用于微生物基因组结构分析的 dna 文本复杂性估计的计算工具

A. Mitina, N. Orlova, A. Dergilev, Yuriy Orlov
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引用次数: 0

摘要

生物信息学的基本任务之一是搜索重复序列,即 DNA 序列和微生物完整基因组中的统计异质性片段。在获得完整基因组序列之前,分析大分子序列(DNA、RNA 和蛋白质)复杂性的理论方法已经确立。随着大规模并行测序技术的普及和可访问数据的指数级增长,这些方法又重新兴起。本文探讨了当代计算机方法和现有程序,这些方法和程序旨在评估 DNA 文本的复杂性,以及构建用于分析微生物基因组结构的特性曲线。文章全面概述了用于检测和可视化基因文本中重复序列的现有在线程序。此外,文章还介绍了一种基于计算机的新方法,该方法利用 Lempel-Ziv 评估文本的语言复杂性并进行压缩。这种方法旨在识别微生物基因组中的结构特征和异常。文章还举例说明了通过分析文本复杂性生成的剖面图。文章讨论了这些复杂性估计值在基因组序列分析中的应用,如 SARS-CoV-2 冠状病毒和腮腺炎正粘病毒的基因组序列。这项研究成功确定了基因文本中复杂度较低的特定区域。
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COMPUTATIONAL TOOLS FOR THE DNA TEXT COMPLEXITY ESTIMATES FOR MICROBIAL GENOMES STRUCTURE ANALYSIS
One of the fundamental tasks in bioinformatics involves searching for repeats, which are statistically heterogeneous segments within DNA sequences and complete genomes of microorganisms. Theoretical approaches to analyzing the complexity of macromolecule sequences (DNA, RNA, and proteins) were established prior to the availability of complete genomic sequences. These approaches have experienced a resurgence due to the proliferation of mass parallel sequencing technologies and the exponential growth of accessible data. This article explores contemporary computer methods and existing programs designed to assess DNA text complexity as well as construct profiles of properties for analysing the genomic structures of microorganisms. The article offers a comprehensive overview of available online programs designed for detecting and visualising repeats within genetic text. Furthermore, the paper introduces a novel computer-based implementation of a method to evaluate the linguistic complexity of text and its compression using Lempel-Ziv. This approach aims to identify structural features and anomalies within the genomes of microorganisms. The article also provides examples of profiles generated through the analysis of text complexity. Application of these complexity estimates in the analysis of genome sequences, such as those of the SARS-CoV-2 coronavirus and the Mumps Orthorubulavirus, is discussed. Specific areas of low complexity within the genetic text have been successfully identified in this research.
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