Reference-free compression of next-generation sequencing data in FASTQ format

被引：0
|
作者：
Tan, Li [1 ]
Sun, Jifeng [2 ]
机构：
[1] Guangzhou Maritime Inst, Sch Informat & Commun Engn, Guangzhou, Guangdong, Peoples R China
[2] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou, Guangdong, Peoples R China
来源：
2017 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB) | 2017年
关键词：
NGS; DEMT model; DSRC; Lossless compression; LOSS-LESS COMPRESSION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present a new reference-free and lossless approach to compress next-generation sequencing (NGS) data in FASTQ format, splitting the input FASTQ data into three parts of metadata, short reads and quality scores, and eliminating their redundancy independently according to their own characteristics. Experiments were conducted on five real-world NGS data. The results show that the proposed algorithm has better compression gain as compared to the previous state of the art compression algorithms.
引用
收藏
页码：10 / 13
页数：4
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