Alfalfa Flower Color Transcriptomic and Metabolomic Profiles

580 common significant differentially expressed genes (CSDEGs) were identified across five alfalfa flower types, from white to four shades of purple. The metabolomics data quantifies two key anthocyanins, delphinidin-3,5-O-diglucoside and malvidin-3,5-O-diglucoside, whose content increases with flower darkness. This dataset from Hongqi Du, published in 2026, integrates transcriptomic and metabolomic analyses to investigate the regulatory mechanisms of purple color variation.

Use Cases

• Identify candidate transcription factors like MYB and bHLH from the 24 TFs within the 108 regularly trending CSDEGs to study their role in pigment regulation.
• Model the correlation between the expression levels of novel genes (e.g., MSTRG.59017, MSTRG.14861) and the quantified contents of delphinidin-3,5-O-diglucoside or malvidin-3,5-O-diglucoside across the five flower types.
• Analyze the 29 CSDEGs definitively involved in the anthocyanin biosynthesis chain to map genetic pathways responsible for the purple color gradient in alfalfa flowers.
• Investigate the decreasing ratio of blue to red pigments across the ZH1 to ZH4 gradient using the metabolomic pigment data to understand color hue variation.

Strengths

• Integrated multi-omics data combining transcriptomic (RNA-seq) and metabolomic (LC-ESI-MS/MS) profiling for a systems biology view.
• Focus on 580 common significant differentially expressed genes (CSDEGs) filtered from comparisons of four purple flower types against white.
• Quantified metabolite data for two dominant anthocyanins, delphinidin-3,5-O-diglucoside and malvidin-3,5-O-diglucoside, across five distinct phenotypic classes.

Limitations

• The dataset is small in scale at 6.5 MB, suggesting it contains processed summary or results data rather than raw sequencing reads.
• Sample scope is limited to five specific alfalfa flower types (white and four purple shades), which may not represent the full genetic diversity of the species.
• The XLS file format may require preprocessing for programmatic analysis and lacks the raw columnar structure typical of primary omics data.

Provenance

Source

figshare, authored by Hongqi Du.

Collection Method

Integrated analysis using LC-ESI-MS/MS for metabolomics and RNA-seq mapped to the Medicago reference genome for transcriptomics; DEGs identified with DESeq2.

Freshness

Last updated March 2026.