分子进化模式分析计算机系统(SAMEM):分析系统发育树深层内部分支的分子进化模式。

Q2 Medicine
Konstantin V Gunbin, Valentin V Suslov, Mikhail A Genaev, Dmitry A Afonnikov
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引用次数: 9

摘要

SAMEM (System for Analysis of Molecular Evolution Modes)是一个基于网络的管道系统,用于推断基因和蛋白质的分子进化模式(http://pixie.bionet.nsc.ru/samem/)。管道1执行蛋白质编码基因进化分析;管道2执行蛋白质进化分析;流水线3准备基因和/或蛋白质的数据集,执行其初步分析,并建立BLOSUM矩阵;管道4检查这些基因是否真的是蛋白质编码。管道1有一个全新的功能,它允许用户使用几种不同的方法获得K(R)/K(C)估计。管道2的一个重要特征是分析系统发育树分支上氨基酸取代率的原始方法。该方法基于马尔可夫模型和非参数排列检验,通过比较预期和观察到的氨基酸取代频率,推断出深层内分支的分子进化模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computer System for Analysis of Molecular Evolution Modes (SAMEM): analysis of molecular evolution modes at deep inner branches of the phylogenetic tree.

SAMEM (System for Analysis of Molecular Evolution Modes), a web-based pipeline system for inferring modes of molecular evolution in genes and proteins (http://pixie.bionet.nsc.ru/samem/), is presented. Pipeline 1 performs analyses of protein-coding gene evolution; pipeline 2 performs analyses of protein evolution; pipeline 3 prepares datasets of genes and/or proteins, performs their primary analysis, and builds BLOSUM matrices; pipeline 4 checks if these genes really are protein-coding. Pipeline 1 has an all-new feature, which allows the user to obtain K(R)/K(C) estimates using several different methods. An important feature of pipeline 2 is an original method for analyzing the rates of amino acid substitutions at the branches of a phylogenetic tree. The method is based on Markov modeling and a non-parametric permutation test, which compares expected and observed frequencies of amino acid substitutions, and infers the modes of molecular evolution at deep inner branches.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
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.
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