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Wang, M.; Li, R.; Zhao, Q. Applications of Omics Technologies in Forest Plants. Encyclopedia. Available online: https://encyclopedia.pub/entry/45503 (accessed on 17 June 2024).
Wang M, Li R, Zhao Q. Applications of Omics Technologies in Forest Plants. Encyclopedia. Available at: https://encyclopedia.pub/entry/45503. Accessed June 17, 2024.
Wang, Mingcheng, Rui Li, Qi Zhao. "Applications of Omics Technologies in Forest Plants" Encyclopedia, https://encyclopedia.pub/entry/45503 (accessed June 17, 2024).
Wang, M., Li, R., & Zhao, Q. (2023, June 13). Applications of Omics Technologies in Forest Plants. In Encyclopedia. https://encyclopedia.pub/entry/45503
Wang, Mingcheng, et al. "Applications of Omics Technologies in Forest Plants." Encyclopedia. Web. 13 June, 2023.
Applications of Omics Technologies in Forest Plants
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The ecological and economic values of forest plants have been gradually recognized worldwide. However, the growing global demand for new forest plant varieties with higher wood production capacity and better stress tolerance cannot be satisfied by conventional phenotype-based breeding, marker-assisted selection, and genomic selection.

genetic breeding high-throughput omics multi-omics integration

1. Genomics

The genome sequence of the model forest plant species, black cottonwood (Populus trichocarpa), was published in 2006 [1] and was recently updated to version 4.1 (Phytozome 13, release date: 5 October 2022) with significantly improved accuracy and continuity compared to the initial version. The release of the black cottonwood genome spawned research in the functional genomics of forest trees, especially angiosperms. However, conifers (gymnosperms), which dominate many temperate and boreal forests, have significantly different functional genomic characteristics from angiosperms, with unusually large genome sizes ranging from 18 to 35 gigabytes (Gb) [2]. In 2013, the first conifer genome belonging to Norway spruce (Picea abies) was published [3]. It was 4.3 Gb, only one-fifth of the Norway spruce actual genome size (19.6 Gb). However, the advent and development of next-generation sequencing (NGS) and third-generation sequencing (TGS) have greatly advanced the capacity to decode genomes of a wide range of forest plants. The emerging long-read and ultra-long-read sequencing and advanced assembly algorithms have provided useful tools for assembling complex plant genomes with high repeat content or heterozygosity [4][5]. As a result, the difficulty of assembling a complete or near-complete genome of forest plants has been significantly alleviated in recent years [6]. To date, several high-quality forest plant genomes have been published, including gymnosperm species with genome sizes larger than 10 Gb, such as Ginkgo biloba [7], Taxus chinensis [8], T. wallichiana [9], Larix kaempferi [10], and Pinus tabuliformis [11].
A well-annotated reference-level genome provides a clear road map for downstream gene function and diversity studies, allowing an in-depth interpretation of genotype–phenotype relationships and functional DNA elements involved in various biological processes [12]. For example, the release of the black cottonwood genome has enabled studies on the genome-wide identification of regulatory genes and non-coding RNAs (ncRNAs) involved in several important biological processes, such as wood formation [13][14], annual growth cycle [15][16], flowering processes [17][18], and responses to abiotic stresses [19][20][21] in Populus species. Moreover, a comprehensive phylogenetic analysis of functional elements based on the genomes of several model plant species, including poplar, rice, and Arabidopsis has been performed. A comparative genomic analysis among the different model species provides insights into the duplication history, selection pressure, and structural divergence of functional genes from an evolutionary aspect [22][23]. Therefore, the whole-genome-scale discovery and cross-species comparison of insertion and distribution of transposable elements, especially the long terminal repeat retrotransposons, could help further investigate their effects on the adjacent gene expression and plant phenotype [24][25]. Overall, the accumulation of high-quality genome sequences will provide a comprehensive understanding of the contribution of functional DNA elements to phenotypic variation among forest plants.
Many forest plant species have a wide geographical distribution, large population size, and high genetic diversity at local and regional scales [26]. Thus, a single reference genome cannot simply represent the DNA sequence diversity within a species. However, population genomics studies using whole genome resequencing (WGRS) or reduced-representation genome sequencing (RRGS) can provide insights into the genetic diversity of forest plants at the SNPs level. SNPs can be detected by mapping reads to a reference genome and subsequent variant calling based on WGRS at the population level. The detected SNPs allow further genome-wide polymorphism analysis during adaptive population divergence. For example, the WGRS of 427 moso bamboos (Phyllostachys edulis) from multiple representative geographic regions and subsequent population genomic analysis revealed several candidate genes under balancing selection or related to several agriculturally important traits, such as clear culm height, node number, density, and compressive strength [27]. In addition, several candidate genes related to light response, growth-promoting cytokinin, and wood development were identified by genome sequencing and WGRS of 80 silver birch (Betula pendula) with clear evidence of recent natural selection [28]. However, despite the continuously decreasing sequencing cost, the WGRS of many plant samples/species is still expensive. As an alternative or complementary approach to WGRS, RRGS consisting of reduced-representation libraries and restriction-site-associated DNA sequencing has been developed by integrating restriction enzymes into high-throughput sequencing to obtain a reduced genome representation [29]. Compared to WGRS, RRGS has apparent advantages of high efficiency, low cost, and does not require a reference genome [29]. RRGS enables genome-wide SNP discovery for non-model species lacking genome sequence information or species with large and complex genomes [30][31]. Furthermore, the genetic linkage map can be constructed using RRGS data. Subsequently, QTL mapping and genome-wide association analysis (GWAS) can be applied to identify the phenotype–genotype relationships across genomes of forest plants [32][33][34]. However, RRGS can also result in missing information possibly related to population differentiation, limiting its application scope.
WGRS and RRGS easily detect SNPs and short insertions and deletions. However, structural variations (SVs), including the presence/absence variants (PAVs), copy number variants, and chromosomal rearrangements, are rarely detected by short-read sequencing [35]. SVs genetically control the phenotypic variability within and between plant species [36][37]. Recent advances in long-read sequencing have enabled the generation of high-quality assemblies for several individuals per species across many plant species, providing a solid foundation for accurately identifying SVs by pan-genomic analysis at the species level [35][38]. Therefore, a pan-genome represents a more comprehensive DNA sequence diversity of a plant species or taxonomic group. As a result, pan-genomic studies have been carried out to comprehensively understand the genetic diversity of several model plant species or economically important crops, including Arabidopsis, barley, rice, tomato, and soybean [39]. The release of these pan-genomes enabled more accurate associate mapping and population genomic analysis, identifying more potential genomic variants related to complex agronomic traits [34]. Population SVs identified in the pan-genomic studies also improved the GS-based prediction accuracy by partially rescuing the “missing heritability” of complex traits [34]. Furthermore, the pan-genomes provide a comprehensive list of candidate genes for direct genome editing, greatly accelerating the precision breeding of these crops. However, pan-genome research has been conducted on very few forest plant species, including poplar [40] and pecan [41]. Given the universality of sequencing technologies, assembly algorithms, and pan-genomic analysis pipelines, pan-genome will soon become a routine analysis tool for mining genetic variation and functional DNA elements of forest plant species.
In addition to genome sequences, population genomics, and pan-genomics, the three-dimensional genome structure, chloroplast genome, and mitochondrial genome are also useful for gene discovery and genetic engineering of plants [42][43][44]. With the increase in different genomic resources, integrating the existing genomic data will facilitate an efficient and accurate functional element identification of forest plants, which is beneficial in linking all DNA variations to phenotypes and designing molecular markers for further breeding processes. Genomic data from different sources have been submitted to publicly available databases, such as the National Center of Biotechnology Information, Ensembl, CoGe, GigaDB, and BIG Data Center. The development of a web-based, comprehensive genomic database, similar to BRAD [45] and Gramene [46], could also largely accelerate the genomic data integration for the genetic breeding of forest plant species. Currently, the applications of genomic technologies in forest plant breeding are limited by the complexity of polyploid genomes and the lack of genomic resources in some understudied species [38].

2. Transcriptomics

Transcriptomics is one of the most commonly used omics approaches in plant biology research. It involves studying the transcriptome, the complete set of transcripts generated by a cell or tissue [47]. Understanding the transcriptome is crucial in elucidating the structural and functional organization of the genome [48]. Several hybridization- and sequence-based approaches [48], including microarray, expressed sequence tag sequencing, serial analysis of gene expression, and RNA sequencing (RNA-seq), have been developed for transcriptome profiling. Among these approaches, RNA-seq, which captures all transcripts by high-throughput sequencing, is a revolutionary tool for accurate high-resolution transcriptome analysis [49]. For example, NGS-based RNA-seq generates millions of short reads, ranging between 25 and 300 bp in length.
Many computational tools have been developed to interpret the RNA-seq short-reads data. For example, a transcriptome assembly was reconstructed by aligning RNA-seq reads to a known genome assembly using assemblers such as Cufflinks [50], StringTie [51], and Scripture [52]. Furthermore, RNA-seq reads have been de novo assembled into transcripts even without a reference genome using assemblers such as Trinity [53], SOAPdenovo-Trans [54], and Oases-Velvet [55]. These reference-based or de novo strategies have been successfully applied in constructing a reference transcriptome with RNA-seq reads for many plant species. However, although NGS short reads have high accuracy, they rarely span multiple exons. Furthermore, assembling NGS short reads into full-length transcripts is complicated by alternative splicing events frequently occurring in the genome [56]. Luckily, this challenge is alleviated by TGS-based transcriptome sequencing approaches, such as full-length isoform sequencing and nanopore-based direct RNA sequencing, which allows the direct sequencing of full-length transcripts without assembly [57][58]. However, given the relatively high error rate of TGS reads, highly accurate NGS RNA-seq reads are required to improve TGS-based transcriptome assembly accuracy [59]. At present, the high-quality and full-length transcriptome of forest plant species, including Larix kaempferi [60], Chosenia arbutifolia [61], Fritillaria cirrhosa [62], and Alsophila spinulosa [63] has been obtained.
The reference-level transcriptome assembly is crucial for the downstream analysis of gene expressions under multiple conditions. The gene expression levels are usually evaluated based on the RNA-seq reads. By mapping the RNA-seq reads to a reference transcriptome or genome, the number of reads matching each gene and gene expression levels are quantified by normalizing the read counts using algorithms, such as fragments per kilobase of mapped reads, transcripts per million and counts per million [64]. Subsequently, the differential expression (DE) analysis is performed using either non-parametric or parametric tools, such as DESeq2 [65], edgeR [66], and SAMseq [67]. DE analysis is widely used to analyze the forest plant responses to biotic and abiotic stresses, including drought, heat, salinity, flooding, cold, ultraviolet radiation, diseases, and insects [68][69]. For instance, the gene expression analysis in poplar root under polyethylene glycol-induced drought stress by TGS and NGS transcriptomic sequencing revealed several differentially expressed genes (DEGs) related to plant responses to drought in the biosynthesis and metabolism pathways [70]. In addition, the dual RNA-seq analysis of the interactions between Norway spruce and Heterobasidion revealed that several genes involved in the abscisic acid signaling were differentially expressed in Norway spruce, which might contribute to the Norway spruce response to the pathogen attack [71]. DE analysis also outlines the transcriptome dynamics across different tissues and plant developmental stages [72]. Notably, DE analysis under multiple conditions may identify too many or too few DEGs, requiring the analysts to integrate biological data from other sources or change the software parameters.
The gene expression data can also be used to construct the gene co-expression network (GCN), a powerful tool for the further elucidation of the gene regulatory relationships and identification of candidate functional genes [73]. The weighted gene co-expression network analysis (WGCNA) is a widely used pipeline to construct the GCN by clustering genes into modules based on their expression patterns and hub genes within each module [74]. The gene co-expression analysis has been successfully applied in detecting key functional genes regulating various biological processes in forest plant species, such as Pinus tabuliformis [75], Populus trichocarpa [76], Hevea brasiliensis [77], and Zanthoxylum armatum [78]. As a result, the cross-species GCN comparison effectively deduces the origin of new phenotypes and conserved gene functions at the species level [79].
With the advances in RNA-seq and tissue processing approaches, single-cell RNA-seq (scRNA-seq) has become a revolutionary tool for studying plant functional genomics at the cellular level [80]. scRNA-seq can analyze any tissue in any plant. This emerging approach obtains the transcripts of thousands of cells per sample, providing new insights into gene expression heterogeneity across cells. Based on the scRNA-seq data, cells can be clustered into different categories using dimensionality reduction and clustering, which allows for the reconstruction of the cell differentiation trajectories [80]. Distinct expression patterns of different cell clusters provide further insights into the gene regulatory networks and candidate genes related to plant organ development. For instance, the scRNA-seq of 6796 poplar stem cells predicted the cell differentiation trajectories involved in phloem and xylem development and candidate genes related to vascular development in poplars [81]. Another newly developed technique, spatial transcriptomics, quantifies and localizes gene expression within the tissue by combing histological imaging and RNA sequencing [82]. Its derivative, spatial single-cell transcriptomics, also recently developed, integrates the spatial information from spatial transcriptomics and cellular gene expression based on cRNA-seq, clearly elucidating the complex spatial gene regulatory networks related to plant organ development [83]. Despite its high costs, spatial single-cell transcriptomics has great potential in the precise breeding of forest plants.
Moreover, various transcriptomic technologies are beneficial for the deep mining of candidate resistance- and biosynthesis-related genes in forest plants. Subsequently, the functionally validated candidate genes can be used in developing new forest plant varieties with improved stress tolerance and increased bioactive compound contents. For example, a series of transcriptomic studies have revealed the important roles of several genes in poplar response to salt tolerance [84][85][86], which can be further applied in the transgenic breeding and phenotypic selection of salt-tolerant poplars. Although the price of transcriptome sequencing has significantly dropped, the need for fresh samples and complex stress response mechanisms have limited the application of transcriptomics in forest plants.

3. Epigenomics

Epigenomics studies the epigenome, which consists of the biochemical modifications in the nuclear DNA, histone proteins, and ncRNAs [87]. Although these epigenomic modifications do not alter the nucleotide sequences, they are inherited across generations through mitosis or imprinting. Epigenetic changes, such as DNA methylation, histone modifications and variants, and ncRNA regulation, are frequently induced by environmental stresses or endogenous signals during plant development, which alters the chromatin structure and gene expression [88]. As a result, epigenetics is a powerful driving force in the environmental adaptation of plants by altering their phenotypic plasticity [87]. Therefore, epigenomic studies can provide important insights into the epigenetic basis underlying complex phenotypes and the local adaptation of forest plants, which cannot be deduced using DNA sequence variants.
The availability of high-quality reference genomes facilitates the genome-wide detection of epigenetic variants at the single-nucleotide level in forest plant species [89]. DNA methylation has been extensively studied in plants by detecting base modifications using bisulfite or long-read sequencing [90]. In plants, DNA methylation occurs as CG, CHG, and CHH in gene bodies and transposable elements. More importantly, the methylation levels substantially vary across plant species, tissues, and cells [91]. In addition, DNA methylation is a highly dynamic process during plant growth and development, including the establishment, maintenance, and active removal of methylation sites [91]. The dynamics of DNA methylation play important roles in the epigenetic regulation of plant growth, development, and response to environmental stresses [92]. For instance, several studies have revealed the potential role of DNA methylation in flower development [93], drought tolerance [94], wood formation [95], and immune response to pathogen infection [96] in Populus species. Moreover, the recently developed single-cell methylation profiling approaches have allowed the tracing of DNA methylation dynamics at the single-cell level [97].
In addition to the DNA methylation marks, histone marks, including histone modifications and variants, also transcriptionally silence or activate genes [98][99]. Histone modifications include histone methylation, acetylation, phosphorylation, ubiquitylation, sumoylation, and the reversible amino acid modifications at the N-terminal tail of histone proteins within the nucleosome core [88]. Among these modifications, histone methylation and acetylation are the most studied modifications regulating plant development and environmental stress response [100][101]. Histone variants are sequence variants of core histones H2A, H2B, H3, and H4, which regulate nucleosome structure and function [102]. Histone marks are detected by DNA/RNA-protein interactions across the genome using chromatin immunoprecipitation sequencing or the transposase-accessible chromatin assay with high-throughput sequencing [103].
Furthermore, regulatory ncRNAs, including the long non-coding and small RNAs, are important epigenetic marks with diverse functions in response to abiotic stress in forest plants [104]. For instance, several micro RNAs are differentially expressed during Norway spruce embryo development, potentially contributing to epigenetic memory and climatic adaptation [105]. Overall, the functional analysis of different epigenetic marks has extended the scope of plant biology.
Understanding the epigenetic mechanisms and variants is beneficial for the epigenetic improvement of forest plants. Naturally or artificially induced epigenetic variants serve as a novel genetic resource for plant epi-breeding [88]. Since the naturally occurring epigenetic variations are greatly limited, several laboratory-based approaches, including chemical treatment, biotic and abiotic stress treatment, tissue culture, grafting, RNAi, and direct epigenome editing by CRISPR/Cas9 have been applied to manually modify the plant epigenome [106]. These artificial methods are powerful tools in epi-breeding programs, which induce a wider range of phenotypic variation while increasing the transgenerationally inherited epialleles. Naturally and artificially induced epialleles can be employed as epigenetic markers in quantitative epigenetics based on the epigenetic quantitative trait loci and epigenome-wide association, which are important steps at the early stage of epi-breeding [107]. Moreover, epigenome editing methods using advanced CRISPR/Cas9 or CRISPR off technologies directly increase the stress resilience of plants through epigenome engineering [108][109]. In recent years, quantitative epigenetics and epigenome engineering have been successfully applied in the epigenetic breeding of crop plants, including rice, tomato, potato, and soybean [110][111][112][113]. For example, Yu et al. (2021) modulated the plant RNA m6A methylation using a transgenic approach, which significantly increased the rice and potato yield and biomass [112]. Compared to crops, the epigenetic mechanisms and variants in forest plants are relatively understudied. With an increased knowledge of the epigenetics of forest plants, epigenetic breeding will play an important role in improving more complex traits, promoting the forest plants’ adaptation to the changing climate. However, given the complexity of the epigenetic mechanisms, the rapid development of new epigenetic markers, epigenome editing tools with improved efficiency, and deep epigenome sequencing of non-model forest plants are needed.

4. Proteomics

Proteins are large biological molecules, the main undertakers of life activities, and are important components of plant cells and tissues, which form the physical basis of life. Proteomics studies the proteome, including the composition, localizations, modifications, and interactions of all the proteins expressed in a genome [114]. The plant proteome significantly varies across the cells and under different developmental and environmental conditions [115]. In addition, most eukaryotic proteins undergo post-translational modifications, altering protein expressions and functions [116]. Thus, proteomics is a powerful omics tool for comprehensively understanding the biological processes in the post-genomic era [117]. Genome sequencing provides DNA sequences of protein-coding genes, laying a solid foundation for proteomics research. The proteome contains much more complex functional gene information than that provided by the genome [118]. As a result, the accurate identification and quantification of the complete proteome are highly challenging. So far, several sequencing technologies have been developed for proteomics-based analysis, including protein microarrays, gel-based approaches, quantitative approaches (isobaric tags for absolute and relative quantification (iTRAQ), isotope-coded affinity tag, and stable isotope labeling with amino acids), and high-throughput approaches (mass spectrometry and nuclear magnetic resonance spectroscopy) [114]. Among these approaches, liquid chromatography with tandem-mass spectrometry and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry is widely used to monitor plant proteome dynamics. A comprehensive plant proteome profiling provides valuable insights into the molecular mechanisms underlying plant growth, development, and stress responses [115]. For example, the tandem mass tag-based proteome sequencing of Masson pine (Pinus massoniana) with different resin yields revealed several differentially expressed proteins related to resinosis [119]. In addition, the Picea asperata somatic embryo proteome profiling using iTRAQ and comparative proteomics analysis under partial desiccation treatment provided novel insights into stress-related proteins and metabolic pathways in P. asperata [120].
Proteomics also identifies the candidate proteins underlying complex traits by linking protein expression to genetic maps through QTL analysis [121]. The identified proteins serve as powerful biomarkers for the precision breeding of quantitative traits in plants [122]. Currently, proteomics and associated QTL mapping have successfully identified functional proteins and genes related to production and stress tolerance in several crops [123]. For instance, the large-scale proteome sequencing of 102 barley genotypes revealed drought-sensitive proteins in the different genotypes [124]. A further genetic linkage analysis of these proteins identified several proteomic QTLs (pQTLs) with potential breeding value for drought-tolerant barley. In addition, the label-free proteome sequencing of 148 recombinant inbred lines of pepper (Capsicum annuum) revealed several candidate hotspot regions encoding functional proteins related to fruit development through pQTL analysis [125]. The identified proteins could serve as stable markers for further breeding processes.
However, despite its great potential in plant biology research and genetic breeding, proteomics is limited by several challenges compared to genomic and transcriptomic approaches [126]. First, the identification and quantification of the whole proteome are still challenging due to the limitations of the different proteome sequencing methods. Second, the precision and reproducibility of proteome sequencing and existing proteomics pipelines are unsatisfactory. Third, deciphering the complex proteomic networks is still challenging since proteomics is far more complex than genomics. To date, proteomics has mainly served as an ancillary strategy for functional studies in the system biology of plants. However, the large-scale applications of plant proteomics are still a long way off [127].

6. Metabolomics

Metabolomics is an emerging post-genomics tool for comprehensive qualitative and quantitative studies of small-molecule metabolites with molar masses below 1000 in the cells or tissues [128]. Plants produce various metabolites, including primary and secondary metabolites. Primary metabolites are essential for plant growth and development, while secondary metabolites play a major role in the plant responses to environmental factors [129]. Metabolites are the end products of gene transcription and protein expression within an organism and act as links between genotypes and phenotypes [130]. Among the omics tools, metabolomics has the closest relationship with the phenotype [131]. Based on this, metabolomics has become an increasingly popular system biology tool for deciphering plant science [132].
The plant metabolome is highly dynamic and complex, with many small-molecule metabolites of diverse structures and content, making accurate metabolome profiling challenging [133]. To date, a few analytical techniques have been developed for high-throughput quantitative metabolomics, including nuclear magnetic resonance, liquid chromatography-mass spectrometry, capillary electrophoresis-mass spectrometry, and gas chromatography-mass spectrometry [134].
Metabolomics is classified into targeted and untargeted based on the study subject [135]. Targeted metabolomics performs a target analysis of known metabolites with high sensitivity and accuracy, revealing the fluctuations in specific metabolic pathways. In contrast, untargeted metabolomics performs a non-biased detection of all metabolites while identifying the differential metabolites with significant changes for further screening analysis. Similar to other omics, single-cell sequencing can be integrated into metabolomics to unravel the cellular metabolism dynamics under environmental changes [136]. Given the metabolic complexity in plants and the limitations in each analytical platform/method, combined approaches are increasingly employed in plant metabolome profiling studies.
The variation in metabolites modulates diverse biological processes and may alter plant phenotypes [137]. As a result, transcriptomics and metabolomics-based correlation analyses have enabled the genome-wide discovery of key genes controlling known and new metabolic pathways [138]. This strategy has been regularly applied in forest plants, including Zanthoxylum armatum [78], Phyllostachys edulis [139], Populus tomentosa [140], and Hevea brasiliensis [141]. Given that most metabolic traits are heritable across generations [142], combining metabolomics with QTL and GWAS can establish direct links between metabolites and phenotypes based on large-scale population metabolomic and phenotypic data [132]. To date, most metabolome QTL (mQTL) and metabolome-based GWAS (mGWAS) have been applied in hunting genes related to metabolic traits in crop plants [132], with a few success cases in poplars and apple trees. For example, a mQTL analysis of the untargeted metabolic profiling data and genetic linkage maps revealed mQTL hotspots with many peel- and flesh-related metabolites [143]. In addition, the mGWAS analysis for flavonoid features in Popuous tomentosa using targeted metabolomics data revealed that more than 1500 significant associations accounted for phenotypic variation [144]. Thus, the metabolic profiling of plant species can provide key functional gene information on metabolite markers linked to specific phenotypes and downstream molecular marker design [132].
Metabolic markers can be identified using metabolic profiling data under various stress conditions. The identified metabolite markers can efficiently guide further plant breeding processes for genetic improvements through direct metabolic engineering [145]. For example, the identified metabolites can help plant breeders accurately identify stress levels in plants [132]. In particular, metabolomics efficiently classified drought-tolerant eucalyptus clones into drought sensitive or tolerant based on their metabolite levels [146]. However, although metabolomics has proven to be an effective tool in plant genetic breeding programs, a single approach to identifying and quantifying all metabolites within a plant species is still lacking [134]. Therefore, future advances in metabolomics approaches should focus on more interesting gene and metabolic pathways beneficial for further plant breeding.

7. Other Omics

Molecular sequencing data and phenotypic observations are essential for the accurate interpretation of genetic architecture underlying complex traits. Plant phenomics is an emerging, interdisciplinary technique that collects high-throughput phenotypic data at an organism-wide scale using cutting-edge optical imaging techniques [147]. This image-based approach performs automated monitoring, acquisition, and processing of high-dimensional and resolution phenotypic data under various developmental and environmental conditions, with low labor consumption and error rate, unlike the traditional manual measurements. For example, unmanned aerial vehicle imagery has been used to monitor the dynamics of needle physiological traits in slash pine (Pinus elliottii) [148] and leaf-level, drought-related phenotypes in Populus nigra [149] at the population scale. The obtained standardized and highly accurate phenotypic data are useful for identifying effective genotype–phenotype relationships through QTL or GWAS analysis, which provides key information for further breeding processes.
Environmental factors play important roles in influencing plant phenotypes through genotype–environment interactions. Therefore, envirotyping for micro- or macro-environmental factors is beneficial for understanding how the complex network of environmental cues, including climate, soil, biotic, and crop management, influences plant growth and development [150]. Phenotypes are predicted more precisely by integrating spatiotemporal genotype, phenotype, and envirotype data into a training model, particularly in a changing climate [151]. Therefore, the recently developed enviromics has great potential in the smart breeding of new forest plant varieties.
In addition, plant ionomics, which studies metal, non-metal, and metalloid compositions in plants through high-throughput elemental profiling [152], can help identify genes related to plant development and stress resilience [153]. Recently, the plant microbiome was found to alter plant phenotypes by influencing their disease resistance, abiotic stress tolerance, and growth promotion [154].

8. Multi-Omics Integration

Genomics is the most used omics discipline. The whole-genome DNA sequence informs the basic properties of a plant species but cannot solely determine the final phenotype. At the same time, not all DNA sequence variants lead to phenotypic variation. Instead, phenotypic plasticity is shaped by many molecular mechanisms, including epigenetic modification, gene expression and silencing, post-translational protein modification, and metabolite accumulation. Therefore, a single omics cannot sufficiently and comprehensively unravel the complex biological regulatory networks controlling the various phenotypic traits [155].
The continuous and rapid progress in developing various high-throughput omics technologies has facilitated the integration of different omics data for plant system biology studies [156]. Transcriptomics, proteomics, and metabolomics are the most frequently used omics technologies in the multi-omics integration (MOI) studies of plants, as they are the core of system biology [156]. MOI studies are accelerated by genomic information provided by well-annotated genomes and associated genomic analysis epigenomics and other omics approaches. For example, since DNA methylation alters gene expression, transcriptome analysis is combined with methylation analysis to unravel the functional relationship between the epigenome and transcriptome [90]. In particular, Balmant et al. (2020) integrated genomics, transcriptomics, and phenomics into system genetics frameworks to identify the key regulators related to lignin biosynthesis in Populus deltoides [157]. Generally, MOI analysis mainly involves the establishment of associations between datasets from different omics [158]. However, multiple omics platforms produce a large amount of high-throughput data, greatly challenging the subsequent MOI analysis [159]. Therefore, analysts require a good understanding of the formats and characteristics of various omics data sources and a good background in software operations, statistical modeling, and data interpretation.
The most simple and intuitive analysis strategy in MOI studies is the correlation analysis between two or more omics datasets using various models, such as Pearson, Spearman, and Kendall rank correlation analyses [160]. These correlation analyses can be applied to differentially expressed or specific biochemical pathway-related transcripts, proteins, and metabolites. However, various biological factors along with experimental errors, may cause weak correlations between different omics datasets [156][161]. For example, transcriptome and proteome sequencing of Quercus ilex under severe drought conditions recorded a poor correlation (r = 0.11) between mRNA and protein [162]. Such inconsistencies are alleviated by further sequencing and analysis.
Another strategy for analyzing MOI-related data is clustering analysis based on the similarity of various omics data using hierarchical or partition clustering methods [163]. Several statistical approaches, including similarity matrices, canonical correlation and co-inertia analysis, and matrix factorization, have been successfully applied in grouping forest plant multi-omics data [163]. For instance, Pascual et al. (2017) used a k-means clustering approach to integrate proteomic, metabolomic, and physiological data of Pinus radiata based on their quantitative trends during different periods of ultra-violet treatment, obtaining 30 clusters [164]. Further analysis can correlate the clustering results with specific scientific questions. Moreover, multivariate-based analysis has enabled the integration of multi-omics data using multi-variant data analysis approaches, such as principal components analysis, partial least squares, and orthogonal projections to latent structures (OPLS) [165]. For example, the OPLS analysis of the transcriptome, proteome, and metabolome data from transgenic poplars identified several proteins related to wood formation [166].
A common weakness of correlation, clustering, and multivariate-based analyses is that they are based on statistical methods rather than prior knowledge of molecular mechanisms. However, bioinformatics analysts have developed several pathway-based approaches, such as pathway mapping and co-expression analyses, to integrate known pathway information into MOI analysis [156]. Pathway mapping analysis maps various omics datasets against publicly available pathway databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaCyc [167]. For example, López-Hidalgo et al. (2018) reconstructed and visualized 123 of the 127 known KEGG pathways in Quercus ilex at the transcriptome, proteome, and metabolome levels using MapMan software [168]. At the same time, multiple omics datasets could be employed to annotate certain plant metabolic pathways using known pathway information as the reference. For example, Wang et al. (2021) reconstructed the biosynthesis of two alkaloids, sanshools and wgx-50, using transcriptome and metabolome data [78].
Alternatively, the co-expression analysis can be integrated into existing pathway databases. The WGCNA approach, the most popular gene co-expression analysis tool, can detect regulatory networks for each omics layer, construct a consensus correlation network [169][170], and identify hub elements, such as hub genes, proteins, and metabolites. Furthermore, the multi-omics WGCNA approach can perform efficient gene and module clustering and provide key regulatory network information through MOI.
However, neither statistical-based nor pathway-based MOI methods are independent. Instead, these methods are often combined to answer biological questions precisely. Several complementary MOI approaches exist, such as top-down differential analysis and bottom-up genome-scale modeling [156]. These MOI approaches have been widely used in the in-depth analysis of complex metabolic pathways and other biological processes in forest plants. In particular, MOI studies provide deep insights into the genotype–phenotype–environment interactions in forest plants, essential to reconstruct complex prediction models for precision breeding [171]. Moreover, MOI studies provide a comprehensive list of functional genes and pathways for further experimental validation analysis, accelerating genome/epigenome editing for the generation of new forest plant varieties. However, the heterogeneity in signal-to-noise ratio across multiple omics layers, the adverse effects of missing values, the limitation in the interpretation ability of multi-omics models, and data sharing difficulties, such as metadata annotation, data storage, and computing resources, remain universal challenges across all-pervading MOI studies [159]. Nonetheless, MOI analysis is a powerful tool for genome-wide functional element identification and forest plant breeding. Furthermore, the development of single-cell sequencing technology provides an exciting opportunity for single-cell MOI analysis.

References

  1. Tuskan, G.A.; Difazio, S.; Jansson, S.; Bohlmann, J.; Grigoriev, I.; Hellsten, U.; Putnam, N.; Ralph, S.; Rombauts, S.; Salamov, A.; et al. The genome of black cottonwood, Populus trichocarpa (Torr. & Gray). Science 2006, 313, 1596–1604.
  2. Mackay, J.; Dean, J.F.; Plomion, C.; Peterson, D.G.; Cánovas, F.M.; Pavy, N.; Ingvarsson, P.K.; Savolainen, O.; Guevara, M.A.; Fluch, S.; et al. Towards decoding the conifer giga-genome. Plant Mol. Biol. 2012, 80, 555–569.
  3. Nystedt, B.; Street, N.R.; Wetterbom, A.; Zuccolo, A.; Lin, Y.C.; Scofield, D.G.; Vezzi, F.; Delhomme, N.; Giacomello, S.; Alexeyenko, A.; et al. The Norway spruce genome sequence and conifer genome evolution. Nature 2013, 497, 579–584.
  4. Jung, H.; Winefield, C.; Bombarely, A.; Prentis, P.; Waterhouse, P. Tools and strategies for long-read sequencing and de novo assembly of plant genomes. Trends Plant Sci. 2019, 24, 700–724.
  5. Michael, T.P.; VanBuren, R. Building near-complete plant genomes. Curr. Opin. Plant Biol. 2020, 54, 26–33.
  6. Sun, Y.; Shang, L.; Zhu, Q.H.; Fan, L.; Guo, L. Twenty years of plant genome sequencing: Achievements and challenges. Trends Plant Sci. 2022, 27, 391–401.
  7. Liu, H.; Wang, X.; Wang, G.; Cui, P.; Wu, S.; Ai, C.; Hu, N.; Li, A.; He, B.; Shao, X.; et al. The nearly complete genome of Ginkgo biloba illuminates gymnosperm evolution. Nat. Plants 2021, 7, 748–756.
  8. Xiong, X.; Gou, J.; Liao, Q.; Li, Y.; Zhou, Q.; Bi, G.; Li, C.; Du, R.; Wang, X.; Sun, T.; et al. The Taxus genome provides insights into paclitaxel biosynthesis. Nat. Plants 2021, 7, 1026–1036.
  9. Cheng, J.; Wang, X.; Liu, X.; Zhu, X.; Li, Z.; Chu, H.; Wang, Q.; Lou, Q.; Cai, B.; Yang, Y.; et al. Chromosome-level genome of Himalayan yew provides insights into the origin and evolution of the paclitaxel biosynthetic pathway. Mol. Plant 2021, 14, 1199–1209.
  10. Sun, C.; Xie, Y.H.; Li, Z.; Liu, Y.J.; Sun, X.M.; Li, J.J.; Quan, W.P.; Zeng, Q.Y.; Van de Peer, Y.; Zhang, S.G. The Larix kaempferi genome reveals new insights into wood properties. J. Integr. Plant Biol. 2022, 64, 1364–1373.
  11. Niu, S.; Li, J.; Bo, W.; Yang, W.; Zuccolo, A.; Giacomello, S.; Chen, X.; Han, F.; Yang, J.; Song, Y.; et al. The Chinese pine genome and methylome unveil key features of conifer evolution. Cell 2022, 185, 204–217.
  12. Kersey, P.J. Plant genome sequences: Past, present, future. Curr. Opin. Plant Biol. 2019, 48, 1–8.
  13. Chen, H.; Wang, J.P.; Liu, H.; Li, H.; Lin, Y.C.J.; Shi, R.; Yang, C.; Gao, J.; Zhou, C.; Li, Q.; et al. Hierarchical transcription factor and chromatin binding network for wood formation in Populus trichocarpa. Plant Cell 2019, 31, 602–626.
  14. Yu, J.; Zhou, C.; Li, D.; Li, S.; Lin, Y.C.J.; Wang, J.P.; Chiang, V.L.; Li, W. A PtrLBD39-mediated transcriptional network regulates tension wood formation in Populus trichocarpa. Plant Commun. 2022, 3, 100250.
  15. Lian, C.; Li, Q.; Yao, K.; Zhang, Y.; Meng, S.; Yin, W.; Xia, X. Populus trichocarpa PtNF-YA9, a multifunctional transcription factor, regulates seed germination, abiotic stress, plant growth and development in Arabidopsis. Front. Plant Sci. 2018, 9, 954.
  16. André, D.; Marcon, A.; Lee, K.C.; Goretti, D.; Zhang, B.; Delhomme, N.; Schmid, M.; Nilsson, O. FLOWERING LOCUS T paralogs control the annual growth cycle in Populus trees. Curr. Biol. 2022, 32, 2988–2996.
  17. Mohamed, R.; Wang, C.T.; Ma, C.; Shevchenko, O.; Dye, S.J.; Puzey, J.R.; Etherington, E.; Sheng, X.; Meilan, R.; Strauss, S.H.; et al. Populus CEN/TFL1 regulates first onset of flowering, axillary meristem identity and dormancy release in Populus. Plant J. 2010, 62, 674–688.
  18. Ye, Y.; Xin, H.; Gu, X.; Ma, J.; Li, L. Genome-wide identification and functional analysis of the Basic Helix-Loop-Helix (bHLH) transcription family reveals candidate PtFBH genes involved in the flowering process of Populus trichocarpa. Forests 2021, 12, 1439.
  19. Shuai, P.; Liang, D.; Tang, S.; Zhang, Z.; Ye, C.Y.; Su, Y.; Xia, X.; Yin, W. Genome-wide identification and functional prediction of novel and drought-responsive lincRNAs in Populus trichocarpa. J. Exp. Bot. 2014, 65, 4975–4983.
  20. Ye, X.; Wang, S.; Zhao, X.; Gao, N.; Wang, Y.; Yang, Y.; Wu, E.; Jiang, C.; Cheng, Y.; Wu, W.; et al. Role of lncRNAs in cis- and trans-regulatory responses to salt in Populus trichocarpa. Plant J. 2022, 110, 978–993.
  21. Li, S.; Lin, Y.C.J.; Wang, P.; Zhang, B.; Li, M.; Chen, S.; Shi, R.; Tunlaya-Anukit, S.; Liu, X.; Wang, Z.; et al. The AREB1 transcription factor influences histone acetylation to regulate drought responses and tolerance in Populus trichocarpa. Plant Cell 2019, 31, 663–686.
  22. Cao, Y.; Han, Y.; Jin, Q.; Lin, Y.; Cai, Y. Comparative genomic analysis of the GRF genes in Chinese pear (Pyrus bretschneideri Rehd), poplar (Populous), grape (Vitis vinifera), Arabidopsis and rice (Oryza sativa). Front. Plant Sci. 2016, 7, 1750.
  23. Wei, H.; Movahedi, A.; Liu, G.; Li, Y.; Liu, S.; Yu, C.; Chen, Y.; Zhong, F.; Zhang, J. Comprehensive analysis of carotenoid cleavage dioxygenases gene family and its expression in response to abiotic stress in poplar. Int. J. Mol. Sci. 2022, 23, 1418.
  24. Bennetzen, J.L. Transposable element contributions to plant gene and genome evolution. Plant Mol. Biol. 2020, 42, 251–269.
  25. Du, J.; Tian, Z.; Hans, C.S.; Laten, H.M.; Cannon, S.B.; Jackson, S.A.; Shoemaker, R.C.; Ma, J. Evolutionary conservation, diversity and specificity of LTR-retrotransposons in flowering plants: Insights from genome-wide analysis and multi-specific comparison. Plant J. 2010, 63, 584–598.
  26. Ingvarsson, P.K.; Hvidsten, T.R.; Street, N.R. Towards integration of population and comparative genomics in forest trees. New Phytol. 2016, 212, 338–344.
  27. Zhao, H.; Sun, S.; Ding, Y.; Wang, Y.; Yue, X.; Du, X.; Wei, Q.; Fan, G.; Sun, H.; Lou, Y.; et al. Analysis of 427 genomes reveals moso bamboo population structure and genetic basis of property traits. Nat. Commun. 2021, 12, 5466.
  28. Salojärvi, J.; Smolander, O.P.; Nieminen, K.; Rajaraman, S.; Safronov, O.; Safdari, P.; Lamminmäki, A.; Immanen, J.; Lan, T.; Tanskanen, J.; et al. Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch. Nat. Genet. 2017, 49, 904–912.
  29. Davey, J.W.; Hohenlohe, P.A.; Etter, P.D.; Boone, J.Q.; Catchen, J.M.; Blaxter, M.L. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat. Rev. Genet. 2011, 12, 499–510.
  30. Caballero, M.; Lauer, E.; Bennett, J.; Zaman, S.; McEvoy, S.; Acosta, J.; Jackson, C.; Townsend, L.; Eckert, A.; Whetten, R.W.; et al. Toward genomic selection in Pinus taeda: Integrating resources to support array design in a complex conifer genome. Appl. Plant Sci. 2021, 9, e11439.
  31. Varas-Myrik, A.; Sepúlveda-Espinoza, F.; Fajardo, A.; Alarcón, D.; Toro-Núñez, Ó.; Castro-Nallar, E.; Hasbún, R. Predicting climate change-related genetic offset for the endangered southern South American conifer Araucaria araucana. For. Ecol. Manag. 2022, 504, 119856.
  32. Goto, S.; Kajiya-Kanegae, H.; Ishizuka, W.; Kitamura, K.; Ueno, S.; Hisamoto, Y.; Kudoh, H.; Yasugi, M.; Nagano, A.J.; Iwata, H. Genetic mapping of local adaptation along the altitudinal gradient in Abies sachalinensis. Tree Genet. Genomes 2017, 13, 1–13.
  33. Fuentes-Utrilla, P.; Goswami, C.; Cottrell, J.E.; Pong-Wong, R.; Law, A.; A’Hara, S.W.; Lee, S.J.; Woolliams, J.A. QTL analysis and genomic selection using RADseq derived markers in Sitka spruce: The potential utility of within family data. Tree Genet. Genomes 2017, 13, 1–12.
  34. Lu, N.; Zhang, M.; Xiao, Y.; Han, D.; Liu, Y.; Zhang, Y.; Yi, F.; Zhu, T.; Ma, W.; Fan, E.; et al. Construction of a high-density genetic map and QTL mapping of leaf traits and plant growth in an interspecific F1 population of Catalpa bungei × Catalpa duclouxii Dode. BMC Plant Biol. 2019, 19, 596.
  35. Jayakodi, M.; Schreiber, M.; Stein, N.; Mascher, M. Building pan-genome infrastructures for crop plants and their use in association genetics. DNA Res. 2021, 28, dsaa030.
  36. Saxena, R.K.; Edwards, D.; Varshney, R.K. Structural variations in plant genomes. Brief. Funct. Genomics 2014, 13, 296–307.
  37. Yuan, Y.; Bayer, P.E.; Batley, J.; Edwards, D. Current status of structural variation studies in plants. Plant Biotechnol. J. 2021, 19, 2153–2163.
  38. Della Coletta, R.; Qiu, Y.; Ou, S.; Hufford, M.B.; Hirsch, C.N. How the pan-genome is changing crop genomics and improvement. Genome Biol. 2021, 22, 3.
  39. Shi, J.; Tian, Z.; Lai, J.; Huang, X. Plant pan-genomics and its applications. Mol. Plant 2023, 16, 168–186.
  40. Pinosio, S.; Giacomello, S.; Faivre-Rampant, P.; Taylor, G.; Jorge, V.; Le Paslier, M.C.; Zaina, G.; Bastien, C.; Cattonaro, F.; Marroni, F.; et al. Characterization of the poplar pan-genome by genome-wide identification of structural variation. Mol. Biol. Evol. 2016, 33, 2706–2719.
  41. Lovell, J.T.; Bentley, N.B.; Bhattarai, G.; Jenkins, J.W.; Sreedasyam, A.; Alarcon, Y.; Bock, C.; Beth Boston, L.; Carlson, J.; Cervantes, K.; et al. Four chromosome scale genomes and a pan-genome annotation to accelerate pecan tree breeding. Nat. Commun. 2021, 12, 4125.
  42. Doğan, E.S.; Liu, C. Three-dimensional chromatin packing and positioning of plant genomes. Nat. Plants 2018, 4, 521–529.
  43. Daniell, H.; Lin, C.S.; Yu, M.; Chang, W.J. Chloroplast genomes: Diversity, evolution, and applications in genetic engineering. Genome Biol. 2016, 17, 134.
  44. Maliga, P. Engineering the plastid and mitochondrial genomes of flowering plants. Nat. Plants 2022, 8, 996–1006.
  45. Chen, H.; Wang, T.; He, X.; Cai, X.; Lin, R.; Liang, J.; Wu, J.; King, G.; Wang, X. BRAD V3.0: An upgraded Brassicaceae database. Nucleic Acids Res. 2022, 50, D1432–D1441.
  46. Tello-Ruiz, M.K.; Naithani, S.; Gupta, P.; Olson, A.; Wei, S.; Preece, J.; Jiao, Y.; Wang, B.; Chougule, K.; Garg, P.; et al. Gramene 2021: Harnessing the power of comparative genomics and pathways for plant research. Nucleic Acids Res. 2021, 49, D1452–D1463.
  47. McGettigan, P.A. Transcriptomics in the RNA-seq era. Curr. Opin. Chem. Biol. 2013, 17, 4–11.
  48. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63.
  49. Nagalakshmi, U.; Waern, K.; Snyder, M. RNA-Seq: A method for comprehensive transcriptome analysis. Curr. Protoc. Mol. Biol. 2010, 89, 4–11.
  50. Trapnell, C.; Roberts, A.; Goff, L.; Pertea, G.; Kim, D.; Kelley, D.R.; Pimentel, H.; Salzberg, S.L.; Rinn, J.L.; Pachter, L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 2012, 7, 562–578.
  51. Pertea, M.; Pertea, G.M.; Antonescu, C.M.; Chang, T.C.; Mendell, J.T.; Salzberg, S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015, 33, 290–295.
  52. Guttman, M.; Garber, M.; Levin, J.Z.; Donaghey, J.; Robinson, J.; Adiconis, X.; Fan, L.; Koziol, M.J.; Gnirke, A.; Nusbaum, C.; et al. Ab initio reconstruction of cell type–specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol. 2010, 28, 503–510.
  53. Haas, B.J.; Papanicolaou, A.; Yassour, M.; Grabherr, M.; Blood, P.D.; Bowden, J.; Couger, M.B.; Eccles, D.; Li, B.; Lieber, M.; et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 2013, 8, 1494–1512.
  54. Xie, Y.; Wu, G.; Tang, J.; Luo, R.; Patterson, J.; Liu, S.; Huang, W.; He, G.; Gu, S.; Li, S.; et al. SOAPdenovo-Trans: De Novo transcriptome assembly with short RNA-Seq reads. Bioinformatics 2014, 30, 1660–1666.
  55. Schulz, M.H.; Zerbino, D.R.; Vingron, M.; Birney, E. Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics 2012, 28, 1086–1092.
  56. Martin, J.A.; Wang, Z. Next-generation transcriptome assembly. Nat. Rev. Genet. 2011, 12, 671–682.
  57. An, D.; Cao, H.X.; Li, C.; Humbeck, K.; Wang, W. Isoform sequencing and state-of-art applications for unravelling complexity of plant transcriptomes. Genes 2018, 9, 43.
  58. Zhao, L.; Zhang, H.; Kohnen, M.V.; Prasad, K.V.; Gu, L.; Reddy, A.S. Analysis of transcriptome and epitranscriptome in plants using PacBio Iso-Seq and nanopore-based direct RNA sequencing. Front. Genet. 2019, 10, 253.
  59. Shumate, A.; Wong, B.; Pertea, G.; Pertea, M. Improved transcriptome assembly using a hybrid of long and short reads with StringTie. PLoS Comput. Biol. 2022, 18, e1009730.
  60. Mishima, K.; Hirakawa, H.; Iki, T.; Fukuda, Y.; Hirao, T.; Tamura, A.; Takahashi, M. Comprehensive collection of genes and comparative analysis of full-length transcriptome sequences from Japanese larch (Larix kaempferi) and Kuril larch (Larix gmelinii var. japonica). BMC Plant Biol. 2022, 22, 470.
  61. He, X.; Wang, Y.; Zheng, J.; Zhou, J.; Jiao, Z.; Wang, B.; Zhuge, Q. Full-length transcriptome characterization and comparative analysis of Chosenia arbutifolia. Forests 2022, 13, 543.
  62. Li, R.; Xiao, M.; Li, J.; Zhao, Q.; Wang, M.; Zhu, Z. Transcriptome Analysis of CYP450 Family Members in Fritillaria cirrhosa D. Don and Profiling of Key CYP450s Related to Isosteroidal Alkaloid Biosynthesis. Genes 2023, 14, 219.
  63. Hong, Y.; Wang, Z.; Li, M.; Su, Y.; Wang, T. First multi-organ full-length transcriptome of tree fern Alsophila spinulosa highlights the stress-resistant and light-adapted genes. Front. Genet. 2022, 12, 2812.
  64. McDermaid, A.; Monier, B.; Zhao, J.; Liu, B.; Ma, Q. Interpretation of differential gene expression results of RNA-seq data: Review and integration. Brief. Bioinf. 2019, 20, 2044–2054.
  65. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550.
  66. Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139–140.
  67. Li, J.; Tibshirani, R. Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Stat. Methods Med. Res. 2013, 22, 519–536.
  68. Suzuki, N.; Rivero, R.M.; Shulaev, V.; Blumwald, E.; Mittler, R. Abiotic and biotic stress combinations. New Phytol. 2014, 203, 32–43.
  69. Zhang, H.; Zhu, J.; Gong, Z.; Zhu, J.K. Abiotic stress responses in plants. Nat. Rev. Genet. 2022, 23, 104–119.
  70. Li, W.; Liu, Z.; Feng, H.; Yang, J.; Li, C. Characterization of the gene expression profile response to drought stress in Populus ussuriensis using PacBio SMRT and Illumina Sequencing. Int. J. Mol. Sci. 2022, 23, 3840.
  71. Kovalchuk, A.; Zeng, Z.; Ghimire, R.P.; Kivimäenpää, M.; Raffaello, T.; Liu, M.; Mukrimin, M.; Kasanen, R.; Sun, H.; Julkunen-Tiitto, R.; et al. Dual RNA-seq analysis provides new insights into interactions between Norway spruce and necrotrophic pathogen Heterobasidion annosum sl. BMC Plant Biol. 2019, 19, 2.
  72. Klepikova, A.V.; Penin, A.A. Gene expression maps in plants: Current state and prospects. Plants 2019, 8, 309.
  73. Serin, E.A.; Nijveen, H.; Hilhorst, H.W.; Ligterink, W. Learning from co-expression networks: Possibilities and challenges. Front. Plant Sci. 2016, 7, 444.
  74. Langfelder, P.; Horvath, S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinf. 2008, 9, 559.
  75. Ma, J.J.; Chen, X.; Song, Y.T.; Zhang, G.F.; Zhou, X.Q.; Que, S.P.; Mao, F.; Pervaiz, T.; Lin, J.X.; Li, Y.; et al. MADS-box transcription factors MADS11 and DAL1 interact to mediate the vegetative-to-reproductive transition in pine. Plant Physiol. 2021, 187, 247–262.
  76. Wang, R.; Xie, M.; Zhao, W.; Yan, P.; Wang, Y.; Gu, Y.; Jiang, T.; Qu, G. WGCNA reveals genes associated with lignification in the secondary stages of wood formation. Forests 2023, 14, 99.
  77. Francisco, F.R.; Aono, A.H.; da Silva, C.C.; Gonçalves, P.S.; Scaloppi Junior, E.J.; Le Guen, V.; Fritsche-Neto, R.; Souza, L.M.; de Souza, A.P. Unravelling rubber tree growth by integrating GWAS and biological network-based approaches. Front. Plant Sci. 2021, 12, 768589.
  78. Wang, M.; Tong, S.; Ma, T.; Xi, Z.; Liu, J. Chromosome-level genome assembly of Sichuan pepper provides insights into apomixis, drought tolerance, and alkaloid biosynthesis. Mol. Ecol. Resour. 2021, 21, 2533–2545.
  79. Ovens, K.; Eames, B.F.; McQuillan, I. Comparative analyses of gene co-expression networks: Implementations and applications in the study of evolution. Front. Genet. 2021, 12, 695399.
  80. Rich-Griffin, C.; Stechemesser, A.; Finch, J.; Lucas, E.; Ott, S.; Schäfer, P. Single-cell transcriptomics: A high-resolution avenue for plant functional genomics. Trends Plant Sci. 2020, 25, 186–197.
  81. Chen, Y.; Tong, S.; Jiang, Y.; Ai, F.; Feng, Y.; Zhang, J.; Gong, J.; Qin, J.; Zhang, Y.; Zhu, Y.; et al. Transcriptional landscape of highly lignified poplar stems at single-cell resolution. Genome Biol. 2021, 22, 319.
  82. Burgess, D.J. Spatial transcriptomics coming of age. Nat. Rev. Genet. 2019, 20, 317.
  83. Giacomello, S. A new era for plant science: Spatial single-cell transcriptomics. Curr. Opin. Plant Biol. 2021, 60, 102041.
  84. Zhang, X.; Cheng, Z.; Zhao, K.; Yao, W.; Sun, X.; Jiang, T.; Zhou, B. Functional characterization of poplar NAC13 gene in salt tolerance. Plant Sci. 2019, 281, 1–8.
  85. Wang, L.Q.; Wen, S.S.; Wang, R.; Wang, C.; Gao, B.; Lu, M.Z. PagWOX11/12a activates PagCYP736A12 gene that facilitates salt tolerance in poplar. Plant Biotechnol. J. 2021, 19, 2249–2260.
  86. Yao, W.; Wang, S.; Zhou, B.; Jiang, T. Transgenic poplar overexpressing the endogenous transcription factor ERF76 gene improves salinity tolerance. Tree Physiol. 2016, 36, 896–908.
  87. Kumar, S. Epigenomics of plant responses to environmental stress. Epigenomes 2018, 2, 6.
  88. Singh, D.; Chaudhary, P.; Taunk, J.; Singh, C.K.; Sharma, S.; Singh, V.J.; Singh, D.; Chinnusamy, V.; Yadav, R.; Pal, M. Plant epigenomics for extenuation of abiotic stresses: Challenges and future perspectives. J. Exp. Bot. 2021, 72, 6836–6855.
  89. Amaral, J.; Ribeyre, Z.; Vigneaud, J.; Sow, M.D.; Fichot, R.; Messier, C.; Pinto, J.; Nolet, P.; Maury, S. Advances and promises of epigenetics for forest trees. Forests 2020, 11, 976.
  90. Gouil, Q.; Keniry, A. Latest techniques to study DNA methylation. Essays Biochem. 2019, 63, 639–648.
  91. Bartels, A.; Han, Q.; Nair, P.; Stacey, L.; Gaynier, H.; Mosley, M.; Huang, Q.Q.; Pearson, J.K.; Hsieh, T.F.; An, Y.Q.C.; et al. Dynamic DNA methylation in plant growth and development. Int. J. Mol. Sci. 2018, 19, 2144.
  92. Kumar, S.; Mohapatra, T. Dynamics of DNA methylation and its functions in plant growth and development. Front. Plant Sci. 2021, 12, 596236.
  93. Song, Y.; Ma, K.; Bo, W.; Zhang, Z.; Zhang, D. Sex-specific DNA methylation and gene expression in andromonoecious poplar. Plant Cell Rep. 2012, 31, 1393–1405.
  94. Sow, M.D.; Le Gac, A.L.; Fichot, R.; Lanciano, S.; Delaunay, A.; Le Jan, I.; Lesage-Descauses, M.C.; Citerne, S.; Caius, J.; Brunaud, V.; et al. RNAi suppression of DNA methylation affects the drought stress response and genome integrity in transgenic poplar. New Phytol. 2021, 232, 80–97.
  95. Zhang, Y.; Liu, C.; Cheng, H.; Tian, S.; Liu, Y.; Wang, S.; Zhang, H.; Saqib, M.; Wei, H.; Wei, Z. DNA methylation and its effects on gene expression during primary to secondary growth in poplar stems. BMC Genomics 2020, 21, 498.
  96. Xiao, D.; Zhou, K.; Yang, X.; Yang, Y.; Ma, Y.; Wang, Y. Crosstalk of DNA methylation triggered by pathogen in poplars with different resistances. Front. Microbiol. 2021, 12, 4098.
  97. Karemaker, I.D.; Vermeulen, M. Single-cell DNA methylation profiling: Technologies and biological applications. Trends Biotechnol. 2018, 36, 952–965.
  98. Weber, C.M.; Henikoff, S. Histone variants: Dynamic punctuation in transcription. Genes Dev. 2014, 28, 672–682.
  99. Lawrence, M.; Daujat, S.; Schneider, R. Lateral thinking: How histone modifications regulate gene expression. Trends Genet. 2016, 32, 42–56.
  100. Kumar, V.; Thakur, J.K.; Prasad, M. Histone acetylation dynamics regulating plant development and stress responses. Cell Mol. Life Sci. 2021, 78, 4467–4486.
  101. He, K.; Cao, X.; Deng, X. Histone methylation in epigenetic regulation and temperature responses. Curr. Opin. Plant Biol. 2021, 61, 102001.
  102. Borg, M.; Jiang, D.; Berger, F. Histone variants take center stage in shaping the epigenome. Curr. Opin. Plant Biol. 2021, 61, 101991.
  103. Ma, S.; Zhang, Y. Profiling chromatin regulatory landscape: Insights into the development of ChIP-seq and ATAC-seq. Mol. Biomed. 2020, 1, 9.
  104. Xiao, D.; Chen, M.; Yang, X.; Bao, H.; Yang, Y.; Wang, Y. The intersection of non-coding RNAs contributes to forest trees’ response to abiotic stress. Int. J. Mol. Sci. 2022, 23, 6365.
  105. Yakovlev, I.A.; Fossdal, C.G.; Johnsen, Ø. MicroRNAs, the epigenetic memory and climatic adaptation in Norway spruce. New Phytol. 2010, 187, 1154–1169.
  106. Dalakouras, A.; Vlachostergios, D. Epigenetic approaches to crop breeding: Current status and perspectives. J. Exp. Bot. 2021, 72, 5356–5371.
  107. Gahlaut, V.; Zinta, G.; Jaiswal, V.; Kumar, S. Quantitative epigenetics: A new avenue for crop improvement. Epigenomes 2020, 4, 25.
  108. Nakamura, M.; Gao, Y.; Dominguez, A.A.; Qi, L.S. CRISPR technologies for precise epigenome editing. Nat. Cell Biol. 2021, 23, 11–22.
  109. Nuñez, J.K.; Chen, J.; Pommier, G.C.; Cogan, J.Z.; Replogle, J.M.; Adriaens, C.; Ramadoss, G.N.; Shi, Q.; Hung, K.L.; Samelson, A.J.; et al. Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing. Cell 2021, 184, 2503–2519.
  110. Deng, X.; Song, X.; Wei, L.; Liu, C.; Cao, X. Epigenetic regulation and epigenomic landscape in rice. Natl. Sci. Rev. 2016, 3, 309–327.
  111. Raju, S.K.K.; Shao, M.R.; Sanchez, R.; Xu, Y.Z.; Sandhu, A.; Graef, G.; Mackenzie, S. An epigenetic breeding system in soybean for increased yield and stability. Plant Biotechnol. J. 2018, 16, 1836–1847.
  112. Yu, Q.; Liu, S.; Yu, L.; Xiao, Y.; Zhang, S.; Wang, X.; Xu, Y.; Yu, H.; Li, Y.; Yang, J.; et al. RNA demethylation increases the yield and biomass of rice and potato plants in field trials. Nat. Biotechnol. 2021, 39, 1581–1588.
  113. García-Murillo, L.; Valencia-Lozano, E.; Priego-Ranero, N.A.; Cabrera-Ponce, J.L.; Duarte-Aké, F.P.; Vizuet-de-Rueda, J.C.; Rivera-Toro, D.M.; Herrera-Ubaldo, H.; de Folter, S.; Alvarez-Venegas, R. CRISPRa-mediated transcriptional activation of the SlPR-1 gene in edited tomato plants. Plant Sci. 2023, 329, 111617.
  114. Aslam, B.; Basit, M.; Nisar, M.A.; Khurshid, M.; Rasool, M.H. Proteomics: Technologies and their applications. J. Chromatogr. Sci. 2016, 55, 182–196.
  115. Mergner, J.; Kuster, B. Plant proteome dynamics. Annu. Rev. Plant Biol. 2022, 73, 67–92.
  116. Conibear, A.C. Deciphering protein post-translational modifications using chemical biology tools. Nat. Rev. Chem. 2020, 4, 674–695.
  117. Auerbach, D.; Thaminy, S.; Hottiger, M.O.; Stagljar, I. The post-genomic era of interactive proteomics: Facts and perspectives. Proteomics 2002, 2, 611–623.
  118. Chen, S.; Harmon, A.C. Advances in plant proteomics. Proteomics 2006, 6, 5504–5516.
  119. Traversari, S.; Giovannelli, A.; Emiliani, G. Wood formation under changing environment: Omics approaches to elucidate the mechanisms driving the early-to-latewood transition in Conifers. Forests 2022, 13, 608.
  120. Jing, D.; Zhang, J.; Xia, Y.; Kong, L.; OuYang, F.; Zhang, S.; Zhang, H.; Wang, J. Proteomic analysis of stress-related proteins and metabolic pathways in Picea asperata somatic embryos during partial desiccation. Plant Biotechnol. J. 2017, 15, 27–38.
  121. Zivy, M.; de Vienne, D. Proteomics: A link between genomics, genetics and physiology. Plant Mol. Biol. 2000, 44, 575–580.
  122. Kage, U.; Kumar, A.; Dhokane, D.; Karre, S.; Kushalappa, A.C. Functional molecular markers for crop improvement. Crit. Rev. Biotechnol. 2016, 36, 917–930.
  123. Agregán, R.; Echegaray, N.; López-Pedrouso, M.; Aadil, R.M.; Hano, C.; Franco, D.; Lorenzo, J.M. Proteomic advances in cereal and vegetable crops. Molecules 2021, 26, 4924.
  124. Rodziewicz, P.; Chmielewska, K.; Sawikowska, A.; Marczak, Ł.; Łuczak, M.; Bednarek, P.; Mikołajczak, K.; Ogrodowicz, P.; Kuczyńska, A.; Krajewski, P.; et al. Identification of drought responsive proteins and related proteomic QTLs in barley. J. Exp. Bot. 2019, 70, 2823–2837.
  125. Liu, Z.; Yang, B.; Huang, R.; Suo, H.; Zhang, Z.; Chen, W.; Dai, X.; Zou, X.; Ou, L. Transcriptome- and proteome-wide association of a recombinant inbred line population revealed twelve core QTLs for four fruit traits in pepper (Capsicum annuum L.). Hortic. Res. 2022, 9, uhac015.
  126. Liu, Y.; Lu, S.; Liu, K.; Wang, S.; Huang, L.; Guo, L. Proteomics: A powerful tool to study plant responses to biotic stress. Plant Methods 2019, 15, 1–20.
  127. Jorrin Novo, J.V. Proteomics and plant biology: Contributions to date and a look towards the next decade. Expert Rev. Proteom. 2021, 18, 93–103.
  128. Schauer, N.; Fernie, A.R. Plant metabolomics: Towards biological function and mechanism. Trends Plant Sci. 2006, 11, 508–516.
  129. Bourgaud, F.; Gravot, A.; Milesi, S.; Gontier, E. Production of plant secondary metabolites: A historical perspective. Plant Sci. 2001, 161, 839–851.
  130. Macel, M.; Van Dam, N.M.; Keurentjes, J.J. Metabolomics: The chemistry between ecology and genetics. Mol. Ecol. Resour. 2010, 10, 583–593.
  131. Guijas, C.; Montenegro-Burke, J.R.; Warth, B.; Spilker, M.E.; Siuzdak, G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat. Biotechnol. 2018, 36, 316–320.
  132. Hong, J.; Yang, L.; Zhang, D.; Shi, J. Plant metabolomics: An indispensable system biology tool for plant science. Int. J. Mol. Sci. 2016, 17, 767.
  133. Hagel, J.M.; Facchini, P.J. Plant metabolomics: Analytical platforms and integration with functional genomics. Phytochem. Rev. 2008, 7, 479–497.
  134. Obata, T.; Fernie, A.R. The use of metabolomics to dissect plant responses to abiotic stresses. Cell Mol. Life Sci. 2012, 69, 3225–3243.
  135. Ribbenstedt, A.; Ziarrusta, H.; Benskin, J.P. Development, characterization and comparisons of targeted and non-targeted metabolomics methods. PLoS ONE 2018, 13, e0207082.
  136. Zenobi, R. Single-cell metabolomics: Analytical and biological perspectives. Science 2013, 342, 1243259.
  137. Fang, C.; Fernie, A.R.; Luo, J. Exploring the diversity of plant metabolism. Trends Plant Sci. 2019, 24, 83–98.
  138. Kumar, R.; Bohra, A.; Pandey, A.K.; Pandey, M.K.; Kumar, A. Metabolomics for plant improvement: Status and prospects. Front. Plant Sci. 2017, 8, 1302.
  139. Wang, H.; Guo, L.; Zha, R.; Gao, Z.; Yu, F.; Wei, Q. Histological, metabolomic and transcriptomic analyses reveal mechanisms of cold acclimation of the Moso bamboo (Phyllostachys edulis) leaf. Tree Physiol. 2022, 42, 2336–2352.
  140. Ren, S.; Ma, K.; Lu, Z.; Chen, G.; Cui, J.; Tong, P.; Wang, L.; Teng, N.; Jin, B. Transcriptomic and metabolomic analysis of the heat-stress response of Populus tomentosa Carr. Forests 2019, 10, 383.
  141. Mao, C.; Li, L.; Yang, T.; Gui, M.; Li, X.; Zhang, F.; Zhao, Q.; Wu, Y. Transcriptomics integrated with widely targeted metabolomics reveals the cold resistance mechanism in Hevea brasiliensis. Front. Plant Sci. 2023, 13, 1092411.
  142. Soltis, N.E.; Kliebenstein, D.J. Natural variation of plant metabolism: Genetic mechanisms, interpretive caveats, and evolutionary and mechanistic insights. Plant Physiol. 2015, 169, 1456–1468.
  143. Khan, S.A.; Chibon, P.Y.; de Vos, R.C.; Schipper, B.A.; Walraven, E.; Beekwilder, J.; van Dijk, T.; Finkers, R.; Visser, R.G.F.; van de Weg, E.W.; et al. Genetic analysis of metabolites in apple fruits indicates an mQTL hotspot for phenolic compounds on linkage group 16. J. Exp. Bot. 2012, 63, 2895–2908.
  144. Lu, W.; Du, Q.; Xiao, L.; Lv, C.; Quan, M.; Li, P.; Yao, L.; Song, F.; Zhang, D. Multi-omics analysis provides insights into genetic architecture of flavonoid metabolites in Populus. Ind. Crops Prod. 2021, 168, 113612.
  145. DellaPenna, D. Plant metabolic engineering. Plant Physiol. 2001, 125, 160–163.
  146. Noleto-Dias, C.; Picoli, E.A.D.T.; Porzel, A.; Wessjohann, L.A.; Tavares, J.F.; Farag, M.A. Metabolomics characterizes early metabolic changes and markers of tolerant Eucalyptus ssp. clones against drought stress. Phytochemistry 2023, 212, 113715.
  147. Tardieu, F.; Cabrera-Bosquet, L.; Pridmore, T.; Bennett, M. Plant phenomics, from sensors to knowledge. Curr. Biol. 2017, 27, R770–R783.
  148. Niu, X.; Song, Z.; Xu, C.; Wu, H.; Luan, Q.; Jiang, J.; Li, Y. Prediction of needle physiological traits using UAV imagery for breeding selection of slash pine. Plant Phenom. 2023, 5, 0028.
  149. Ludovisi, R.; Tauro, F.; Salvati, R.; Khoury, S.; Mugnozza Scarascia, G.; Harfouche, A. UAV-based thermal imaging for high-throughput field phenotyping of black poplar response to drought. Front. Plant Sci. 2017, 8, 1681.
  150. Xu, Y.; Li, P.; Zou, C.; Lu, Y.; Xie, C.; Zhang, X.; Prasanna, B.M.; Olsen, M.S. Enhancing genetic gain in the era of molecular breeding. J. Exp. Bot. 2017, 68, 2641–2666.
  151. Xu, Y.; Zhang, X.; Li, H.; Zheng, H.; Zhang, J.; Olsen, M.S.; Varshney, R.K.; Prasanna, B.M.; Qian, Q. Smart breeding driven by big data, artificial intelligence and integrated genomic-enviromic prediction. Mol. Plant. 2022, 15, 1664–1695.
  152. Huang, X.Y.; Salt, D.E. Plant ionomics: From elemental profiling to environmental adaptation. Mol. Plant. 2016, 9, 787–797.
  153. Ali, S.; Tyagi, A.; Bae, H. Ionomic approaches for discovery of novel stress-resilient genes in plants. Int. J. Mol. Sci. 2021, 22, 7182.
  154. Muller, D.B.; Vogel, C.; Bai, Y.; Vorholt, J.A. The plant microbiota: Systems-level insights and perspectives. Annu. Rev. Genet. 2016, 50, 211–234.
  155. Yang, L.; Yang, Y.; Huang, L.; Cui, X.; Liu, Y. From single-to multi-omics: Future research trends in medicinal plants. Briefings Bioinf. 2023, 24, bbac485.
  156. Jamil, I.N.; Remali, J.; Azizan, K.A.; Nor Muhammad, N.A.; Arita, M.; Goh, H.-H.; Aizat, W.M. Systematic multi-omics integration (MOI) approach in plant systems biology. Front. Plant Sci. 2020, 11, 944.
  157. Balmant, K.M.; Noble, J.D.; Alves, F.C.; Dervinis, C.; Conde, D.; Schmidt, H.W.; Vazquez, A.I.; Barbazuk, W.B.; de los Campos, G.; Resende, M.F.R., Jr.; et al. Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis in Populus deltoides. Genome Res. 2020, 30, 1131–1143.
  158. Subramanian, I.; Verma, S.; Kumar, S.; Jere, A.; Anamika, K. Multi-omics data integration, interpretation, and its application. Bioinform. Biol. Insights 2020, 14, 1177932219899051.
  159. Tarazona, S.; Arzalluz-Luque, A.; Conesa, A. Undisclosed, unmet and neglected challenges in multi-omics studies. Nat. Comput. Sci. 2021, 1, 395–402.
  160. Yu, S.; Drton, M.; Promislow, D.E.; Shojaie, A. CorDiffViz: An R package for visualizing multi-omics differential correlation networks. BMC Bioinf. 2021, 22, 486.
  161. Kumar, D.; Bansal, G.; Narang, A.; Basak, T.; Abbas, T.; Dash, D. Integrating transcriptome and proteome profiling: Strategies and applications. Proteomics 2016, 16, 2533–2544.
  162. Guerrero-Sánchez, V.M.; Castillejo, M.Á.; López-Hidalgo, C.; Alconada, A.M.M.; Jorrín-Novo, J.V.; Rey, M.-D. Changes in the transcript and protein profiles of Quercus ilex seedlings in response to drought stress. J. Proteom. 2021, 243, 104263.
  163. Pierre-Jean, M.; Deleuze, J.-F.; Le Floch, E.; Mauger, F. Clustering and variable selection evaluation of 13 unsupervised methods for multi-omics data integration. Briefings Bioinform. 2020, 21, 2011–2030.
  164. Pascual, J.; Cañal, M.J.; Escandon, M.; Meijon, M.; Weckwerth, W.; Valledor, L. Integrated physiological, proteomic, and metabolomic analysis of ultra violet (UV) stress responses and adaptation mechanisms in Pinus radiata. Mol. Cell. Proteom. 2017, 16, 485–501.
  165. Rai, A.; Saito, K.; Yamazaki, M. Integrated omics analysis of specialized metabolism in medicinal plants. Plant J. 2017, 90, 764–787.
  166. Obudulu, O.; Mähler, N.; Skotare, T.; Bygdell, J.; Abreu, I.N.; Ahnlund, M.; Gandla, M.L.; Petterle, A.; Moritz, T.; Hvidsten, T.R.; et al. A multi-omics approach reveals function of Secretory Carrier-Associated Membrane Proteins in wood formation of Populus trees. BMC Genomics 2018, 19, 11.
  167. Altman, T.; Travers, M.; Kothari, A.; Caspi, R.; Karp, P.D. A systematic comparison of the MetaCyc and KEGG pathway databases. BMC Bioinform. 2013, 14, 112.
  168. López-Hidalgo, C.; Guerrero-Sanchez, V.M.; Gómez-Gálvez, I.; Sánchez-Lucas, R.; Castillejo-Sánchez, M.A.; Maldonado-Alconada, A.M.; Valledor, L.; Jorrin-Novo, J.V. A multi-omics analysis pipeline for the metabolic pathway reconstruction in the orphan species Quercus ilex. Front. Plant Sci. 2018, 9, 935.
  169. Zoppi, J.; Guillaume, J.-F.; Neunlist, M.; Chaffron, S. MiBiOmics: An interactive web application for multi-omics data exploration and integration. BMC Bioinform. 2021, 22, 6.
  170. Hu, H.; Campbell, M.T.; Yeats, T.H.; Zheng, X.; Runcie, D.E.; Covarrubias-Pazaran, G.; Broeckling, C.; Yao, L.; Caffe-Treml, M.; Gutiérrez, L.; et al. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. Theor. Appl. Genet. 2021, 134, 4043–4054.
  171. Yang, Y.; Saand, M.A.; Huang, L.; Abdelaal, W.B.; Zhang, J.; Wu, Y.; Li, J.; Sirohi, M.H.; Wang, F. Applications of multi-omics technologies for crop improvement. Front. Plant Sci. 2021, 12, 563953.
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