis of nine quantitative traits including shape, size and awns area of the spike. The traits were obtained as a result of image analysis using the WERecognizer program. A cluster analysis of plants according to the characteristics of the spike shape and comparison of their distributions in tetraploid and hexaploid species showed a higher variability of traits in hexaploid species compared to tetraploid ones. At the same time, the species themselves form two clusters in the visual characteristics of the spike. One type is predominantly hexaploid species (with the exception of one tetraploid, T. dicoccoides). The other group includes tetraploid ones (with the exception of three hexaploid ones, T. compactum, T. antiquorum, T. sphaerococcum, and i:ANK-23). Thus, it has been shown that the morphological characteristics of spikes for hexaploid and tetraploid wheat species, obtained on the basis of computer analysis of images, include differences, which are further used to develop methods for plant classifications by ploidy level and their species in an automatic mode.

The development of next-generation sequencing technologies has provided new opportunities for genotyping various organisms, including plants. Genotyping by sequencing (GBS) is used to identify genetic variability more rapidly, and is more cost-effective than whole-genome sequencing. GBS has demonstrated its reliability and flexibility for a number of plant species and populations. It has been applied to genetic mapping, molecular marker discovery, genomic selection, genetic diversity studies, variety identification, conservation biology and evolutio nary studies. However, reduction in sequencing time and cost has led to the need to develop efficient bioinformatics analyses for an ever-expanding amount of sequenced data. Bioinformatics pipelines for GBS data analysis serve the purpose. Due to the similarity of data processing steps, existing pipelines are mainly characterised by a combination of software packages specifically selected either to process data for certain organisms or to process data from any organisms. However, despite the usage of efficient software packages, these pipelines have some disadvantages. For example, there is a lack of process automation (in some pipelines, each step must be started manually), which significantly reduces the performance of the analysis. In the majority of pipelines, there is no possibility of automatic installation of all necessary software packages; for most of them, it is also impossible to switch off unnecessary or completed steps. In the present work, we have developed a GBS-DP bioinformatics pipeline for GBS data analysis. The pipeline can be applied for various species. The pipeline is implemented using the Snakemake workflow engine. This implementation allows fully automating the process of calculation and installation of the necessary software packages. Our pipeline is able to perform analysis of large datasets (more than 400 samples).