Channel Coding for Mutation Resistance
School Project

What if historians today had access to detailed census data from hundreds or thousands of years ago? It's pretty obvious that our understanding of past cultures would be drastically different. However, what are we doing to prevent the same cycle hundreds of years from now? Where are we storing the massive reams of data that future societies would find invaluable?

The short answer is nowhere useful. It's stored on hard disks, or perhaps metallic platters, some of it tucked away in vaults, some of it not. Few of these storage mediums have adequate life spans. All of them demand resources in terms of space, power, maintenance etc. Even if these disks and platters survived for the next 500 years, there is no guarantee that future societies will have the capability to read these ancient forms of data.

What if we used DNA? An incredibly resilient organic molecule, it's no new idea that DNA is a storage medium ripe for exploration. However, an encoding scheme must be used that protects against the imperfections in DNA synthesis and sequencing, as well as natural degredation over time.

Information Theory

This problem was famously modeled by C. E. Shannon in his well known paper A Mathematical Theory of Communication. It's a bit dense but I highly recommend the read.

The gist of it though is that many many things can be thought of as communicating information across a noisy channel. If you know some things about the channel — such as how likely it is that the channel corrupts one bit of information — you can build error correction algorithms to find and fix the errors with a high degree of certainty.

Certainly the challenge of DNA mutating and degrading over time can be described as communication over a noisy channel. One means of fixing these errors is to encode the data using a Hamming Code.

Proposed here is a small stab at a large problem, using Hamming Codes to encode binary data in nucleotides.


GitHub Repository

The Python program found on this GitHub Repo can read any ASCII text file, and encode it in a DNA sequence that is aligned on 13 bits of data in a (13,8) Hamming Code. That is, 5 parity bits for every 8 bits of original data. The output is in FASTA format.

With an encoded file, the same program can be used to decode an encoded DNA sequence, in FASTA format, back to its original content.

The program makes use of the Python multiprocessing library. ###Included Files

  • Python script for encoding and decoding
  • resources/: Several testing text files ranging from small to large sizes

Usage takes several command line arguments:

usage: [-h] [--decode] [--encode] [--workers W] input [output]

positional arguments:
optional arguments:
	-h, --help         show this help message and exit
	--decode, -d       decode boolean flag
	--encode, -e       encode boolean flag
	--workers W, -w W  number of processes to spawn

Only two arguments are required:

Specify encoding or decoding:

  • --encode, -e: Convert a text file into DNA, in FASTA format, encoded with (13,8) Hamming Code
  • --decode, -d: Convert a FASTA file of 13 bit aligned DNA (same format as output of --encode) back to its original format.

Specify input file

  • The first non-flag argument is the input text file. For --encode, this is a normal text file. For --decode this is a FASTA formatted Hamming encoded file.

Optional arguments:

  • -h, --help : Get usage documentation
  • -w W : Specify the number of processes (workers) to spawn to make use of multiple cores. Default is 1.
  • output file: The second non-flag positional argument is the output destination. Default is out.txt.


The following libraries are required for this software: