Computing distribution of scale independent motifs in biological sequences.
Computing distribution of scale independent motifs in biological sequences.
复制标题
DOI：
10.1186/1748-7188-1-18
复制
发表时间：
2006-10-18
影响因子：
1
通讯作者：
Vinga, Susana
中科院分区：
文献类型：
--
作者：
Almeida, Jonas S;Vinga, Susana
使用混沌博弈表示（CGR）或其推广，通用序列图（USM），来描述生物序列的分布已被发现是令人反感的，因为该坐标系的分形结构。因此，在多个尺度上的符号图案的分布的调查是阻碍了距离和序列相异性之间的不精确的关联。这个问题的解决方案可以释放使用迭代映射作为相态表示的序列，其统计特性可以方便地调查。在这项研究中，一个家庭的核密度函数的描述，容纳符号序列的迭代函数表示的分形性质，因此，使任意长度的序列图案的精确调查，在该尺度独立的表示。此外，建议的核密度包括马尔可夫继承和目前使用的无约束序列相异性度量作为特殊解决方案。因此，所描述的分形核实际上是一种概括，它为一套不同的序列分析技术提供了一个共同的框架。
The use of Chaos Game Representation (CGR) or its generalization, Universal Sequence Maps (USM), to describe the distribution of biological sequences has been found objectionable because of the fractal structure of that coordinate system. Consequently, the investigation of distribution of symbolic motifs at multiple scales is hampered by an inexact association between distance and sequence dissimilarity. A solution to this problem could unleash the use of iterative maps as phase-state representation of sequences where its statistical properties can be conveniently investigated. In this study a family of kernel density functions is described that accommodates the fractal nature of iterative function representations of symbolic sequences and, consequently, enables the exact investigation of sequence motifs of arbitrary lengths in that scale-independent representation. Furthermore, the proposed kernel density includes both Markovian succession and currently used alignment-free sequence dissimilarity metrics as special solutions. Therefore, the fractal kernel described is in fact a generalization that provides a common framework for a diverse suite of sequence analysis techniques.