DNA Sequences Analysis Based on Classifications of Nucleotide Bases

Shi, Long; Huang, Hailin

doi:10.1007/978-3-642-27866-2_45

Cited by 15 publications

(13 citation statements)

References 18 publications

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“…The four-letter alphabets (A/G/U/C) may be mapped onto two-letter (binary) alphabets in three distinct possible ways. As it turns out, these three classifications may be understood on the basis of chemical properties 45 of nucleotide bases [8]. These are:…”

Section: Grouping Of Nucleotide Bases and Transformations Into Binarymentioning

confidence: 99%

The variations of human miRNAs and Ising like base pairing models

Banerjee

Das

Choudhury

et al. 2018

Preprint

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miRNAs are small about 22-base pair long, RNA molecules are of extreme biological importance. Like other longer RNA molecules, messages in miRNAs are encoded by the permutations of only four nucleotide bases represented by A, U, C and G. However, just like words in any language, not all combination of these alphabets make a meaningful word. In fact, we find that the distributions of nucleotides bases in human miRNAs show significant deviation from randomness. First, a miRNA sequence containing four bases are mapped into a binary string with three kinds of classifications according to their chemical properties. Then, we propose a simple nearest neighbor model (Ising model) to understand the statistical variations in human miRNAs.

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Section: Grouping Of Nucleotide Bases and Transformations Into Binarymentioning

confidence: 99%

The variations of human miRNAs and Ising like base pairing models

Banerjee

Das

Choudhury

et al. 2018

Preprint

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“…For miRNAs of chimpanzee, there are 117 and 116 distance patterns of purine [1 2 3] and [1 2 3 4]. There is no miRNAs of gorilla and chimpanzee having purine distance pattern [1]. In all the three cases, it is noted that there are several clusters having only one member which means that the mirRNA of those clusters has unique purine distance pattern.…”

Section: Classification Based On Distance Pattern Of Purine and Pyrimmentioning

confidence: 99%

“…The monomer units for forming the nucleic acid polymers deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are nucleotides which are grouped into three different classes based on their chemical properties, i.e., purine group R={A,G} and pyrimidine group Y={C,T/U}; amino group M={A,C} and keto group K={G,T/U}; strong H-bond group S={C,G} and weak H-bond group W={A,T/U} [1]. There are two kinds of nitrogen-containing bases -Purines and Pyrimidines, first isolated from hydrolysates of nucleic acids, were identified using classical methods of organic chemistry.…”

Section: Introductionmentioning

confidence: 99%

Distribution of Purines and Pyrimidines over miRNAs of Human, Gorilla and Chimpanzee

Das

Choudhury

Chaudhuri³

et al. 2017

Preprint

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Meaningful words in English need vowels to break up the sounds that consonants make. The Nature has encoded her messages in RNA molecules using only four alphabets A, U, C and G in which the nine member double-ring bases (adenine (A) and Guanine (G)) are purines, while the six member single-ring bases (cytosine (C) and uracil (U)) are pyrimidines. Four bases A, U, C and G of RNA sequences are divided into three kinds of classifications according to their chemical properties. One of the three classifications, the purine-pyrimidine class is important. In understanding the distribution (organization) of purines and pyrimidines over some of the non-coding regions of RNA, all miRNAs from three species of Family Hominidae (namely human, gorilla and chimpanzee) are considered. The distribution of purines and pyrimidines over miRNA shows deviation from randomness. Based on the quantitative metrics (fractal dimension, Hurst exponent, Hamming distance, distance pattern of purine-pyrimidine, purine-pyrimidine frequency distribution and Shannon entropy) five different clusters have been made. It is identified that there exists only one miRNA in human hsa-miR-6124 which is purely made of purine bases only.

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“…DMK (Wei and Jiang, 2010) brings in the distribution information of DNA sequences based on the position of each word. By analyzing the classifications of nucleotide bases, Shi and Huang (2012) transformed a DNA sequence to three DNA sequences, and counted word frequencies in them to create the feature vectors. Bauer et al (2008) used the average mutual information to represent the DNA sequence information, called the AMI model.…”

Section: Related Workmentioning

confidence: 99%

“…However, most traditional alignment-free models, such as k-tuple (Vinga and Almeida, 2003), DMK (Wei and Jiang, 2010), TSM (Shi and Huang, 2012), AMI (Bauer et al, 2008), and CV (Qi et al, 2004) models build their feature vectors only in the time domain, i.e., they use direct word sequences.…”

Section: Introductionmentioning

confidence: 99%

A wavelet-based feature vector model for DNA clustering

Jp¹,

Ry²

2015

Genet. Mol. Res.

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ABSTRACT. DNA data are important in the bioinformatic domain. To extract useful information from the enormous collection of DNA sequences, DNA clustering is often adopted to efficiently deal with DNA data. The alignmentfree method is a very popular way of creating feature vectors from DNA sequences, which are then used to compare DNA similarities. This paper proposes a wavelet-based feature vector (WFV) model, which is also an alignment-free method. From the perspective of signal processing, a DNA sequence is a sequence of digital signals. However, most traditional alignment-free models only extract features in the time domain. The WFV model uses discrete wavelet transform to adaptively yield feature vectors with a fixed dimension based on the features in both the time and frequency domains. The level of wavelet transform is adjusted according to the length of the DNA sequence rather than a fixed manually set value. The WFV model prefers a 32-dimension feature vector, which greatly promotes system performance. We compared the WFV model with the other five alignment-free models, i.e., k-tuple, DMK, TSM, AMI, and CV, on several large-scale DNA datasets on the DNA clustering application by means of the K-means algorithm. The experimental results showed that the WFV 19163-19172 (2015) model outperformed the other models in terms of both the clustering results and the running time.

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DNA Sequences Analysis Based on Classifications of Nucleotide Bases

Cited by 15 publications

References 18 publications

The variations of human miRNAs and Ising like base pairing models

The variations of human miRNAs and Ising like base pairing models

Distribution of Purines and Pyrimidines over miRNAs of Human, Gorilla and Chimpanzee

A wavelet-based feature vector model for DNA clustering

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