Hybrid performance in maize predicted with combinations of omics data
We explored genomics, transcriptomics (mRNA and sRNA) and metabolomics of maize parent lines as predictors for agronomic performance of single-cross hybrids. Our results indicate that the merit of any individual predictor is trait dependent and that combining predictors has advantages for application across traits. We conclude that downstream “omics” can complement genomics for hybrid prediction and thereby contribute to more efficient selection of hybrid candidates.
Provenance — who produced it, who reused it
Linked to 1 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
Deep data QC
100/100 · AStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
Maize bulk RNA-seq. Perfect A-grade (100/100) with flawless Q30 (100%), perfect Q20 (100%), and exceptional mean base quality (70.2), all measured. Perfect quality metrics ensure maximum confidence in plant transcriptome quantification; though near-perfect scores warrant independent verification to rule out metric inflation.
The A grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0