Transcriptome datasets of maize plant cultures treated with humic- and amino acids
Kincső Decsi,
Mostafa Ahmed,
Roquia Rizk,
Donia Abdul-Hamid,
Zsolt Vaszily,
Zoltán Tóth
Affiliations
Kincső Decsi
Institute of Agronomy, Georgikon Campus, Hungarian University of Agriculture and Life Sciences, 8360 Keszthely, 7 Festetics street, Hungary
Mostafa Ahmed
Festetics Doctoral School, Institute of Agronomy, Georgikon Campus, Hungarian University of Agriculture and Life Sciencess, 8360 Keszthely, 16 Deák Ferenc street, Hungary; Department of Agricultural Biochemistry, Faculty of Agriculture, Cairo University, Giza 12613, University Street, Egypt; Corresponding author.
Roquia Rizk
Institute of Agronomy, Georgikon Campus, Hungarian University of Agriculture and Life Sciences, 8360 Keszthely, 7 Festetics street, Hungary; Department of Agricultural Biochemistry, Faculty of Agriculture, Cairo University, Giza 12613, University Street, Egypt
Donia Abdul-Hamid
Heavy Metals Department, Central Laboratory for The Analysis of Pesticides and Heavy Metals in Food (QCAP), Dokki, Cairo 12311, 77 Nadi El-said street, Egypt
There has been a global surge in the need for commercially accessible plant conditioners that are derived from natural ingredients and are therefore environmentally benign. Currently, sustainable agriculture and minimizing the ecological impact are of great importance. Preparations that contain commonly used humic acids and/or natural amino acids are ideal for meeting these criteria. An investigation was conducted to examine the impact of three plant foliar fertilizers containing humic acid and one fertilizer containing a combination of humic and amino acids on maize crops. By employing the shallow mRNA sequencing technique, we acquired datasets that, once processed, are ideal for investigating the impacts of the foliar fertilizers examined in the study. Five SRA datasets were uploaded to NCBI. These datasets include the TSA (Transcriptome Shotgun Assembly), the contigs that were blasted, mapped, and annotated from the pre-processed datasets, as well as the count table obtained from the RNA-seq read quantification. All of these data are included in the Mendeley database. In the future, the databases will enable the investigation of alterations in plant biochemical processes at the gene expression level.
