Opposite nucleotide usage biases in different parts of the Corynebacterium diphtheriae spaC gene

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2015-06-01 DOI:10.1504/IJBRA.2015.070140

V. V. Khrustalev, E. V. Barkovsky, V. Kolodkina, T. Khrustaleva

引用次数: 4

Abstract

In this work we described a bacterial open reading frame with two different directions of nucleotide usage biases in its two parts. The level of GC-content in third codon positions (3GC) is equal to 40.17 ± 0.22% during the most of the length of Corynebacterium diphtheriae spaC gene. However, in the 3'-end of the same gene (from codon #1600 to codon #1873) 3GC level is equal to 64.61 ± 0.91%. Using original methodology ('VVTAK Sliding window' and 'VVTAK VarInvar') we approved that there is an ongoing mutational AT-pressure during the most of the length of spaC gene (up to codon #1599), and there is an ongoing mutational G-pressure in the 3′-end of spaC. Intragenic promoters predicted by three different methods may be the cause of the differences in preferable types of nucleotide mutations in spaC parts because of their autonomous transcription.

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相反的核苷酸使用偏差在不同部分的白喉棒状杆菌空间基因

在这项工作中，我们描述了一个细菌开放阅读框与两个不同方向的核苷酸使用偏差在其两个部分。在白喉棒状杆菌空间c基因的大部分长度上，第三密码子位置(3GC)的gc含量为40.17±0.22%。而在同一基因的3′端(从密码子#1600到密码子#1873)，3GC水平为64.61±0.91%。使用原始的方法(“VVTAK滑动窗口”和“VVTAK VarInvar”)，我们证实在spaC基因的大部分长度(直到密码子#1599)存在持续的突变at压力，并且在spaC的3 '端存在持续的突变g压力。三种不同方法预测的基因内启动子可能是由于它们的自主转录而导致空间ac部分核苷酸突变类型差异的原因。

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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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.