An Improved Longest Common Subsequence Algorithm for Reducing Memory Complexity in Global Alignment of DNA Sequences
The comparison of biological sequences is one of the oldest problems in computational biology. Global alignment is designed to search for highly similar regions in two DNA sequences, where appear in the same order and orientation. Longest Common Subsequence (LCS) is the most typical algorithm for gl...
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| creator | Parvinnia, E. Taheri, M. ziarati, K. |
| description | The comparison of biological sequences is one of the oldest problems in computational biology. Global alignment is designed to search for highly similar regions in two DNA sequences, where appear in the same order and orientation. Longest Common Subsequence (LCS) is the most typical algorithm for global alignment that has optimal solution and independent to the shape of its input sequences. Since the space complexity of this algorithm is the multiplication of sequence lengths; we cannot use it for long sequences. In this paper, some rules are extracted to reduce amount of redundant information. Remained information is stacked to be used in backward iteration for finding the optimal path. As we examined in the proposed algorithm, the stack size in comparison with space consumed by LCS algorithm is reduced about 10 times and we could increase the length of input DNA sequences in global alignment. |
| doi_str_mv | 10.1109/BMEI.2008.212 |
| format | Conference Proceeding |
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Global alignment is designed to search for highly similar regions in two DNA sequences, where appear in the same order and orientation. Longest Common Subsequence (LCS) is the most typical algorithm for global alignment that has optimal solution and independent to the shape of its input sequences. Since the space complexity of this algorithm is the multiplication of sequence lengths; we cannot use it for long sequences. In this paper, some rules are extracted to reduce amount of redundant information. Remained information is stacked to be used in backward iteration for finding the optimal path. 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Global alignment is designed to search for highly similar regions in two DNA sequences, where appear in the same order and orientation. Longest Common Subsequence (LCS) is the most typical algorithm for global alignment that has optimal solution and independent to the shape of its input sequences. Since the space complexity of this algorithm is the multiplication of sequence lengths; we cannot use it for long sequences. In this paper, some rules are extracted to reduce amount of redundant information. Remained information is stacked to be used in backward iteration for finding the optimal path. As we examined in the proposed algorithm, the stack size in comparison with space consumed by LCS algorithm is reduced about 10 times and we could increase the length of input DNA sequences in global alignment.</description><subject>Application software</subject><subject>Biomedical engineering</subject><subject>Biomedical informatics</subject><subject>Computational biology</subject><subject>Data mining</subject><subject>DNA</subject><subject>Dynamic programming</subject><subject>global alignment</subject><subject>Heuristic algorithms</subject><subject>Longest Common Subsequence (LCS)</subject><subject>Sequences</subject><subject>Shape</subject><issn>1948-2914</issn><issn>1948-2922</issn><isbn>9780769531182</isbn><isbn>0769531180</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9jE9PwjAcQBuVREWOnrz0Cwz765-1PU5EJAFNhDsZ22-zZm1xG0a-vRiJp3d4eY-QW2BjAGbvH5bT-ZgzZsYc-Bm5AitNwi3n52RktWE6tUoAGH7x70AOyPUx0VYKUPySjLrugzEGNtUA_Ir0WaBzv2vjF5Z0EUONXU8n0fsY6Gq_7fBzj6FAmjV1bF3_7mkVW_qG5b5woaZL9LE9_Aa7Br9df6Au0FkTt3lzTFwdPIaexoo-vmR0dZp1N2RQ5U2HoxOHZP00XU-ek8XrbD7JFokDrfoEeY5QFooLw1IOpUmVkKiVsLLSyGTBuUhtWgFo4HklgWlp8EiRGxAghuTub-sQcbNrnc_bw0YqaVKhxA9Cgl4V</recordid><startdate>200805</startdate><enddate>200805</enddate><creator>Parvinnia, E.</creator><creator>Taheri, M.</creator><creator>ziarati, K.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200805</creationdate><title>An Improved Longest Common Subsequence Algorithm for Reducing Memory Complexity in Global Alignment of DNA Sequences</title><author>Parvinnia, E. ; Taheri, M. ; ziarati, K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-e2ae1dc52380621d86534e75394f7e04c223696f11712af410748ef413a81313</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Application software</topic><topic>Biomedical engineering</topic><topic>Biomedical informatics</topic><topic>Computational biology</topic><topic>Data mining</topic><topic>DNA</topic><topic>Dynamic programming</topic><topic>global alignment</topic><topic>Heuristic algorithms</topic><topic>Longest Common Subsequence (LCS)</topic><topic>Sequences</topic><topic>Shape</topic><toplevel>online_resources</toplevel><creatorcontrib>Parvinnia, E.</creatorcontrib><creatorcontrib>Taheri, M.</creatorcontrib><creatorcontrib>ziarati, K.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Parvinnia, E.</au><au>Taheri, M.</au><au>ziarati, K.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An Improved Longest Common Subsequence Algorithm for Reducing Memory Complexity in Global Alignment of DNA Sequences</atitle><btitle>2008 International Conference on BioMedical Engineering and Informatics</btitle><stitle>BMEI</stitle><date>2008-05</date><risdate>2008</risdate><volume>1</volume><spage>57</spage><epage>61</epage><pages>57-61</pages><issn>1948-2914</issn><eissn>1948-2922</eissn><isbn>9780769531182</isbn><isbn>0769531180</isbn><abstract>The comparison of biological sequences is one of the oldest problems in computational biology. Global alignment is designed to search for highly similar regions in two DNA sequences, where appear in the same order and orientation. Longest Common Subsequence (LCS) is the most typical algorithm for global alignment that has optimal solution and independent to the shape of its input sequences. Since the space complexity of this algorithm is the multiplication of sequence lengths; we cannot use it for long sequences. In this paper, some rules are extracted to reduce amount of redundant information. Remained information is stacked to be used in backward iteration for finding the optimal path. As we examined in the proposed algorithm, the stack size in comparison with space consumed by LCS algorithm is reduced about 10 times and we could increase the length of input DNA sequences in global alignment.</abstract><pub>IEEE</pub><doi>10.1109/BMEI.2008.212</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2008 International Conference on BioMedical Engineering and Informatics, 2008, Vol.1, p.57-61 |
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| language | eng |
| recordid | cdi_ieee_primary_4548635 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Application software Biomedical engineering Biomedical informatics Computational biology Data mining DNA Dynamic programming global alignment Heuristic algorithms Longest Common Subsequence (LCS) Sequences Shape |
| title | An Improved Longest Common Subsequence Algorithm for Reducing Memory Complexity in Global Alignment of DNA Sequences |
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