{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/31074"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/31074","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Searching Biological Sequence Databases Using Distributed Adaptive Computing","abstract":"Genetic research projects currently can require enormous computing power to processes the vast quantities of data available. Further, DNA sequencing projects are generating data at an exponential rate greater than that of the development microprocessor technology; thus, new, faster methods and techniques of processing this data are needed. One common type of processing involves searching a sequence database for the most similar sequences. Here we present a distributed database search system that utilizes adaptive computing technologies. The search is performed using the Smith-Waterman algorithm, a common sequence comparison algorithm. To reduce the total search time, an initial search is performed using a version of the algorithm, implemented in adaptive computing hardware, which is designed to efficiently perform the initial search. A final search is performed using a complete version of the algorithm. This two-stage search, employing adaptive and distributed hardware, achieves a performance increase of several orders of magnitude over similar processor based systems.","abstract_html":"Genetic research projects currently can require enormous computing power to processes the vast quantities of data available. Further, DNA sequencing projects are generating data at an exponential rate greater than that of the development microprocessor technology; thus, new, faster methods and techniques of processing this data are needed. One common type of processing involves searching a sequence database for the most similar sequences. Here we present a distributed database search system that utilizes adaptive computing technologies. The search is performed using the Smith-Waterman algorithm, a common sequence comparison algorithm. To reduce the total search time, an initial search is performed using a version of the algorithm, implemented in adaptive computing hardware, which is designed to efficiently perform the initial search. A final search is performed using a complete version of the algorithm. This two-stage search, employing adaptive and distributed hardware, achieves a performance increase of several orders of magnitude over similar processor based systems.","abstract_has_math":false,"creators":["Pappas, Nicholas Peter"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Athanas, Peter M."],"committee_members":["Dickerman, Allan W.","Hsiao, Michael S.","Jones, Mark T."],"year":2003,"date_issued":"2003-01-24","date_published":"2003-01-24","updated_at":"2026-07-22T22:18:58Z","subjects":["bioinformatics","Smith-Waterman algorithm","adaptive computing","configurable computing","sequence comparison","sequence alignment","Field programmable gate arrays"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-01302003-145726"],"render_values":[{"text":"etd-01302003-145726","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/31074","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Athanas, Peter M."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Dickerman, Allan W.","Hsiao, Michael S.","Jones, Mark T."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Pappas, Nicholas Peter"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:31:11Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:31:11Z","2004-02-06"]},{"key":"dc:date.issued","label":"Date","values":["2003-01-24"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["bioinformatics","Smith-Waterman algorithm","adaptive computing","configurable computing","sequence comparison","sequence alignment","Field programmable gate arrays"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-01302003-145726"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/31074"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Genetic research projects currently can require enormous computing power to processes the vast quantities of data available. Further, DNA sequencing projects are generating data at an exponential rate greater than that of the development microprocessor technology; thus, new, faster methods and techniques of processing this data are needed. One common type of processing involves searching a sequence database for the most similar sequences. Here we present a distributed database search system that utilizes adaptive computing technologies. The search is performed using the Smith-Waterman algorithm, a common sequence comparison algorithm. To reduce the total search time, an initial search is performed using a version of the algorithm, implemented in adaptive computing hardware, which is designed to efficiently perform the initial search. A final search is performed using a complete version of the algorithm. This two-stage search, employing adaptive and distributed hardware, achieves a performance increase of several orders of magnitude over similar processor based systems."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Searching Biological Sequence Databases Using Distributed Adaptive Computing"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Athanas, Peter M."],"dc:contributor.committeemember":["Dickerman, Allan W.","Hsiao, Michael S.","Jones, Mark T."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Pappas, Nicholas Peter"],"dc:date.accessioned":["2014-03-14T20:31:11Z"],"dc:date.available":["2014-03-14T20:31:11Z","2004-02-06"],"dc:date.issued":["2003-01-24"],"dc:description.abstract":["Genetic research projects currently can require enormous computing power to processes the vast quantities of data available. 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