This repository includes datasets from the following publications. 1 Grass, R. N., Heckel, R., Puddu, M., Paunescu, D., & Stark, W. J. Robust chemical preservation of digital information on DNA in silica with error-correcting codes. Angewandte Chemie International Edition, 54, 8, 2552–2555 (2015)2 Erlich, Y. & Zielinski, D. DNA fountain enables a robust and efficient storage architecture. Science, 355, 6328, 950–954 (2017).3 Srinivasavaradhan, S. R., Gopi, S., Pfister, H. D. & Yekhanin S. Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage. in 2021 IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, 2453–2458 (2021). The datasets are given in a binned format to enhance the reproducibility of the results presented in the paper. Bar-Lev, D., Orr, I., Sabary, O., Etzion T., & Yakkobi, E.   Scalable and robust DNA-based storage via coding theory and deep learning. 2024. Detailed description of the formatThe binned format was created using the binning step described in the paper ("Scalable and robust DNA-based storage via coding theory and deep learning"). Each cluster of reads appears in the file with a header followed by the reads. More specifically:The header consists of 2 lines, the first corresponds to the encoded sequence of the clusters, and the second is a line of 18x“*” that should be ignoredThe reads in the clusters are provided after the header, where each read is given in a separate lineEach cluster ends with two empty linesData processingTo ease the processing of our datasets, we also provide the following Python scripts (see https://github.com/itaiorr/Deep-DNA-based-storage)reads_preprocessor.py includes our preprocessing procedure for the raw reads. The procedure detects  and truncates the primers binning.py - parses the file of the binned reads and creates two Python dictionaries. In the first dictionary, each key is an encoded sequence,  and the value is a  list of the reads in the cluster. In the second dictionary the keys are the index and the value is a list of the reads in the cluster.

This repository includes datasets from the following publications. 1 Grass, R. N., Heckel, R., Puddu, M., Paunescu, D., & Stark, W. J. Robust chemical preservation of digital information on DNA in silica with error-correcting codes. Angewandte Chemie International Edition, 54, 8, 2552–2555 (2015)2 Erlich, Y. & Zielinski, D. DNA fountain enables a robust and efficient storage architecture. Science, 355, 6328, 950–954 (2017).3 Srinivasavaradhan, S. R., Gopi, S., Pfister, H. D. & Yekhanin S. Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage. in 2021 IEEE International Symposium on Information Theory (ISIT), Melbourne, Australia, 2453–2458 (2021). The datasets are given in a binned format to enhance the reproducibility of the results presented in the paper. Bar-Lev, D., Orr, I., Sabary, O., Etzion T., & Yakkobi, E.   Scalable and robust DNA-based storage via coding theory and deep learning. 2024. Detailed description of the formatThe binned format was created using the binning step described in the paper ("Scalable and robust DNA-based storage via coding theory and deep learning"). Each cluster of reads appears in the file with a header followed by the reads. More specifically:The header consists of 2 lines, the first corresponds to the encoded sequence of the clusters, and the second is a line of 18x“*” that should be ignoredThe reads in the clusters are provided after the header, where each read is given in a separate lineEach cluster ends with two empty linesData processingTo ease the processing of our datasets, we also provide the following Python scripts (see https://github.com/itaiorr/Deep-DNA-based-storage)reads_preprocessor.py includes our preprocessing procedure for the raw reads. The procedure detects  and truncates the primers binning.py - parses the file of the binned reads and creates two Python dictionaries. In the first dictionary, each key is an encoded sequence,  and the value is a  list of the reads in the cluster. In the second dictionary the keys are the index and the value is a list of the reads in the cluster.