基于k-mer组分信息的系统发生树构建方法

随着越来越多基因组的测序完成,基于全基因组的非比对的系统发生分析已成为研究热点。不同的生物物种或个体基因组之间的核酸组分不完全相同。遗传语言-DNA序列的信息很大程度上反映在其k—mer频数中。基于基因组序列k-mer频数的系统发生树则从新的角度为我们提供物种之间的亲缘关系。本文定义基于k-mer,频数的信息参数,并用它表征基因组序列,计算不同基因组之间信息参数的距离,用邻接法对84个病毒构建了系统发生树,发现构建的系统发生树很大程度上与已有的系统发生树相吻合。     

k-长DNA子序列频数分布研究

在详细阐述了生成DNA序列分形图像的Hao方法后,提出一种能够直观显示k-长DNA子序列频数分布差异性的三维频数分布图生成方法。把3D频数分布图转化为1D对数频谱图,突出显示了频数分布的局部特征,提出k-长DNA子序列频数区划分准则,并详细研究了甚高频数区的n阶零间隔现象,指出n阶零间隔分布就是基因组进化过程所留痕迹的假设,并给出对数频谱图特征的生物学解释。实验发现许多DNA序列频数概率分布近似服从非中心F分布,对于分布呈多峰现象的基因组序列,可采用多个非中心F分布的叠加来拟合。在比较非中心F分布与Gamma分布后,提出一种结合二者在拟合方面具有互补优势的新分布,实验证明这种新分布能够更好地吻合实际DNA序列的频数分布。最后研究了两种特异出现频数(最高出现频数与出现频数为1的k-长子序列个数)与k值的关系,发现不同物种的这两种关系具有良好的一致性。     

AKE - the Accelerated k-mer Exploration web-tool for rapid taxonomic classification and visualization

Background

With the advent of low cost, fast sequencing technologies metagenomic analyses are made possible. The large data volumes gathered by these techniques and the unpredictable diversity captured in them are still, however, a challenge for computational biology.

Results

In this paper we address the problem of rapid taxonomic assignment with small and adaptive data models (< 5 MB) and present the accelerated k-mer explorer (AKE). Acceleration in AKE’s taxonomic assignments is achieved by a special machine learning architecture, which is well suited to model data collections that are intrinsically hierarchical. We report classification accuracy reasonably well for ranks down to order, observed on a study on real world data (Acid Mine Drainage, Cow Rumen).

Conclusion

We show that the execution time of this approach is orders of magnitude shorter than competitive approaches and that accuracy is comparable. The tool is presented to the public as a web application (url: https://ani.cebitec.uni-bielefeld.de/ake/, username: bmc, password: bmcbioinfo).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0384-0) contains supplementary material, which is available to authorized users.