The increasing development of “omics” technologies have enabled unprecedented progress in plant biology, from the molecular to the systems biology level. When synergistically integrated with computer science, omics approaches (including genomics, epigenomics, transcriptomics, proteomics and metabolomics) have demonstrated the capacity to not only enhance our understanding of critical plant processes (such as metabolism, development, and signaling in model species) but also to provide valuable insights into the biology of non-model crop species, particularly on the improvement of agronomically and economically important traits. Though genomics and transcriptomics are the most popular platforms due to their decreased costs, improved sequencing quality, and abundant bioinformatic tools, proteomics and metabolomics are becoming more prevalent.
While major advancements of non-model crop species have been made by omics approaches, a large body of omics studies have centered on computational analyses with correlative and inferred conclusions without experimental validation. Current limitations on the understanding of individual gene function in non-model crops represent a major obstacle to answering critical questions about the genetics control and regulation of those agronomically- or economically- important traits. Such obstacles are largely due to technical limitations in genetic transformation of many crop species and insufficient genetics resources, such as mutant libraries, which are key components of forward and reverse genetics as the traditional way to decipher gene functions.
To circumvent these limitations and compensate the insufficiency of resources, synergistic integration of omics and the traditional genetics and/or population genetics approaches could address the challenges and facilitate crop studies from molecular to system levels. Moreover, many crop species have valuable genetic resources and germplasm, such as introgression lines, near isogenic lines (NIL) and recombinant inbred lines (RIL). Omics studies taking advantages of these crop genetic resources are of particular interest and would provide novel insights. In addition, high-throughput methods (including both experimental and bioinformatic) for inferring, characterizing, or validating functional information could be developed with integration of genetic, genomic and/or multi-omic technologies. Species of interest include agronomically- and/or economically- significant non-model crops, for example, wheat, maize, sorghum, sugarcane and duckweed. Studies purely using model plant species (such as Arabidopsis, Brachypodium, Chlamydomonas) are not encouraged.
With such background, we welcome investigators in the field of genomics-enabled crop genetics to contribute their high-quality original research, brief case reports, mini-reviews, reviews, or opinion articles. Potential topics include, but are not limited to:
• Crop genomic studies: Integrating genomics or transcriptomics approaches and crop genetics or population genetics with the goal of understanding agronomically- and/or economically- important traits
• Crop multi-omic studies: Applying multi-omics technologies to provide novel insights or to answer critical questions of agronomically- and/or economically- important traits
• Crop functional studies: Focusing on the functions of individual genes or components combining traditional genetics and omics approaches (RNA-seq, Chip-seq and BSA-seq) to illustrate the molecular and systematic mechanisms. Traditional genetics studies includes but are not limited to those using transgenic crops, mutants, near isogenic lines, introgression lines, and segregating populations.
• Crop population genetics: Population genetics studies covering quantitative trait loci (QTL), genome-wide association study (GWAS) and genomic selection with the effective combination of omics approaches, for example integration with transcriptomics and proteomics to provide functional insights of a QTL region and prioritize candidate gene validation.
• Bioinformatics for crop multi-omics studies: Bioinformatics software and pipelines developed to: (1) provide novel NGS data-mining results or to address technical challenges for the understudied crop species; or (2) integrate and interpret multi-omics data and/or genetics/population genetics information. The software or pipelines should not be limited within particular species.
The increasing development of “omics” technologies have enabled unprecedented progress in plant biology, from the molecular to the systems biology level. When synergistically integrated with computer science, omics approaches (including genomics, epigenomics, transcriptomics, proteomics and metabolomics) have demonstrated the capacity to not only enhance our understanding of critical plant processes (such as metabolism, development, and signaling in model species) but also to provide valuable insights into the biology of non-model crop species, particularly on the improvement of agronomically and economically important traits. Though genomics and transcriptomics are the most popular platforms due to their decreased costs, improved sequencing quality, and abundant bioinformatic tools, proteomics and metabolomics are becoming more prevalent.
While major advancements of non-model crop species have been made by omics approaches, a large body of omics studies have centered on computational analyses with correlative and inferred conclusions without experimental validation. Current limitations on the understanding of individual gene function in non-model crops represent a major obstacle to answering critical questions about the genetics control and regulation of those agronomically- or economically- important traits. Such obstacles are largely due to technical limitations in genetic transformation of many crop species and insufficient genetics resources, such as mutant libraries, which are key components of forward and reverse genetics as the traditional way to decipher gene functions.
To circumvent these limitations and compensate the insufficiency of resources, synergistic integration of omics and the traditional genetics and/or population genetics approaches could address the challenges and facilitate crop studies from molecular to system levels. Moreover, many crop species have valuable genetic resources and germplasm, such as introgression lines, near isogenic lines (NIL) and recombinant inbred lines (RIL). Omics studies taking advantages of these crop genetic resources are of particular interest and would provide novel insights. In addition, high-throughput methods (including both experimental and bioinformatic) for inferring, characterizing, or validating functional information could be developed with integration of genetic, genomic and/or multi-omic technologies. Species of interest include agronomically- and/or economically- significant non-model crops, for example, wheat, maize, sorghum, sugarcane and duckweed. Studies purely using model plant species (such as Arabidopsis, Brachypodium, Chlamydomonas) are not encouraged.
With such background, we welcome investigators in the field of genomics-enabled crop genetics to contribute their high-quality original research, brief case reports, mini-reviews, reviews, or opinion articles. Potential topics include, but are not limited to:
• Crop genomic studies: Integrating genomics or transcriptomics approaches and crop genetics or population genetics with the goal of understanding agronomically- and/or economically- important traits
• Crop multi-omic studies: Applying multi-omics technologies to provide novel insights or to answer critical questions of agronomically- and/or economically- important traits
• Crop functional studies: Focusing on the functions of individual genes or components combining traditional genetics and omics approaches (RNA-seq, Chip-seq and BSA-seq) to illustrate the molecular and systematic mechanisms. Traditional genetics studies includes but are not limited to those using transgenic crops, mutants, near isogenic lines, introgression lines, and segregating populations.
• Crop population genetics: Population genetics studies covering quantitative trait loci (QTL), genome-wide association study (GWAS) and genomic selection with the effective combination of omics approaches, for example integration with transcriptomics and proteomics to provide functional insights of a QTL region and prioritize candidate gene validation.
• Bioinformatics for crop multi-omics studies: Bioinformatics software and pipelines developed to: (1) provide novel NGS data-mining results or to address technical challenges for the understudied crop species; or (2) integrate and interpret multi-omics data and/or genetics/population genetics information. The software or pipelines should not be limited within particular species.
