Database indexing for large DNA and protein sequence collections

Hunt, Ela; Atkinson, Malcolm; Irving, Robert W.

doi:10.1007/s007780200064

Cited by 51 publications

(51 citation statements)

References 51 publications

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“…Moreover, FAST almost eliminates random disk accesses, which were the major cause of performance degradation of the previous disk-based suffix tree construction algorithms [1], [3], [8], [9]. In general, even when accessing the same amount of disk volume, the performance greatly depends on disk access patterns; sequential access can improve the performance more than hundred times compared to random access.…”

Section: Related Workmentioning

confidence: 99%

“…Hunt et al [3] proposed the first algorithm to construct the suffix tree for human genome sequences. Since then, a few more algorithms with improved performance were proposed [1], [8], [9].…”

Section: Related Workmentioning

confidence: 99%

“…One of them is aligning short genome subsequences of 100 ∼ 1000 length to the human genome sequences [3]- [5]. This application is getting more attention along with the recent advances in DNA sequencing technology * * * .…”

Section: Introductionmentioning

confidence: 99%

“…The suffix tree supports very fast alignment and can also be used in many other applications such as finding frequent subsequences, common subsequences, and maximal palindromes [3], [8], [9]. However, it has a couple of shortcomings; it requires a very long time to construct and also occupies a large volume size [6], [8].…”

Section: Introductionmentioning

confidence: 99%

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

Loh

Ahn

2011

IEICE Trans. Inf. & Syst.

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SUMMARYThe suffix tree is one of most widely adopted indexes in the application of genome sequence alignment. 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. As a result of our experiments, our algorithm constructed a suffix tree of approximately 60% of the size within almost the same time period.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Loh

Ahn

2011

IEICE Trans. Inf. & Syst.

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“…For coping with the memory bottleneck problem, a few disk-based algorithms have been proposed for constructing the suffix tree [3], [5], [11], [18], [22]. Disks have much larger size than main memory at the lower cost; however, they require much longer access time up to several hundred times.…”

Section: Introductionmentioning

confidence: 99%

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

Loh

Moon

Lee

2011

IEICE Trans. Inf. & Syst.

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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.

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CSI: Clustered Segment Indexing for Efficient Approximate Searching on the Secondary Structure of Protein Sequences

Seo

Park

Won

2005

Lecture Notes in Computer Science

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Abstract. Approximate searching on the primary structure (i.e., amino acid arrangement) of protein sequences is an essential part in predicting the functions and evolutionary histories of proteins. However, because proteins distant in an evolutionary history do not conserve amino acid residue arrangements, approximate searching on the proteins' secondary structure is quite important in finding out distant homology. In this paper, we propose an indexing scheme for efficient approximate searching on the secondary structure of protein sequences. Exploiting the concept of clustering and lookahead, the proposed indexing scheme processes three types of secondary structure queries (i.e., exact match, range match, and wildcard match) very quickly. To evaluate the performance of the proposed method, we conducted extensive experiments using a set of actual protein sequences. According to the experimental results, the proposed method was proved to be 6.3 times faster in exact match, 3.3 times faster in range match, and 1.5 times faster in wildcard match compared to the existing indexing methods.

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Database indexing for large DNA and protein sequence collections

Cited by 51 publications

References 51 publications

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

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

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

CSI: Clustered Segment Indexing for Efficient Approximate Searching on the Secondary Structure of Protein Sequences

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