Maximum Exact Matches for High Throughput Genome Subsequence Assembly

Raja, G.; Reddy, U. Srinivasulu

doi:10.1080/03772063.2019.1603085

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…Their method addresses the issues in routine and high-throughput cloning. Raja et al [23] introduce a method named MapReduce Maximum Exact Matches (MR-MEM) for searching and then mapping genome subsequences. The key contribution of their work is the utilization of MapReduce, a parallel execution framework.…”

Section: Past Contributionsmentioning

confidence: 99%

Optimizing the DNA fragment assembly using metaheuristic-based overlap layout consensus approach

Uzma

Halim

2020

Applied Soft Computing

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Section: Past Contributionsmentioning

confidence: 99%

Optimizing the DNA fragment assembly using metaheuristic-based overlap layout consensus approach

Uzma

Halim

2020

Applied Soft Computing

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“…An important fact to consider when designing QBCE is to reduce

T

from size

N

t

through

A_{F}

(this possibly reduces genome size) [21,22], and this in practice increases the initial probability of finding each pattern

P

from

t / N

t' / t

(as discussed later in Lemma 5.2), and thus, amplitude amplification by

G_{O}

takes care of finding each

P

as highly probable result

\approx 1

. Therefore, the method is suitable for finding patterns in genome sequences [23]…”

Section: Quantum‐based Combined Exact (Qbce) Pattern Matchingmentioning

confidence: 99%

Quantum‐based exact pattern matching algorithms for biological sequences

Soni

Rasool

2021

ETRI Journal

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In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in ON time, whereas quantum algorithm design is based on Grover's method, which completes the search in ON time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum‐based combined exact (QBCE) algorithm for the pattern‐matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing‐based exact (QPBE) pattern‐matching algorithm. We show that to find all t occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are OtandON, and the exceptional worst case is bounded by OtandON. Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern‐matching methods.

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