陈慧

摘  要：该文通过将生物学特征和生物学含义引入DNA序列数据的压缩处理中， 提出了基于生物信息学特征的基因组序列的Context建模熵编码技术，拟结合基因组序列特点，研究针对基因组序列的Context建模熵编码技术。在算法中DNA序列根据组成部分生物学含义的不同切分重组为4个集合：编码序列CDS集合、内含子序列集合、RNA序列集合以及剩余序列的集合。根据各集合中序列的具体生物学特征分别进行预处理， 并通过熵编码算法进行压缩。实验结果表明，该算法在基准测试序列上的压缩性能优于原有的DNA序列压缩方法，特别是对于生物信息学特征清晰的长序列，算法能够在较短的时间内获得较高的压缩率。

关键词：基因组序列  Context建模  熵编码  集合

中图分类号：G64                             文献标识码：A文章编号：1672-3791（2021）03（c）-0025-03

Application of Entropy Coding Genome Sequence Based on Context Modeling

CHEN Hui

（Dianchi College of Yunnan University， Kunming， Yunnan Province， 650228  China）

Abstract： In this paper， by introducing biological characteristics and biological meaning into the compression processing of DNA sequence data， Context modeling entropy coding technology of genome sequence based on bioinformatics features was proposed. It is intended to combine the characteristics of genome sequence to study the context modeling entropy coding technology of genome sequence. In the algorithm， DNA sequences are reorganized into four sets according to the different slices of the biological meaning of the constituent parts： the CDS set of the coding sequence， the intron sequence set， the RNA sequence set and the remaining sequence set. According to the specific biological characteristics of the sequences in each set， the sequences were preprocessed and compressed by entropy coding algorithm. The experimental results show that the compression performance of the proposed algorithm is better than that of the original DNA sequence compression method， especially for long sequences with clear bioinformatics features， the algorithm can obtain a higher compression rate in a relatively short time.

Key Words： Genome sequence; Context modeling; Entropy coding; Congregation

基因组序列要保持尽可能多的遗传特性，则注定其基因组序列中要维持最低的重复片段。重复序列片段过短导致在Context建模过程中，直接选取近邻碱基来构建条件概率分布未必能够保证碱基间的相关性得以最大限度发挥。而文献[1]中指出，基因组中碱基间存在长程相关性。这就意味着，与当前碱基相关的那些碱基未必在其附近。因此，在对基因组序列进行压缩时，不能简单地按照碱基在序列中的顺序进行建模和编码。一种对基因组序列进行重新排序，使得排序后相邻碱基间的相关性得以增强，然后再对重排序后的序列进行编码以充分发挥熵编码性能。……