Name
Title | ||
|---|---|---|
An efficient alignment algorithm for searching simple pseudoknots over long genomic sequence. |
Abstract | ||
|---|---|---|
Structural alignment has been shown to be an effective computational method to identify structural noncoding RNA(ncRNA) candidates as ncRNAs are known to be conserved in secondary structures. However, the complexity of the structural alignment algorithms becomes higher when the structure has pseudoknots. Even for the simplest type of pseudoknots (simple pseudoknots), the fastest algorithm runs in O(mn3) time, where m, n are the length of the query ncRNA (with known structure) and the length of the target sequence (with unknown structure), respectively. In practice, we are usually given a long DNA sequence and we try to locate regions in the sequence for possible candidates of a particular ncRNA. Thus, we need to run the structural alignment algorithm on every possible region in the long sequence. For example, finding candidates for a known ncRNA of length 100 on a sequence of length 50,000, it takes more than one day. In this paper, we provide an efficient algorithm to solve the problem for simple pseudoknots and it is shown to be 10 times faster. The speedup stems from an effective pruning strategy consisting of the computation of a lower bound score for the optimal alignment and an estimation of the maximum score that a candidate can achieve to decide whether to prune the current candidate or not. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TCBB.2012.104 | IEEE/ACM Trans. Comput. Biology Bioinform. |
Keywords | Field | DocType |
long sequence,structural alignment,simple pseudoknots,query ncrna,efficient alignment algorithm,particular ncrna,long genomic sequence,searching simple pseudoknots,long dna sequence,target sequence,structural alignment algorithm,known ncrna,optimal alignment,bioinformatics,pseudoknot,algorithm design and analysis,computational biology,genomics,rna,genetics,molecular biophysics | Structural alignment,Pseudoknot,Alignment-free sequence analysis,Algorithm design,Computer science,Upper and lower bounds,Algorithm,Genomics,Bioinformatics,Computation,Speedup | Journal |
Volume | Issue | ISSN |
9 | 6 | 1557-9964 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Ma | 1 | 0 | 0.34 |
| Thomas K. F. Wong | 2 | 89 | 9.80 |
| Tak-Wah Lam | 3 | 1860 | 164.96 |
| Wing-Kai Hon | 4 | 856 | 78.67 |
| K Sadakane | 5 | 0 | 0.34 |
| S. M. Yiu | 6 | 528 | 44.78 |