siRCon - A siRNA Constructor

## Short Summary

## siRNAs short introduction

## siRNA design

Both sequence extensions are also part of our vector system, enabling efficient design and construction of effective siRNAs. If our vector system is selected when using our tool, the fitting overlaps to our vectors are added automatically. More theoretical information about the overhangs and scaffolds can be found here.

## Choosing appropriate design methods

In the following as well as in our software tool siRCon, nucleotide sequences exclusively contain the letter 'T' for sake of simplicity. Please note that in the case of RNA, the corresponding base is uracil.

*et al.*, 2001). These are not compatible with overhangs and scaffold sequences required by the prokaryotic mechanisms. Therefore, we decided to use the rules published by Ui-Tei as an alternative design method (Naito and Ui-Tei, 2012). Furthermore, we adapted the rational siRNA design as it was more suitable for our application (Reynolds

*et al.*, 2004). Both design rules apply only to the 19 nt long target binding sequence.

## Rational siRNA design

*et al.*identified eight criteria that are important for their functionality (Reynolds

*et al.*, 2004). Each criterion gets a score that is either positive or negative, corresponding to its effect on the siRNA. All siRNA candidates with a score above six are potential highly functional siRNAs.

Rule | Score |
---|---|

30%-52% G/C content | +1 |

At least 3 'W' ('A' or 'T') at positions 15-19 | +1 (for each 'A' or 'T') |

Absence of internal repeats (\(T_m \lt 20\)) | +1 |

An 'A' at position 3 | +1 |

An 'A' at position 19 | +1 |

A 'T' at position 19 | +1 |

An 'A' or 'T' at position 19 | -1 |

An 'A', 'C' or 'T' at position 13 | -1 |

## Ui-Tei rule

*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.

- An ‘A’ or ‘T’ at position 19
- A ‘G’ or ‘C’ at position 1
- At least five ‘T’ or ‘A’ residues from positions 13 to 19
- No ‘GC’ stretch more than 9 nt long

## Calculating silencing probability

TThe 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 (Takasaki, 2009). Since we have no data on prokaryotic siRNAs, we use the same prior probability for our predictions.

The gene silencing probability \(P(eff|X)\) is described as: $$ P(eff|X) = \frac{P^{eff} P(X|eff)}{P^{eff} P(X|eff) + P^{inf} P(X|inf)} \qquad (1)$$ The 19 nt siRNA binding sequence is represented by X, where \(x_i^n\) corresponds to the bases adenine, guanine, cytosine or uracil (indexes 1≤n≤4) at sequence position i. The probabilities P(X|eff) and P(X|inf) are calculated based on prior knowledge about siRNA sequences that were shown to be effective respectively ineffective in silencing their target mRNAs. Based on the analysis of 833 effective and 847 ineffective siRNAs, Takasaki et al. determined the likelyhood with which base n occures at position i in an effective/ineffective siRNA sequences, represented by the coefficients \(q_{x_i^n}^{eff}\) and \(q_{x_i^n}^{inf}\) respectively (Takasaki, 2009). These coeffecients are often referred to as frequency ratios of n at position i.

In combination with the frequency ratios it is now possible to calculate the silencing probability for the 19 bp long binding site of siRNAs.

## siRNA selection for RNAi and repression of translation

## Check siRNA

## Command line application

The command line application can be obtained directly here or downloaded from our GitHub repository. To run the command line application, Python 2.7 needs to be installed.

## Graphical Interface usage

Like the command line application, the graphical interface version can either be downloaded directly here, or via our GitHub repository.
In the graphical interface, the modules are accessible via tabs (Figure 6). The last tab contains usage and copyright information.

### Tab 1: siRNA for RNAi

- Insert gene sequence
- Choose TACE vector system (optionally)
- Constructions of siRNAs
- View resulting siRNAs (sense and antisense sequence) and their corresponding probability
- Decide if siRNAs should be saved with MicC scaffold (only if TACE is not used)
- Save results as FASTA file

### Tab 2: siRNA for silencing

- Insert gene sequence
- Choose TACE vector system (optionally)
- Constructions of siRNAs
- View resulting siRNAs (sense and antisense sequence) and their corresponding probability
- Decide if siRNAs should be saved with MicC scaffold (only if TACE is not used)
- Decide if siRNAs should be saved with OmpA scaffold (only if TACE is not used)
- Save results as FASTA file

## Tab 3: Check siRNA

- Insert gene sequence
- Insert siRNA sequences
- Choose method the siRNA was constructed for (siRNA for RNAi or siRNA for silencing)
- Choose if siRNA was constructed for TACE (optionally)
- Validation of entered siRNA for given target gene sequences
- View results
- Save results (optionally)

## Outlook

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**Foley, P.L., Hsieh, P., Luciano, D.J., and Belasco, J.G. (2015).**Specificity and evolutionary conservation of the Escherichia coli RNA pyrophosphohydrolase RppH. J. Biol. Chem. 290: 9478–9486.

**Kibbe, W.A. (2007).**OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Res 35: W43–W46.

**Na, D., Yoo, S.M., Chung, H., Park, H., Park, J.H., and Lee, S.Y. (2013).**Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs. Nat. Biotechnol. 31: 170–174.

**Naito, Y. and Ui-Tei, K. (2012).**siRNA Design Software for a Target Gene-Specific RNA Interference. Front Genet 3.

**Reynolds, A., Leake, D., Boese, Q., Scaringe, S., Marshall, W.S., and Khvorova, A. (2004).**Rational siRNA design for RNA interference. Nature Biotechnology 22: 326–330.

**Siomi, H. and Siomi, M.C. (2009).**On the road to reading the RNA-interference code. Nature 457: 396–404.

**Takasaki, S. (2009).**Selecting effective siRNA target sequences by using Bayes’ theorem. Computational Biology and Chemistry 33: 368–372.

**Ui-Tei, K., Naito, Y., Takahashi, F., Haraguchi, T., Ohki-Hamazaki, H., Juni, A., Ueda, R. and Saigo, K. (2004).**Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. Nucleic Acids Res. 32: 936-948.