A Fast Divide-and-Conquer Algorithm for Indexing Human Genome Sequences

Abstract

SUMMARYSince the release of human genome sequences, one of the most important research issues is about indexing the genome sequences, and the suffix tree is most widely adopted for that purpose. The traditional suffix tree construction algorithms suffer from severe performance degradation due to the memory bottleneck problem. The recent disk-based algorithms also provide limited performance improvement due to random disk accesses. Moreover, they do not fully utilize the recent CPUs with multiple cores. In this paper, we propose a fast algorithm based on 'divideand-conquer' strategy for indexing the human genome sequences. Our algorithm nearly eliminates random disk accesses by accessing the disk in the unit of contiguous chunks. In addition, our algorithm fully utilizes the multi-core CPUs by dividing the genome sequences into multiple partitions and then assigning each partition to a different core for parallel processing. Experimental results show that our algorithm outperforms the previous fastest DIGEST algorithm by up to 10.5 times.

A Storage-Efficient Suffix Tree Construction Algorithm for Human Genome Sequences

“…Since it is impossible to reduce the number of nodes in the suffix tree for a given sequence, our approach reduces the storage size of the suffix tree nodes. As a result of our experiments, compared with the one by Loh et al [7], our algorithm constructed a suffix tree of approximately 60% of the size within almost the same time period. Figure 1 shows the suffix tree for a genome sequence X = ATAGCTAGATCG$ [7].…”

“…As a result of our experiments, compared with the one by Loh et al [7], our algorithm constructed a suffix tree of approximately 60% of the size within almost the same time period. Figure 1 shows the suffix tree for a genome sequence X = ATAGCTAGATCG$ [7]. The symbol '$' is attached at the end of X to prevent any suffix from being the prefix of any other suffixes and hence to enable the efficient processing of suffixes.…”

“…Although it supports very fast alignment, it has a couple of shortcomings, such as a very long construction time and a very large volume size. Loh et al [7] proposed a suffix tree construction algorithm with dramatically improved performance; however, the size still remains as a challenging problem. We propose an algorithm by extending the one by Loh et al to reduce the suffix tree size.…”

“…However, it has a couple of shortcomings; it requires a very long time to construct and also occupies a large volume size [6], [8]. Loh et al [7] proposed a suffix tree construction algorithm to cope with the first shortcoming. The algorithm dramatically reduced the construction time by making the most of recent multi-core CPUs and minimizing disk access costs.…”

“…In this paper, we propose a suffix tree construction algorithm, which is an extension of the one by Loh et al [7], for coping with the second shortcoming of the suffix tree. Since it is impossible to reduce the number of nodes in the suffix tree for a given sequence, our approach reduces the storage size of the suffix tree nodes.…”