RNAJog help document


RNAJog is a tool designed for optimizing the coding sequence (CDS) of mRNA to achieve high protein expression levels. RNAJog can generate codon sequences with high codon adaptation index (CAI) and low minimum free energy (MFE), ensuring enhanced translational efficiency and mRNA stability. This tool enables users to optimize mRNA sequences for their target proteins or existing mRNA sequences. The following guidance will assist you in generating your optimized mRNA sequences effectively.


 Note:
* The software is free to academic users ONLY; For commercial usage, please contact with us.
* Please do not use any crawler tools or submit frequently in a very short time .

Guidance

Step 1: Select the Optimization Model
Choose between two distinct models: RNAJog and RNAJog-Zero. The key difference lies in their training datasets:
  • RNAJog is trained on the iCodon dataset.
  • RNAJog-Zero is trained on randomly generated sequences.

Step 2: Input the Sequence
Provide the protein or RNA sequence you wish to optimize. For guidance on the input format, click the Example button.

Step 3: Configure Optimization Settings
Provide the protein or RNA sequence you wish to optimize. For guidance on the input format, click the Example button.

(1).   MFE-CAI Weight:
  This parameter balances the importance of Minimum Free Energy (MFE) and Codon Adaptation Index (CAI) during optimization.
    • A higher weight prioritizes MFE optimization.
    • A lower weight prioritizes CAI optimization.

(2).   Excluded Subsequences:
  Specify any subsequences (e.g., XhoI: CTCGAG, BspQI: GCTCTTC) that must be avoided in the optimized codon sequence. The algorithm will exclude these subsequences. If exclusion is impossible, no output will be generated.

(3).   Expression Host:
  Select the host organism for mRNA expression. Options include Homo sapiens and Escherichia coli. For other species, provide the corresponding codon usage table.

(4).   Decoding Strategy:
  Choose between:
    • Greedy: Selects the codon with the highest probability at each step.
    • Sample: Samples codons based on their probabilities. If sample is selected, you can further specify the sample temperature and number of samples in the extended interface.

(5).   Submit and Receive Results:
  Enter your email address and click Submit. Upon successful processing, you will receive the optimized results in a CSV file.



Output

If protein is chosen as the input, the output file will contain the optimized codon sequences, along with their corresponding decoding strategy, sequence length, MFE-CAI weight, Minimum Free Energy (MFE), and Codon Adaptation Index (CAI).

If RNA is chosen as the input, the output file will append the provided RNA sequence to the top of the table, with its decoding strategy labeled as "input" in the table.