Protein map: An alignment-free sequence comparison method based on various properties of amino acids

Yu, Chenglong; Cheng, Shiu Yuen; He, Rong; Yau, Shing‐Tung

doi:10.1016/j.gene.2011.07.002

Cited by 45 publications

(31 citation statements)

References 37 publications

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“…The dataset is a sample dataset for protein classification analysis [1,9,31,32]. The dataset is made up of 28 proteins encoded by the mitochondrial genome of 28 different mammal species [1,9,31,32].…”

Section: Resultsmentioning

confidence: 99%

“…Among the various methods, many compute the biological distances and classify proteins using polygenetic-trees, since sequence and structural similarity is considered to be closely related to protein homology [2]. Some representatives of these methods are the natural vector method [10, 13, 14], protein map [8,9], K-string dictionary [11], and Yau-Hausdorff distance [6]. These methods are unsupervised that do not require the usage of labeled training data.…”

Section: Introductionmentioning

confidence: 99%

“…Early protein classification methods concern only the bivariate relationships of proteins [2–4, 6, 9–14]. However, these bivariate relationships do not fully reflect the true genetic relationship of proteins [19].…”

Section: Introductionmentioning

confidence: 99%

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An information-based network approach for protein classification

2017

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Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An information-based network approach for protein classification

2017

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“…The assignments of Randić's type and Wen's type (including the variations with some modifications) are utilized in Refs. [47] and [46,48], respectively.…”

Section: Graphical Representation Of Proteinsmentioning

confidence: 99%

Graphical Representation of Biological Sequences

Mizuta¹

2018

Bioinformatics in the Era of Post Genomics and Big Data

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Sequence comparison is one of the most fundamental tasks in bioinformatics. For biological sequence comparison, alignment is the most profitable method when the sequence lengths are not so large. However, as the time complexity of the alignment is the square order of the sequence length, the alignment requires a large amount of computational time for comparison of sequences of large size. Therefore, so-called alignment-free sequence comparison methods are needed for comparison between such as whole genome sequences in practical time. In this chapter, we reviewed the graphical representation of biological sequences, which is one of the major alignment-free sequence comparison methods. The notable effects of weighting during the course of the graphical representation introduced first by the author and co-workers were also mentioned.

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“…BLAST: (Basic Local Alignment Search Tool) [4] is an example of a larger scale. Most of these algorithms uses SmithWaterman algorithm for performing sequence alignment [6][7][8][9][10][21][22][23][24][25]. This algorithm which is also used in crimes' forensic investigation does not use full DNA to DNA sequence comparison.…”

Section: Introductionmentioning

confidence: 99%

Locating All Common Subsequences in Two DNA Sequences

Khalil¹

2016

IJITCS

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Abstract-Biological sequence comparison is one of the most important and basic problems in computational biology. Due to its high demands for computational power and memory, it is a very challenging task. The well-known algorithm proposed by Smith-Waterman obtains the best local alignments at the expense of very high computing power and huge memory requirements. This paper introduces a new efficient algorithm to locate the longest common subsequences (LCS) in two different DNA sequences. It is based on the convolution between the two DNA sequences: The major sequence is represented in the linked-list X while the minor one is represented in circular linked-list Y. An array of linked lists is established where each linked list is corresponding to an element of the linked-list X and a new node is added to it for each match between the two sequences. If two or more matches in different locations in string Y share the same location in string X, the corresponding nodes will construct a unique linked-list. Accordingly, by the end of processing, we obtain a group of linked-lists containing nodes that reflect all possible matches between the two sequences X and Y. The proposed algorithm has been implemented and tested using C# language. The benchmark test shows very good speedups and indicated that impressive improvements has been achieved.

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Protein map: An alignment-free sequence comparison method based on various properties of amino acids

Cited by 45 publications

References 37 publications

An information-based network approach for protein classification

An information-based network approach for protein classification

Graphical Representation of Biological Sequences

Locating All Common Subsequences in Two DNA Sequences

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