This repository contains the data analysis, in the form of Jupyter Notebooks and data files, for the error characterization and figures in the following publication:
Andreas L. Gimpel, Wendelin J. Stark, Reinhard Heckel, Robert N. Grass. Experimental quantification of errors and biases in DNA data storage for a digital twin. Manuscript in preparation.
The Python package dt4dds, providing a customizable, digital representation of the widely-used DNA data storage workflow involving array synthesis, PCR, Aging, and Sequencing-By-Synthesis, is found in the dt4dds repository.
The data analysis has been tested and performed on Windwos 10 using Python 3.10. The following Python packages are required:
dt4dds
statsmodels
scikit-learn
To clone this repository from Github, use
git clone https://github.com/fml-ethz/dt4dds_notebooks
cd dt4dds_notebooksSome of the analysis in this repository is based on sequencing data from other publications.
Yaniv Erlich, Dina Zielinski. DNA Fountain enables a robust and efficient storage architecture. Science 355, 950-954 (2017). DOI:10.1126/science.aaj2038
The MasterPool and DeepCopy datasets were used for the characterization of the efficiency distribution. The data is publicly available in the ENA archives here and here.
Koch, J., Gantenbein, S., Masania, K. et al. A DNA-of-things storage architecture to create materials with embedded memory. Nat Biotechnol 38, 39–43 (2020). https://doi.org/10.1038/s41587-019-0356-z
All Bunny datasets, including the generations, were used for the characterization of the efficiency distribution and for external validation. The data is publicly available in this figshare repository.
Chen, YJ., Takahashi, C.N., Organick, L. et al. Quantifying molecular bias in DNA data storage. Nat Commun 11, 3264 (2020). https://doi.org/10.1038/s41467-020-16958-3
The datasets run36.npy and run42.py were used for the characterization of the efficiency distribution. The data is publicly available in this GitHub repository.