Quantum gate algorithm for reference-guided DNA sequence alignment.

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4B bits or ∼760MB of storage (National Institute of General Medical Sciences, n.d.). Today's most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.

Quantum algorithm for de novo DNA sequence assembly based on quantum walks on graphs.

De novo DNA sequence assembly is based on finding paths in overlap graphs, which is a NP-hard problem. We developed a quantum algorithm for de novo assembly based on quantum walks in graphs. The overlap graph is partitioned repeatedly to smaller graphs that form a hierarchical structure. We use quantum walks to find paths in low rank graphs and a quantum algorithm that finds Hamiltonian paths in high hierarchical rank. We tested the partitioning quantum algorithm, as well as the quantum algorithm that finds Hamiltonian paths in high hierarchical rank and confirmed its correct operation using Qiskit. We developed a custom simulation for quantum walks to search for paths in low rank graphs. The approach described in this paper may serve as a basis for the development of efficient quantum algorithms that solve the de novo DNA assembly problem.

Regulating the quorum sensing signalling circuit to control bacterial virulence: in silico analysis.

Pathogenic bacteria employ a communication mechanism, known as quorum sensing (QS), to obtain information about their cell density and to synchronise their behaviour. Most bacteria species use QS signalling circuits to optimise the secretion of virulence factors that damage their host. Recently, QS has been recognised as a target for antimicrobial drugs that can control bacterial infections. Here the QS process is modelled as a state transition graph with transitions depending on the diffusion and local concentration of the QS molecules (autoinducers). Based on this model a simulation tool has been developed to simulate the QS process in both open and confined spaces. Using this simulation tool a number of numerical experiments has been carried out with various strategies of QS circuit regulation. The results of these experiments showed that regulation of the QS signalling circuit can lead to significantly reduced bacterial virulence.