Memory Efficient de novo Assembly Algorithm using Disk Streaming of K-mers

Sequencing the whole genome of various species has many applications, not only in understanding biological systems, but also in medicine, pharmacy, and agriculture. In recent years, the emergence of high-throughput next generation sequencing technologies has dramatically reduced the time and costs for whole genome sequencing. These new technologies provide ultrahigh throughput with a lower per-unit data cost. However, the data are generated from very short fragments of DNA. Thus, it is very important to develop algorithms for merging these fragments. One method of merging these fragments without using a reference dataset is called de novo assembly. Many algorithms for de novo assembly have been proposed in recent years. Velvet and SOAPdenovo2 are well-known assembly algorithms, which have good performance in terms of memory and time consumption. However, memory consumption increases dramatically when the size of input fragments is larger. Therefore, it is necessary to develop an alternative algorithm with low memory usage. In this paper, we propose an algorithm for de novo assembly with lower memory. In the proposed method, memory-efficient DSK (disk streaming of k-mers) to count k-mers is adopted. Moreover, the amount of memory usage for constructing de bruijn graph is reduced by not keeping edge information in the graph. In our experiment using human chromosome 14, the average maximum memory consumption of the proposed method was approximately 7.5–8.8% of that of the popular assemblers.