Abstract
We have developed a multiple genome alignment algorithm by using a sequence clustering algorithm to combine local pairwise genome sequence matches produced by pairwise genome alignments, e.g, BLASTZ. Sequence clustering algorithms often generate clusters of sequences such that there exists a common shared region among all sequences in each cluster. To use a sequence clustering algorithm for genome alignment, it is necessary to handle numerous local alignments between a pair of genomes. We propose a multiple genome alignment method that converts the multiple genome alignment problem to the sequence clustering problem. This method does not need to make a guide tree to determine the order of multiple alignment, and it accurately detects multiple homologous regions. As a result, our multiple genome alignment algorithm performs competitively over existing algorithms. This is shown using an experiment which compares the performance of TBA, MultiPipMaker (MPM) and our algorithm in aligning 12 groups of 56 microbial genomes and by evaluating the number of common COGs detected.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 30-41 |
| Number of pages | 12 |
| Journal | Lecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science) |
| Volume | 3388 |
| State | Published - Oct 17 2005 |
| Externally published | Yes |
| Event | RECOMB 2004 International Workshop, RRCG 2004 - Comparative Genomics - Bertinoro, Italy Duration: Oct 16 2004 → Oct 19 2004 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Multiple genome alignment by clustering pairwise matches. / Choi, Jeong-Hyeon; Choi, Kwangmin; Cho, Hwan Gue; Kim, Sun.
In: Lecture Notes in Bioinformatics (Subseries of Lecture Notes in Computer Science), Vol. 3388, 17.10.2005, p. 30-41.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Multiple genome alignment by clustering pairwise matches
AU - Choi, Jeong-Hyeon
AU - Choi, Kwangmin
AU - Cho, Hwan Gue
AU - Kim, Sun
PY - 2005/10/17
Y1 - 2005/10/17
N2 - We have developed a multiple genome alignment algorithm by using a sequence clustering algorithm to combine local pairwise genome sequence matches produced by pairwise genome alignments, e.g, BLASTZ. Sequence clustering algorithms often generate clusters of sequences such that there exists a common shared region among all sequences in each cluster. To use a sequence clustering algorithm for genome alignment, it is necessary to handle numerous local alignments between a pair of genomes. We propose a multiple genome alignment method that converts the multiple genome alignment problem to the sequence clustering problem. This method does not need to make a guide tree to determine the order of multiple alignment, and it accurately detects multiple homologous regions. As a result, our multiple genome alignment algorithm performs competitively over existing algorithms. This is shown using an experiment which compares the performance of TBA, MultiPipMaker (MPM) and our algorithm in aligning 12 groups of 56 microbial genomes and by evaluating the number of common COGs detected.
AB - We have developed a multiple genome alignment algorithm by using a sequence clustering algorithm to combine local pairwise genome sequence matches produced by pairwise genome alignments, e.g, BLASTZ. Sequence clustering algorithms often generate clusters of sequences such that there exists a common shared region among all sequences in each cluster. To use a sequence clustering algorithm for genome alignment, it is necessary to handle numerous local alignments between a pair of genomes. We propose a multiple genome alignment method that converts the multiple genome alignment problem to the sequence clustering problem. This method does not need to make a guide tree to determine the order of multiple alignment, and it accurately detects multiple homologous regions. As a result, our multiple genome alignment algorithm performs competitively over existing algorithms. This is shown using an experiment which compares the performance of TBA, MultiPipMaker (MPM) and our algorithm in aligning 12 groups of 56 microbial genomes and by evaluating the number of common COGs detected.
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M3 - Conference article
VL - 3388
SP - 30
EP - 41
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -