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RedLibs Sequence Generator RedLibs (standalone)
Sequence Generator parameters
Sequence Generator pipeline
Template (= parent 5’-UTR sequence)
Mutable positions
Max no. of consecutive positions
Max no. of mutable positions
RedLibs is a tool that generates RBS libraries encoded by individual degenerate sequences for straight-forward cloning. These libraries have a user-defined size (i.e. number RBS variants encoded by the degenerate sequence), and span the rTR range from low to high following a uniform distribution to optimally “scan” different expression levels.
Input
In the first window, the user can specify different sequence constraints for the 17 nts upstream of the start codon including the number of mutable and fixed positions (see mouseovers for explanations of the other parameters).
Example:
Template: ACCACAGAGTTGAGAGG
Mutable positions: ACCACANRRNNNAGAGG

Result: RedLibs will suggest libraries on the basis of the template allowing A, C, T or G for the positions designated with N, only A or G for the position designated with R.
In the second window, different target parameters for the library optimization can be specified, most importantly, the desired target size (see mouseovers for explanations of the other parameters).
Example:
Template: ACCACAGAGTTGAGAGG
Mutable positions: ACCACANRRNNNAGAGG
Target library size: 16

Result: Following the sequence constraints above, RedLibs will suggest libraries with a size of 16 RBS variants encoded by single, degenerate sequences.
Output
The output of RedLibs comprises a comma-separated list of the TopX (default: Top10) optimized libraries (*.txt) as well as a graphical output thereof (*.png). The *.txt file specifies the degenerate sequences of the libraries, as well as the sequences of individual library members and their predicted rTR (“strength”). The *.png file displays histograms for the TopX libraries that visualize the distribution of the rTR and the uniformity for each library.

When you use this pipeline in your published work, please do not forget to cite:

  • Jeschek, M., Gerngross, D., & Panke, S. (2016). Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort. Nature communications, 7, 11163 (https://doi.org/10.1038/ncomms11163)
  • Höllerer, S., Papaxanthos, L., Gumpinger, A. C., Fischer, K., Beisel, C., Borgwardt, K., Benenson, Y., & Jeschek, M. (2020). Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nature communications, 11(1), 3551 (https://doi.org/10.1038/s41467-020-17222-4)

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