DNA Data Storage Research @ Imperial College

DNA Data Storage Overview

DNA, the blueprint of life, is not only responsible for encoding genetic information but also holds incredible potential as a data storage medium  to store archival data in the long term. Find out more about DNA data storage and the research we do at Imperial in the two videos below which feature our research and the remainder of the content. Do not forget to read our DNA storage FAQ and read about our work in a recent article in Chemistry World to which we contributed!

Read our DNA Storage FAQ

Our Research Focus

DNA data storage starts with translating any type of binary files (documents, movies, music, databases) to a binary representation, encoding the binary representation to sequences while incorporating error correction, and then synthesising the sequence representation to physical DNA molecules. The resulting molecules may be stored for years if not decades after which they are sequenced to recover the data. The resulting sequencing data is aligned, decoded and potential errors are fixed based on incorporated error correction codes to finally obtain the digital files encoded.

Our research focuses on all stages of the DNA data storage cycle:
      – Encoding, decoding and error correction for DNA data storage
      – Optimising synthesis processes for DNA data storage
      – Physical storage of DNA molecules for data storage
      – Sequencing for DNA storage based on Machine Learning/AI methods

Making DNA Data Storage a Reality

Our ultimate goal to make it a staple of modern data centres to store archival data in the very long term.

DNA Data Storage

DNA Data Storage, the process of storing arbitrary binary data in DNA sequences is actively researched by us and the community. Its properties, primarily durability and compact form factor, make it an ideal storage media for archiving data for decades. Watch the video here to see what makes DNA an ideal storage media and how it all works.

Processing Data in DNA

Once data is stored in DNA, biomolecular processes can be used to process it on an unprecedented scale. More precisely, combinatorial problems (e.g., databases joins, travelling salesperson problems and others) can be solved in DNA very fast thanks to the unprecedented level of parallelism and also very energy efficient comapred to traditional computing. Watch the video here to learn more.

Modelling DNA Storage as a Constrained Channel

Key to storing binary data in synthetic in DNA is the translation between the binary representation of digital data to the quaternary domain of DNA. This translation must adhere to constraints imposed by the synthesis and sequencing processes used to write and read respectively. A technological advance in either process changes the constraints and renders current encoding schemes obsolete. In this line of work we present a recipe for taking constraints and producing an appropriate encoding scheme. Such a mechanism allows moving the encoding in lockstep with the technological advances in the underlying processes. We further show a method to understand trade-offs in constraints for a given overhead of bits needed to meet such constraints

Publications

Selected publications resulting from our research

2022

Hunter, W., Low, C., Heinis, T.,

Generating Synthetic Data for DNA Origami-based Information Storage Systems Conference

2022.

BibTeX | Links:

2021

Omer S. Sella; Amir Apelbaum; Thomas Heinis; Jasmine Quah; Andrew W. Moore

DNA archival storage, a bottom up approach Conference

ACM Workshop on Hot Topics in storage and File Systems, 2021.

BibTeX

Eugenio Marinelli, Eddy Ghabach, Thomas Bolbroe, Omer Sella, Thomas Heinis, Raja Appuswamy

DNA4DNA: Preserving Culturally Significant Digital Data with Synthetic DNA Journal Article

In: 17th International Conference on Digital Preservation (iPRES 2021), 2021.

BibTeX

Eugenio Marinelli, Eddy Ghabach, Thomas Bolbroe, Omer Sella, Thomas Heinis, Raja Appuswamy

Digital Preservation with Synthetic DNA Journal Article

In: 37eme Conference sur la Gestion de Donnees – Principes, Technologies et Applications (BDA 2021), 2021.

BibTeX

Eva Gil San Antonio, Thomas Heinis, Louis Carteron, Melpomeni Dimoopoulou, Marc Antonini

Nanopore Sequencing Simulator or DNA Data Storage Journal Article

In: Visual Communications and Image Processing (VCIP 2021), 2021.

BibTeX

2019

Raja Appuswamy, Kevin Le Brigand, Pascal Barbry, Marc Antonini, Olivier Madderson, Paul Freemont, James McDonald, Thomas Heinis

OligoArchive: Using DNA in the DBMS Storage Hierarchy. Proceedings Article

In: Conference on Innovative Data Systems Research (CIDR '19) , 2019.

BibTeX | Links:

Thomas Heinis, Jamie J Alnasir

Survey of information encoding techniques for dna Journal Article

In: arXiv preprint arXiv:1906.11062, 2019.

BibTeX | Links:

Meet the Team

Thomas Heinis

Lab Director

Omer Sella

Omer S. Sella

Researcher

Roman Sokolovskii

Research Associate

William Hunter

William Hunter

Doctorate Research

Zijian (James) Zhou

Doctorate Research

Samira Brunmayr

MEng Student

Jamie J. Alnasir

Research Associate

Samantha Kwok

M.Sc. Student

Jasmine Quah

M.Sc. Student

Chandler Low

M.Sc. Student

Join us! We have the following student, PhD and PostDoc opportunities available…

Partners & Collaborators

Helixworks
Kilobaser

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