The predictor is based on a machine learning algorithm that requires; coding sequence length, length of 5’ UTR length, in-frame upstream AUG codons, KOZAK sequence, mRNA folding energy near the start codon and the nucleotides present at positions -6 and -3, respectively as input features. The model is trained using the Random Forest algorithm on endogenous S. cerevisiae transcript. It accurately predicts the initiation rate of endogenous and exogenous transcripts in S. cerevisiae.
TIR optimiser combines the TIR predictor with the Monte-Carlo search algorithm and optimizes gene sequences for a specific initiation rate in S. cerevisiae.
You just need to enter the mRNA sequence of a gene (with 5’ UTR). It can be pasted to the window or uploaded as a file. You also need to supply the start and stop codon indices. Start codon index is the number of the first nucleotide of the start codon when counted from the 5’ end. Similarly, the stop codon index is the exact position of the first nucleotide of the stop codon from the 5' end. The web-server allows predicting initiation rate of multiple sequences using one single input file.
Please make sure that the number of nucleotides in the coding sequence should be a multiple of three, otherwise the web-server will show an error in the code.
You can download the sample file from here
To use TIR optimiser, you need to provide the sequence which you wish to optimize for a target initiation rate. The nucleotide sequence of the desired gene must be pasted to a dialogue box labeled as “enter the sequence”. In addition to that, you also need to supply start and stop codon indices and target initiation rate (see TIR predictor for the definition of start and stop codon indices.) Then, click on the "TIR optimiser" to initiate the gene optimisation process. The optimized sequence (with initiation rate) is displayed in a form of the table which can also be downloaded as a file.
The TIR optimizer utilizes a simulated annealing procedure to effectively locate global minima. By default, we establish a consistent effective temperature variation, ensuring an equal number of iterations for each effective temperature. Users have the option to modify the effective temperature by filling in the dialogue box. It's important to note that the effective temperature is not an actual temperature. We recommend that any variations made should be in the same order of magnitude as the default temperature. You can also change the default number of iterations in the dialogue box. Please note that more iteration steps leads to a longer optimisation time.
You can download the sample file from here
| Click here to add sequence |
OR
Upload file containing sequences:Note: For a large number of iterations, we recommend downloading the offline version of the TIR optimizer from GitHub https://github.com/CBB2023/tirpredictor. You can then run it on your local machine for faster processing.
TIR predictor is a free service offered to the scientific community.Extracts of the information in the web site may be reviewed, reproduced or translated for research or private study but may not be sold or used in conjunction with commercial purposes, and provided any use is subject to an appropriate acknowledgement of the source.
When using TIR predictor, authors should refer to the following publication: Sulagno Chakraborty, Inayat Ullah Irshad, Mahima and Ajeet Sharma."TIR Predictor and Optimizer: Web-tools for accurate prediction of translation initiation rate and precision gene design."
The data and information contained herein and in the results of TIR predictor are provided on an ''as is'' basis and IIT Jammu makes no representations or warranties, either expressed or implied, as to their accuracy, completeness or suitability for a particular purpose. Similarly, IIT Jammu makes no representations or warranties with regard to the non-infringement of third party proprietary rights. Thus, IIT Jammu does not accept any responsibility or liability with regard to the reliance on, and/or use of, such data and information.
For questions or comments concerning the research feel free to
contact
Ajeet Sharma:
ajeet.sharma@iitjammu.ac.in