Algebraic approach to optimal clone selection applied in metagenomic projects

Q2 Medicine

In Silico Biology Pub Date : 2010-02-15 DOI:10.1145/1722024.1722066

M. Cantão, L. V. de Araújo, E. G. Lemos, J. E. Ferreira

引用次数: 4

Abstract

Due to the wide diversity of unknown organisms in the environment, 99% of them cannot be grown in traditional culture medium in laboratories. Therefore, metagenomics projects are proposed to study microbial communities present in the environment, from molecular techniques, especially the sequencing. Thereby, for the coming years it is expected an accumulation of sequences produced by these projects. Thus, the sequences produced by genomics and metagenomics projects present several challenges for the treatment, storing and analysis such as: the search for clones containing genes of interest. This work presents the OCI Metagenomics, which allows defines and manages dynamically the rules of clone selection in metagenomic libraries, thought an algebraic approach based on process algebra. Furthermore, a web interface was developed to allow researchers to easily create and execute their own rules to select clones in genomic sequence database. This software has been tested in metagenomic cosmid library and it was able to select clones containing genes of interest.

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元基因组项目中最优克隆选择的代数方法

由于环境中未知生物种类繁多，99%的未知生物无法在实验室的传统培养基中生长。因此，提出了宏基因组学项目来研究存在于环境中的微生物群落，从分子技术，特别是测序。因此，在未来几年，预计这些项目产生的序列将不断积累。因此，基因组学和宏基因组学项目产生的序列对治疗、存储和分析提出了一些挑战，例如:寻找包含感兴趣基因的克隆。本文提出了基于过程代数的OCI宏基因组学方法，该方法允许动态定义和管理宏基因组库中的克隆选择规则。此外，还开发了一个web界面，使研究人员可以轻松地创建和执行自己的规则，以选择基因组序列数据库中的克隆。该软件已在宏基因组库中进行了测试，并能够选择含有感兴趣基因的克隆。

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

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.