DNA序列数据压缩技术分析
Analysis of Compression Techniques for DNA Sequence Data
摘要：
生物学数据主要包括脱氧核糖核酸（DNA）和蛋白质序列。这些是存在于人类所有细胞中的生物分子。由于DNA的自我复制特性，它是存在于所有呼吸生物中的遗传物质的关键组成部分。这种生物分子（DNA）使遗传物质保持不变，这是所有人格化生命运作和扩展所必需的。为了保存单个人的DNA数据，我们需要100个CD-ROM，而且这个大小还在不断增加，越来越多的序列被添加到公共数据库中。序列数据的大量增加对从这些数据中精确提取信息提出了挑战。由于许多数据分析和可视化工具不支持处理如此大量的数据。为了减小DNA和蛋白质序列的大小，许多科学家提出了各种序列压缩算法，如compress或gzip、上下文树加权（CTW）、Lampel Ziv Welch（LZW）、算术编码、游程编码和替换方法等，这些技术都为最小化生物数据集的体积做出了充分的贡献。另一方面，传统的压缩技术也不太适合这些类型的顺序数据的压缩。在本文中，我们探讨了各种类型的技术，用于压缩大量的DNA序列数据。本文通过对这些技术的分析，发现有效的技术不仅可以减少序列的长度，而且可以避免任何信息的丢失。对现有研究的回顾也表明，DNA序列的压缩对于理解DNA数据的关键特征以及提高存储效率和数据传输具有重要意义。此外，蛋白质序列的压缩也是研究界面临的一个挑战。评价这些压缩算法的主要指标包括压缩比、运行时间复杂度等。
英文摘要：
Biological data mainly comprises of Deoxyribonucleic acid (DNA) and protein sequences. These are the biomolecules which are present in all cells of human beings. Due to the self-replicating property of DNA, it is a key constitute of genetic material that exist in all breathingcreatures. This biomolecule (DNA) comprehends the genetic material obligatory for the operational and expansion of all personified lives. To save DNA data of single person we require 10CD-ROMs.Moreover, this size is increasing constantly, and more and more sequences are adding in the public databases. This abundant increase in the sequence data arise challenges in the precise information extraction from this data. Since many data analyzing and visualization tools do not support processing of this huge amount of data. To reduce the size of DNA and protein sequence, many scientists introduced various types of sequence compression algorithms such as compress or gzip, Context Tree Weighting (CTW), Lampel Ziv Welch (LZW), arithmetic coding, run-length encoding and substitution method etc. These techniques have sufficiently contributed to minimizing the volume of the biological datasets. On the other hand, traditional compression techniques are also not much suitable for the compression of these types of sequential data. In this paper, we have explored diverse types of techniques for compression of large amounts of DNA Sequence Data. In this paper, the analysis of techniques reveals that efficient techniques not only reduce the size of the sequence but also avoid any information loss. The review of existing studies also shows that compression of a DNA sequence is significant for understanding the critical characteristics of DNA data in addition to improving storage efficiency and data transmission. In addition, the compression of the protein sequence is a challenge for the research community. The major parameters for evaluation of these compression algorithms include compression ratio, running time complexity etc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
