Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters

Najam, Maleeha; Rasool, Raihan Ur; Ahmad, Hafiz Farooq; Ashraf, Usman; Malik, Asad Waqar

doi:10.1155/2019/7074387

Cited by 19 publications

(12 citation statements)

References 26 publications

(22 reference statements)

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“…De Bruijn graph is applied for the assembly of DNA sequences produced by NGS. Najam et al [ 44 ] proposed a pattern matching algorithm using multiple bloom filters. The method assures finding all positions of the pattern in the text in the compressed text itself.…”

Section: Related Workmentioning

confidence: 99%

Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things

Rexie

Raimond

Murugaaboopathy

et al. 2022

Computational Intelligence and Neuroscience

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An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various applications. A few examples of such problems are alignment and assembly of short reads from next generation sequencing (NGS), comparing DNA sequences, and determining the frequency of a pattern in a sequence. The approximate matching of DNA sequences is also well suited for many applications equivalent to the exact matching of the sequence since the DNA sequences are often subject to mutation. Consequently, recognizing pattern similarity becomes necessary. Furthermore, it can also be used in virtually every application that calls for pattern matching, for example, spell-checking, spam filtering, and search engines. According to the traditional approach, finding a similar pattern in the case where the sequence length is ls and the pattern length is lp occurs in O (ls ∗ lp). This heavy processing is caused by comparing every character of the sequence repeatedly with the pattern. The research intended to reduce the time complexity of the pattern matching by introducing an approach named “optimized pattern similarity identification” (OPSI). This methodology constructs a table, entitled “shift beyond for avoiding redundant comparison” (SBARC), to bypass the characters in the texts that are already compared with the pattern. The table pertains to the information about the character distance to be skipped in the matching. OPSI discovers at most spots of similar patterns occur in the sequence (by ignoring è mismatches). The experiment resulted in the time complexity identified as O (ls. è). In comparison to the size of the pattern, the allowed number of mismatches will be much smaller. Aspects such as scalability, generalizability, and performance of the OPSI algorithm are discussed. In comparison with the hamming distance-based approximate pattern matching algorithm, the proposed algorithm is found to be 69% more efficient.

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Section: Related Workmentioning

confidence: 99%

Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things

Rexie

Raimond

Murugaaboopathy

et al. 2022

Computational Intelligence and Neuroscience

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“…Though various techniques are available, significantly higher compression ratio is not attained. The DNA sequences can be efficiently compressed only by exploiting the special properties of DNA [14]. The absence of efficient compression technique especially for DNA sequences motivated us to perform this work.…”

Section: Fig 1 Dna Sequence In Text Formatmentioning

confidence: 99%

Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

Alosta¹,

Souri²

2021

FPA

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In recent years, a massive amount of genomic DNA sequences are being created which leads to the development of new storing and archiving methods. There is a major challenge to process, store or transmit the huge volume of DNA sequences data. To lessen the number of bits needed to store and transmit data, data compression (DC) techniques are proposed. Recently, DC becomes more popular, and large number of techniques is proposed with applications in several domains. In this paper, a lossless compression technique named Arithmetic coding is employed to compress DNA sequences. In order to validate the performance of the proposed model, the artificial genome dataset is used and the results are investigated interms of different evaluation parameters. Experiments were performed on artificial datasets and the compression performance of Arithmetic coding is compared to Huffman coding, LZW coding, and LZMA techniques. From simulation results, it is clear that the Arithmetic coding achieves significantly better compression with a compression ratio of 0.261 at the bit rate of 2.16 bpc.

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“…It can be inferred the biological function the sequence may have by DNA sequence alignment. The mechanism of DNA sequence alignment allows the target sequence to be compared with other sequences to find regions of local similarity [1], this process can be modelled as pattern matching problem, in which a pattern is a sequence of DNA characters (A, C, G, T) that is to be searched in a DNA sequence or chromosome [3]. Optimal multi pattern matching for DNA sequence is a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

“…DNA sequencing is important in practical areas such as life and agricultural sciences. But nowadays, the DNA sequence data with massive size is a significant computational challenge [ 3 ]. Therefore, efficient DNA sequences processing is a major problem to cope with.…”

Section: Introductionmentioning

confidence: 99%

A novel optimal multi-pattern matching method with wildcards for DNA sequence

Wang

Saif

Liu

et al. 2021

THC

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BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

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Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters

Cited by 19 publications

References 26 publications

Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things

Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things

Design of Effective Lossless Data Compression Technique for Multiple Genomic DNA Sequences

A novel optimal multi-pattern matching method with wildcards for DNA sequence

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