DNA-fragment-clustering 0.1.2

DNA fragment clustering and grouping tool

The Coli Toolkit (CTK): An extension of the modular Yeast Toolkit for use in E. coli

This python package contains the code responsible for clustering small DNA fragments in preparation for de novo synthesis. The project is also described in the paper: The Coli Toolkit (CTK): An extension of the modular Yeast Toolkit to the E. coli chassis by Jacob Mejlsted^1,2,3, Erik Kubaczka^1,3, Sebastian Wirth^1,3, and Heinz Koeppl^1,3*.

Install

pip install DNA-fragment-clustering

Python Usage

from DNA_fragment_clustering import DNA_clustering DNA_clustering("input.csv", aggressive = False)

Clustering of de novo DNA fragments

The Python executable DNA_fragments.py performs clustering and grouping of de novo DNA fragments meant for synthesis. From the methods:

The clustering software uses the Levenshtein similarity matrix to compute the differences between the various fragments that the user wants to synthesize. Using affinity propagation, the software defines clusters with high sequence similarity. From this, groups are made of up to three sequences from distinct clusters to obtain low sequence similarity in the final DNA sequence sent for synthesis. If the aggressive clustering option is selected, groups only containing one sequence are concatenated together to minimize the amount of DNA needed to be synthetized. Following the grouping, the DNA sequences are concatenated and the restriction sites for BsmBI are exchanged to BbsI and BspMI for the second and third occurrences, respectively. The final sequence is then outputted as a .csv file to the same folder as the input file was chosen from.

Input format

The input .csv files were based on the output format of Benchling.
The format uses three columns: Name, Author, Sequence These are the name of the DNA fragment, the author/owner of the DNA sequences, and sequence in question, respectively.

Citation

If you use this code or the data provided here, please cite the corresponding paper.

License

The code and the data is available under an MIT License. Please cite the corresponding paper if you use our code and/or data.

Funding & Acknowledgments

The authors acknowledge Anika Kofod Petersen for her work on the prototype of the de novo synthesis clustering pipeline. The work was made possible with the support of a scholarship from the German Academic Exchange Service (DAAD), project number 91877921 to J.M. E.K. was supported by ERC-PoC grant PLATE (101082333). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. We acknowledge the use of Python and the aforementioned Python packages.