Applications of Convolutional Codes to DNA Codes and Error-Correction

TL;DR

This paper explores how convolutional error-correcting codes can be applied to DNA sequences, improving biological data encoding by leveraging convolutional codes' ability to incorporate neighboring information.

Contribution

It analyzes the properties of convolutional and DNA codes and proposes revisions to enhance convolutional code models for DNA sequence applications.

Findings

Convolutional codes effectively model biological phenomena in DNA sequences.

Revisions improve the error-correction capabilities of convolutional codes for DNA data.

The study highlights the potential of convolutional codes in biocomputation.

Abstract

Convolutional codes are error-correcting linear codes that utilize shift registers to encode. These codes have an arbitrary block size and they can incorporate both past and current information bits. DNA codes represent DNA sequences and are defined as sets of words comprised of the alphabet A, C, T, G satisfying certain mathematical bounds and constraints. The application of convolutional code models to DNA codes is a growing field of biocomputation. As opposed to block codes, convolutional codes factor in nearby information bits, which makes them an optimal model for representing biological phenomena. This study explores the properties of both convolutional codes and DNA codes, as well as how convolutional codes are applied to DNA codes. It also proposes revisions to improve a current convolutional code model for DNA sequences.