Choose the right agrigenomics sequencing approach
The biological question should drive the sequencing approach. Among the workflows featured here, low-pass WGS with imputation is suited to population-scale analysis of genetic variation in plants and animals, while 16S rRNA gene amplicon sequencing or shotgun metagenomics can be used to study microbial communities associated with agricultural samples and environments.
| Application | Sequencing approach | Solution |
|---|---|---|
| Genome-wide variation across plant or animal populations | Low-pass whole-genome sequencing with imputation | NEXTFLEX™ HT Agrigenomics Low-Pass WGS Kit |
| Microbial communities associated with soil, plants, livestock, and other agricultural environments | 16S rRNA gene amplicon sequencing or shotgun metagenomics | Metagenomics workflows |
Applications of NGS in agrigenomics
NGS-based agrigenomics connects genome-wide variation with phenotypic, pedigree, and environmental information. In plant and animal programs, this can inform selection, identify genomic regions associated with important traits, and help manage genetic diversity. Sequencing can also reveal how microbial communities vary across agricultural samples and environments.
Genomic selection and breeding
Genome-wide genotypes can be combined with phenotype and pedigree information to estimate breeding values and inform the selection of plant lines or animals. Low-pass WGS with imputation provides a scalable way to generate dense genotype information across large breeding populations.
Trait discovery and population characterization
Genome-wide genotyping can answer two related questions. When paired with phenotypic measurements, it can identify genomic regions associated with agronomic or production traits and provide candidates for marker development and validation. Across collections of plant lines, animal breeds, or breeding populations, it can also reveal relatedness, population structure, genetic diversity, and introgression. This information can be used to define breeding populations, manage germplasm, and preserve valuable variation.
Agricultural microbiome analysis
Microbial communities contribute to soil function, nutrient cycling, plant–microbe interactions, and animal health. 16S amplicon sequencing can compare bacterial and archaeal community composition across samples or conditions, while shotgun metagenomics provides broader taxonomic coverage and information about the genes and metabolic pathways represented in the community. The resulting profiles can be compared across soils, crops, livestock, environments, and management practices.
Revvity solutions across the NGS agrigenomics workflow
Agrigenomics workflows may begin with plant tissue, seeds, animal tissue, soil, or other agricultural samples, each with different handling and homogenization requirements. Sample type, sequencing application, and study scale all influence the choice of nucleic acid extraction, quality control, library preparation, and automation methods used downstream.
Sample homogenization
Plant leaves, roots, seeds, animal tissues, and soil differ in toughness, moisture content, and processing requirements, so the homogenization method must fit the material and throughput. Bead mills are well suited to tough or heterogeneous samples and can process sealed tubes or 96-well plates. Rotor-stator systems offer flexibility across individual samples and a range of volumes, while automated homogenization can support larger batches and more standardized processing.
Nucleic acid extraction
Depending on the source, agricultural samples may contain polysaccharides, polyphenols, lipids, humic substances, or other contaminants that can co-purify with DNA or RNA and inhibit downstream enzymatic steps. Extraction workflows must therefore balance yield, purity, and nucleic acid integrity while producing consistent results across variable starting materials. Automated magnetic-bead extraction standardizes sample processing and minimizes hands-on time across low- to high-throughput projects.
Sample and library quality assessment
Accurate input measurements are especially important when preparing large numbers of NGS libraries, where variation between samples can lead to uneven library representation and sequencing coverage. Assessing concentration, purity, and fragment size or integrity before library preparation helps identify unsuitable material and supports appropriate input normalization.
After library preparation, measuring library concentration and fragment-size distribution helps identify failed or atypical libraries and supports more consistent pooling before sequencing.
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NGS library preparation
Library preparation requirements differ substantially across agrigenomics applications. Low-pass WGS workflows prioritize uniform coverage, high multiplexing, and cost-effective processing across large plant or animal cohorts, while 16S and shotgun metagenomics use distinct strategies to profile microbial communities.
Workflow automation
Large agrigenomics programs may require library preparation across hundreds or thousands of plant or animal samples. At that scale, repeated reagent additions, index transfers, magnetic-bead cleanups, normalization, and pooling become major sources of hands-on time and sample-to-sample variation. Automated liquid handling can standardize these steps in 96- or 384-well formats.
Bioinformatics and data analysis
Low-pass WGS analysis uses a suitable reference panel to infer genotypes at variant sites that were not directly observed at low sequencing depth, producing dense genotype data for genomic selection, trait association, and population analysis. For agricultural microbiome applications, 16S and shotgun metagenomics pipelines convert sequencing reads into taxonomic profiles; shotgun analysis can also identify the genes and metabolic pathways represented in the community.
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FAQs
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What determines the accuracy of genotype imputation?
Imputation accuracy depends on the quality and composition of the reference panel, how closely it represents the target population, sequencing coverage, marker density, and the analysis pipeline. A reference panel that does not adequately capture the genetic diversity of the study population can reduce accuracy, particularly for rare variants or genetically distinct lines and breeds.
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How do genomic selection and trait-association studies differ?
Genomic selection uses markers distributed across the genome to predict the breeding value of an individual, usually with a model trained on both genotype and phenotype data. Trait-association studies instead look for specific genomic regions or variants statistically associated with a measured characteristic. One supports selection across the whole genome; the other helps identify loci that may contribute to a particular trait.
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Can the same NGS workflow be used across different plant or animal species?
Many workflow steps can be adapted across species, but the starting material, genome characteristics, and available reference resources still matter. Homogenization and extraction may need to be adjusted for the sample type, while alignment, variant calling, and imputation require an appropriate reference genome and, where applicable, a representative reference panel.
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What should be considered when scaling an agrigenomics workflow?
Scaling requires more than increasing the number of samples processed at once. Laboratories also need sufficient indexing capacity, consistent extraction and library-preparation performance, reliable sample tracking, an appropriate normalization and pooling strategy, adequate sequencing capacity, and an analysis pipeline that can handle the resulting data volume. Automation can reduce hands-on work, but the complete workflow should be planned around the expected sample numbers and turnaround time.
