Difference between revisions of "Team:Bielefeld-CeBiTec/Software"

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The gene silencing probability can be described as:
 
The gene silencing probability can be described as:
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$$ P(eff|X) = \frac{(P^eff P(X|eff)}{P(X)}  $$
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$$ P(eff|X) = \frac{(P^(eff) P(X|eff)}{P(X)}  $$
 
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Peff is the prior probability 0.1 as mentioned above. The siRNA sequence is represented by X, where X1, X2 … Xn belong to the possible nucleotides adenine, guanine, cytosine and thymine. As P(X|eff) is the probability, that the given siRNA sequence will effectively silence if the nucleotides belong to the frequent nucleotides of common effective siRNAs, it is computed as the product of the probabilities that a particular nucleotide is located at a particular position of the siRNA:
 
Peff is the prior probability 0.1 as mentioned above. The siRNA sequence is represented by X, where X1, X2 … Xn belong to the possible nucleotides adenine, guanine, cytosine and thymine. As P(X|eff) is the probability, that the given siRNA sequence will effectively silence if the nucleotides belong to the frequent nucleotides of common effective siRNAs, it is computed as the product of the probabilities that a particular nucleotide is located at a particular position of the siRNA:
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$$ P(X|eff) = \prod_i=1^19 q_(x_i^n)^(eff) $$
 
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Revision as of 05:33, 13 October 2018

siRCon - A siRNA Constructor
In our Project we introduce RNA interference (RNAi) and silencing with siRNAs as an alternative for CRISPR/Cas. To make use of our silencing vector system Tace, functional siRNA for prokaryotic organisms must be determined. Thus, we developed a siRNA construction tool, which can find possible siRNAs for a given gene sequence and calculate their gene silencing probability. It consists of the three modules RNAi, siRNA and check siRNA. Obtained siRNAs are perfectly compatible with our siRNA vector system. To the best of our knowledge, this is the first tool dedicated to predicting customized siRNA for the application in prokaryotes. This Python tool comes in two versions: a command line application and an easy-to-use graphical interface.

Choosing appropriate design methods

In 2012 the SYSU-Software Team integrated an siRNA cDNA designer as a small part in their project. siRNAs designed with this tool were applicable in eukaryotic organisms. They included two different design methods: Tom Tuschl’s method and Rational siRNA design.
Tom Tuschl’s method focuses basically on the existence of 5’ and 3’ ‘TT’ overhangs (Figure X)(Elbashir et al., 2001). These are not compatible with overhangs and scaffold sequences necessary for the prokaryotic mechanisms. So, we decided to use the Ui-Tei rules as an alternative design method (Naito and Ui-Tei, 2012). We adapted the Rational siRNA design since it was compatible for our uses. Both design rules apply only to the 19bp long target binding sequence.

Rational siRNA design

By a systematic analysis of 180 eukaryotic siRNAs Reynolds et al. identified eight criteria that are important for the functionality of siRNA (Reynolds et al., 2004). Each criterion gets a score that can be either positive or negative. All siRNA that have a score higher than six are potential high functional siRNAs.
The melting Temperature Tm is calculated as followed (Kibbe, 2007):
The tool checks each criterion and only consider siRNAs with a score higher than six for further steps.

Ui-Tei rule

Ui-Tei et al analyzed 62 eukaryotic siRNAs and identified four design rules for effective siRNAs (Ui-Tei, 2004). Only siRNAs fulfilling all four criteria are considered functional siRNAs.

Calculating silencing probability

Not only the sequences of possible effective siRNAs are to be determined and returned by the tool, but also the probability with which they are effective. This probability can be calculated with the help of Bayes’ theorem. This theorem calculates probabilities of dependent events. The following calculations and formular are based on Takasaki (2009).
The initial hypothesis is that the given siRNA effectively silences an mRNA. To perform the calculations a prior probability is necessary. The prior probability for effective gene silencing of mammalian genes can be obtained from former siRNA experiments and is approximately 0.1. Since we have no data on prokaryotic siRNAs we use the same prior probability for our prediction.
The gene silencing probability can be described as: $$ P(eff|X) = \frac{(P^(eff) P(X|eff)}{P(X)} $$
Peff is the prior probability 0.1 as mentioned above. The siRNA sequence is represented by X, where X1, X2 … Xn belong to the possible nucleotides adenine, guanine, cytosine and thymine. As P(X|eff) is the probability, that the given siRNA sequence will effectively silence if the nucleotides belong to the frequent nucleotides of common effective siRNAs, it is computed as the product of the probabilities that a particular nucleotide is located at a particular position of the siRNA: $$ P(X|eff) = \prod_i=1^19 q_(x_i^n)^(eff) $$
qeff xin indicates how likely the occurrence of base n is at position i based on known effective siRNAs. It can also be called frequency ratio of n at position i. The last element P(X) of formula (1) is the possibility that X will effectively silence the target sequence. It is the sum of the probability that X is effective if its nucleotides are found in effective siRNAs plus the probability that X is effective if its nucleotides are found in ineffective siRNAs. Both probabilities are weighted with the prior probabilities Peff and Pinf = 1-Peff.
P(X|inf) is calculated similar to P(X|eff) and is the probability that X will effectively silence if the nucleotides belong to the frequent nucleotides of common ineffective siRNAs
In this case, qinf xin indicates how likely the occurrence of base n is at position i based on known ineffective siRNAs.
With all defined formulas, formula 1 can now be calculated as follows:
In order to actually calculate the silencing probability, only the frequency ratios of the individual nucleotides at positions 1 to 19 are missing. These could be taken from the same publication from Takasaki as the calculations.
For the frequency ratios 833 effective and 847 ineffective siRNAs from previous publications were analyzed. For each nucleotide, the probability of occurrence was determined for each position of the siRNA. Different models were taken into account in the calculation. First of all, the occurrence of the different nucleotides at positions 1 to 19 can be considered independently. The probabilities for each position are then calculated independently. However, the occurrences of the nucleotides can also be considered dependently. This means the occurrence of a nucleotide depends on the nucleotide at the position before. For the calculation of dependent probabilities, the Simple Markow Model was used. It has been found that the resulting silence probability is most accurate when the frequency ratios of the effective siRNAs are calculated dependent and the frequency ratios of the ineffective siRNAs are calculated independent. All frequency ratios are summarized in Table X and Table Y.
Together with the frequency ratios it is now possible to calculate the silencing probability for 19 bp long siRNAs.

siRNA overhangs and scaffolds

In order to achieve effective gene silencing or knockdown, the 19 bp binding sequence must be supplemented with overhangs. There are different sequences that can be added to the binding sequence for different functionalities.
In Figure N the scheme of RNAi siRNAs is shown. For the siRNA to be recognized by the RNase E, the 5’ end of the siRNA have to start with the nucleotides adenine and guanine (Foley et al., 2015). Furthermore, the nucleotides at position three and four are not allowed to match with the target mRNA. At the 3’ end of the siRNA the MicC scaffold is added, which recruits the RNase E and facilitates the hybridization of siRNA and target mRNA (Na et al., 2013).
Figure M shows the scheme of a siRNA that should only silence the mRNA target. To achieve a higher stability of the siRNA, the OmpA scaffold is added at the 5’ end. In addition, the hybridization of the siRNA and the target mRNA should be facilitated by MicC again.
These overhang and scaffold sequences are also part of our vector system. If the vector system is selected when using our tool, the fitting overlaps to the vectors are added automatically. More theoretical information about the overhangs and scaffolds can be found here.

Check siRNA (Alpha)

Beside the construction of siRNAs, we also implemented a check siRNA functionality, which is still under construction and only supports a few features right know. For a given target sequence and a corresponding siRNA it is checked whether the siRNA might bind to its target and how well it fulfills the described criteria’s. Furthermore, its silence effectivity is calculated.

Command line application usage

Graphical Interface usage

Outlook