DNA序列表示方法

Q2 Medicine In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722073

G. Santhosh Kumar, S. Shiji

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引用次数: 1

摘要

DNA序列表示方法用于有效地表示基因结构，有助于编码序列的异同分析。文献中提出了许多不同类型的表述。它们大致可分为数值表示、图形表示、几何表示和混合表示。DNA的结构和功能分析很容易与图形和几何表示方法，因为它提供了DNA结构的视觉表示。在数值方法中，将数值赋给序列，并使用数字信号处理方法对序列进行分析。混合方法也报道在文献中分析DNA序列。本文综述了DNA序列表示方法的最新进展。我们还提出了各种方法的分类。尽可能地对这些方法进行比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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DNA sequence representation methods

DNA sequence representation methods are used to denote a gene structure effectively and help in similarities/dissimilarities analysis of coding sequences. Many different kinds of representations have been proposed in the literature. They can be broadly classified into Numerical, Graphical, Geometrical and Hybrid representation methods. DNA structure and function analysis are made easy with graphical and geometrical representation methods since it gives visual representation of a DNA structure. In numerical method, numerical values are assigned to a sequence and digital signal processing methods are used to analyze the sequence. Hybrid approaches are also reported in the literature to analyze DNA sequences. This paper reviews the latest developments in DNA Sequence representation methods. We also present a taxonomy of various methods. A comparison of these methods where ever possible is also done.

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来源期刊

In Silico Biology Computer Science-Computational Theory and Mathematics

CiteScore

2.20

自引率

0.00%

发文量

期刊介绍： The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.