A method based on the improved inter-nucleotide distances of genomes to construct vertebrates phylogeny tree

Zhou, Liqian; Li, Rui; Han, Gangwen

doi:10.1109/bmei.2014.7002877

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…One way to encode the distance between pairs of nucleotides that are a fixed length apart in the sequence is with a fixed distance of k 46 i.e., i+k, i+2k,.., i+nk can be used to represent between nucelotides and S(i), S(i+k), can be encodes as k, k1 if the same nucelotides occur at each position. Inter-nucleotide distance encoding can be useful in some applications 47 , as it can reduce the dimensionality of the data and facilitate certain types of analysis. However, it may not be appropriate for all types of gene sequence analysis, as it discards some information about the specific nucleotides in the sequence 48 .…”

Section: /14mentioning

confidence: 99%

Comparative Study of Encoded and Alignment-Based Methods for Virus Taxonomy Classification

Shaukat

Nguyen

Hsu

et al. 2023

Preprint

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The emergence of viruses and their variants has made virus taxonomy more important than ever before in controlling the spread of diseases. The creation of efficient treatments and cures that target particular virus properties can be aided by understanding virus taxonomy. Alignment-based methods are commonly used for this task, but are computationally expensive and time-consuming, especially when dealing with large datasets or when detecting new virus variants is time sensitive. An alternative approach, the encoded method, has been developed that does not require prior sequence alignment and provides faster results. However, each encoded method has its own claimed accuracy. Therefore, careful evaluation and comparison of the performance of different encoded methods are essential to identify the most accurate and reliable approach for virus taxonomy classification. This study aims to address this issue by providing a comprehensive and comparative analysis of the potential of encoded methods for virus classification and phylogenetics. We compared the vectors generated for each encoded method using distance metrics to determine their similarity to alignment-based methods. The results and their validation show that K-Mer followed by CgrDft encoded methods, perform similarly to state-of-the-art multi-sequence alignment methods. This is the first study to incorporate and compare encoded methods that will facilitate future research in making more informed decisions regarding selection of a suitable method for virus taxonomy.

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Section: /14mentioning

confidence: 99%

Comparative Study of Encoded and Alignment-Based Methods for Virus Taxonomy Classification

Shaukat

Nguyen

Hsu

et al. 2023

Preprint

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“…where f 0 .k/ is the observed relative frequency distribution of distance k, and f .k/ is the reference relative frequency distribution [40,41] . Inter-nucleotide or inter-dinucleotide distance-based methods have been applied to analyze the distribution kernel of genome patterns for a whole DNA genome [42,43] , and similar research has been conducted in recent years [44] .…”

Section: Inter-nucleotide Distance Encodingmentioning

confidence: 99%

“…Studies on constructing phylogenetic trees using inter-nucleotide distance have stimulated the development of inter-nucleotide distance-based techniques [41,45] . The algorithms have become more efficient because of the use of k-word distance, which count the distance between k-tuple, 2 k 9 [45,46] .…”

Section: Inter-nucleotide Distance Encodingmentioning

confidence: 99%

Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning

Yu,

Li,

2018

Big Data Min. Anal.

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Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical values for data representation and feature learning in various applications. Similar conversion occurs in Genomic Signal Processing (GSP), where genome sequences are transformed into numerical sequences for signal extraction and recognition. This kind of conversion is also called encoding scheme. The diverse encoding schemes can greatly affect the performance of GSP applications and machine learning models. This paper aims to collect, analyze, discuss, and summarize the existing encoding schemes of genome sequence particularly in GSP as well as other genome analysis applications to provide a comprehensive reference for the genomic data representation and feature learning in machine learning.

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“…В работах [9,10,[33][34][35][36] использовались интервалы между ближайшими одинаковыми компонентами (межнуклеотидное расстояние) в качестве основы для исследования и сравнения нуклеотидных последовательностей. Однако в них рассматривались только статистические (ранговые) распределения интервалов [37,38], а также преобразования Фурье и вейвлет-преобразования [39] нуклеотидных последовательностей, что не позволяло описать отдельную последовательность одним числом.…”

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About Similarity Measures of Components Arrangement of Naturally Ordered Data Arrays

Gumenyuk¹,

Skiba²,

Pozdnichenko³

et al. 2019

Тр. СПИИРАН

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At present, adequate mathematical tools are not used to analyze the arrangement of components in arrays of naturally ordered data of a different nature, including words or letters in texts, notes in musical compositions, symbols in sign sequences, monitoring data, numbers representing ordered measurement results, components in genetic texts. Therefore, it is difficult or impossible to measure and compare the order of messages allocated in long information chains. The main approaches for comparing symbol sequences are using probabilistic models and statistical tools, pairwise and multiple alignment, which makes it possible to determine the degree of similarity of sequences using edit distance measures. The application of pseudospectral and fractal representation of symbolic sequences is somewhat exotic. "The curse of a priori unconscious knowledge" of the obvious orderliness of the sequence should be especially noticed, as it is widespread in mathematical linguistics, bioinformatics (mathematical biology), and other similar fields of science. The noted approaches almost do not pay attention to the study and detection of the patterns of the specific arrangement of all symbols, words, and components of data sets that constitute a separate sequence. The object of study in our works is a specifically organized numerical tuple – the arrangement of components (order) in symbolic or numerical sequence. The intervals between the closest identical components of the order are used as the basis for the quantitative representation of the chain arrangement. Multiplying all the intervals or summing their logarithms allows one to get numbers that uniquely reflect the arrangement of components in a particular sequence. These numbers, allow us to obtain a whole set of normalized characteristics of the order, among which the geometric mean interval and its logarithm. Such characteristics surprisingly accurately reflect the arrangement of the components in the symbolic sequences. In this paper, we present an approach for quantitative comparing the arrangement of arrays of naturally ordered data (information chains) of an arbitrary nature. The measures of similarity/distinction and procedure of comparison of the chain order, based on the selection of a list of equal and similar by the order characteristics of the subsequences (components), are proposed. Rank distributions are used for faster selection of a list of matching components. The paper presents a toolkit for comparing the order of information chains and demonstrates some of its applications for studying the structure of nucleotide sequences.

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A method based on the improved inter-nucleotide distances of genomes to construct vertebrates phylogeny tree

Cited by 4 publications

References 22 publications

Comparative Study of Encoded and Alignment-Based Methods for Virus Taxonomy Classification

Comparative Study of Encoded and Alignment-Based Methods for Virus Taxonomy Classification

Survey on encoding schemes for genomic data representation and feature learning—from signal processing to machine learning

About Similarity Measures of Components Arrangement of Naturally Ordered Data Arrays

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