Biology subjects

Organick, L.

Publications and source records attributed to Organick, L..

3 recordsLinked to original sources

bioRxiv2019

Quantifying Molecular Bias in DNA Data Storage

DNA has recently emerged as an attractive medium for future digital data storage because of its extremely high information density and potential longevity. Recent work has shown promising results in developing proof-of-principle prototype systems. However, very uneven (biased) sequencing coverage distributions have been reported, which indicates inefficiencies in the storage process and points to optimization opportunities. These deviations from the average coverage in oligonucleotide copy distribution result in sequence drop-out and make error-free data retrieval from DNA more challenging. The uneven copy distribution was believed to stem from the underlying molecular processes, but the interplay between these molecular processes and the copy number distribution has been poorly understood until now. In this paper, we use millions of unique sequences from a DNA-based digital data archival system to study the oligonucleotide copy unevenness problem and show that two important sources of bias are the synthesis process and the Polymerase Chain Reaction (PCR) process. By mapping the sequencing coverage of a large complex oligonucleotide pool back to its spatial distribution on the synthesis chip, we find that significant bias comes from array-based oligonucleotide synthesis. We also find that PCR stochasticity is another main driver of oligonucleotide copy variation. Based on these findings, we develop a statistical model for each molecular process as well as the overall process and compare the predicted bias with our experimental data. We further use our model to explore the trade-offs between synthesis bias, storage physical density and sequencing redundancy, providing insights for engineering efficient, robust DNA data storage systems.

synthetic biology↗

bioRxiv2018

Exploiting Index Cross-Talk to Modify Variant Calls

Modern next-generation DNA sequencers support multiplex sequencing to improve throughput and decrease costs. This is done by pooling and sequencing samples together in parallel, which are later demultiplexed according to their unique indexes1, 2. When reads are assigned to the wrong index, called index cross-talk, information is leaked between samples3-6. This creates a physical information side-channel, a well known class of vulnerabilities in information security7-10, that may be used to modify downstream results. Here we demonstrate the feasibility of such an attack through the use of a separately indexed library that causes a wild-type human exome to be misclassified as heterozygous at the sickle-cell locus. Simple methods can be used to minimize or detect attempts to modify genetic variants using this side-channel, such as filtering by read quality or finding outliers in read coverage. To further minimize this risk we recommend the use of new library preparation methods that reduce index cross-talk, like unique dual indexes11, 12, whenever samples are sequenced together in important applications. Biotechnology that interfaces molecular and digital information, like DNA sequencers, may have security risks typically associated with information systems, including the side-channel vulnerability described in this study. We encourage the community to consider the security of genomics-information pipelines before they reach mass adoption.

bioengineering↗

bioRxiv2017

Scaling up DNA data storage and randomaccess retrieval

Current storage technologies can no longer keep pace with exponentially growing amounts of data. 1 Synthetic DNA offers an attractive alternative due to its potential information density of ~ 1018 B/mm3, 107 times denser than magnetic tape, and potential durability of thousands of years.2 Recent advances in DNA data storage have highlighted technical challenges, in particular, coding and random access, but have stored only modest amounts of data in synthetic DNA. 3,4,5 This paper demonstrates an end-to-end approach toward the viability of DNA data storage with large-scale random access. We encoded and stored 35 distinct files, totaling 200MB of data, in more than 13 million DNA oligonucleotides (about 2 billion nucleotides in total) and fully recovered the data with no bit errors, representing an advance of almost an order of magnitude compared to prior work. 6 Our data curation focused on technologically advanced data types and historical relevance, including the Universal Declaration of Human Rights in over 100 languages,7 a high-definition music video of the band OK Go,8 and a CropTrust database of the seeds stored in the Svalbard Global Seed Vault.9 We developed a random access methodology based on selective amplification, for which we designed and validated a large library of primers, and successfully retrieved arbitrarily chosen items from a subset of our pool containing 10.3 million DNA sequences. Moreover, we developed a novel coding scheme that dramatically reduces the physical redundancy (sequencing read coverage) required for error-free decoding to a median of 5x, while maintaining levels of logical redundancy comparable to the best prior codes. We further stress-tested our coding approach by successfully decoding a file using the more error-prone nanopore-based sequencing. We provide a detailed analysis of errors in the process of writing, storing, and reading data from synthetic DNA at a large scale, which helps characterize DNA as a storage medium and justify our coding approach. Thus, we have demonstrated a significant improvement in data volume, random access, and encoding/decoding schemes that contribute to a whole-system vision for DNA data storage.

bioengineering↗