The discrepancy between mRNAs levels and translation effectivity in eukaryotic cells, in particular in plant cells, encountered by researchers at any step of organism development and under influence of different stress factors at an entire organism. Thus, nowadays researchers direct close attention to study fine translation mechanisms.
As we know mRNA has regulatory codes which define the individual mRNA fate during translation. In silico analysis of different mRNA regions is applied by scientists for detection such regulatory codes and motives. For the purpose of such regulatory codes discovery in mRNA and their correlation with translational efficiency we have created online database JetGene (https://jetgene.bioset.org/) that contains cDNA, CDS, 5’-/3’-UTR sequences of six groups of living organisms. This should be noted all information about CDS and cDNA is downloaded and updated from Ensembl regularly. JetGene has wide toolkit and very friendly user interface. JetGene allows make a comparative analysis of sequences, namely: (i) to estimate the variation of length, nucleotide composition, codon usage frequency, to analyze GC-content, CpG-islands, to study nucleotides surrounding of the start codon and much more; (ii) to identify and define statistically significant representation of potential regulatory contexts at mRNA with different translation efficiency. It should be mentioned that the analysis could be performed both by full-length transcript, by interval of transcript and by coding/non-coding regions. It’s important to note that sequences selected by for the analysis by a user can be extracted in fasta-format from JetGene at any step of the work. The graphical interpretation of analysis accompanies every phase of the study. Such pleasant details are greatly facilitated the work of any researcher. Moreover, beta-version of JetGene (https://beta.bioset.org) allows user to compare two mRNA samples and apply omics data for searching and prediction regulatory determinants of translation.
Supported by the Russian Science Foundation (project no. 18-14-00026; IVG-P).
