Abstract
Cold stress limits alfalfa (Medicago sativa) growth and persistence, but public transcriptomic datasets differ widely in genotype, tissue, treatment duration, and experimental design. We integrated RNA-seq data from ten independent BioProjects using a common processing workflow while retaining project-specific structures. A recurrent contrast-level DEG-derived pool of 4,354 genes was ranked by random forest using expression profiles from 240 samples. The original model showed strong internal discrimination (OOB ROC–AUC = 0.937), whereas fully nested leave-one-BioProject-out validation yielded an accuracy of 0.729, balanced accuracy of 0.676, and ROC–AUC of 0.727. PlantTFDB annotation identified MsG0680033896.01, MsG0680033848.01, and MsG0480021906.01 as the three highest-ranked transcription factors. The first two candidates showed greater stability in project-held-out and alternative machine-learning analyses. In project-aware multilevel meta-analysis, neither the primary 50-contrast analysis nor the 54-contrast sensitivity analysis identified genome-wide significant transcripts after Benjamini–Hochberg correction. However, MsG0680033896.01 and MsG0680033848.01 showed predominantly positive effects, positive pooled estimates, and confidence intervals excluding zero in both analyses, whereas MsG0480021906.01 showed weaker directional consistency. Co-expression, promoter prediction, chromosomal localization, and AlphaFold3 modeling provided additional computational context, including localization of the two leading candidates within a Chr6 CBF/DREB1-like-enriched region. These results prioritize MsG0680033896.01 and MsG0680033848.01 as high-confidence computational candidates and retain MsG0480021906.01 as an additional project-sensitive candidate for future functional testing.
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Data availability
The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed at the corresponding author.
Code availability
The analysis code and supporting files used in this study are publicly available in the GitHub repository “Medicago-sativa-cold-TF-framework” (release v1.0.0): https://github.com/jianghuixinchen/Medicago-sativa-cold-TF-framework.
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During the preparation of this manuscript, the authors used ChatGPT, version 5.3 for language editing and grammar refinement. The authors have reviewed and edited the output and take full responsibility for the content of this publication.
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This research was funded by the Natural Science Foundation of Heilongjiang Province (Grant No. PL2025C051), and Basic Research Fund Project for Provincial Undergraduate Universities of Heilongjiang Province (2025-KYYWF-ZR0121).
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Investigation, Methodology, Data curation, Visualization, Writing- Original draft, H.J.; Investigation, Methodology, Software, Validation, Visualization, M.W.; Investigation, Validation, X.Z.; Investigation, Data curation, Z.R.; Investigation, Visualization, D.L.; Investigation, Project administration, G.C.; Conceptualization, Investigation, Writing – review and editing, Supervision, Project administration, and Funding acquisition, S.Y. All authors reviewed the manuscript.
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Supplementary Table S2. GO enrichment results of cold-responsive DEGs (XLSX 729 KB) (download XLSX )
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Supplementary Dataset S1. Complete contrast-level DEG statistics and project-level cold-responsive gene sets (ZIP 7.81 MB)
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Jiang, H., Wang, M., Zhu, X. et al. An integrative multi-project transcriptomic and structural prediction framework identifies candidate cold-responsive transcription factors in Medicago sativa. Funct Integr Genomics 26, 239 (2026). https://doi.org/10.1007/s10142-026-02027-3
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DOI: https://doi.org/10.1007/s10142-026-02027-3