{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:theses-1166"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:theses-1166","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Performance comparison of five RNA-seq alignment tools","abstract":"Aligning millions of short reads to a reference genome is a critical task in high throughput sequencing. In recent years, a large number of mapping algorithms have been developed, all of which have in common that they align a vast number of reads to genomic or transcriptomic sequences. RNA-Seq data is discrete in nature, therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. To provide guidance in the choice of alignment algorithms, five different alignment tools for RNA-Seq data are evaluated. In order to compare the accuracy and sensitivity of the Bowtie, Bowtie2, GMAP, Tophat and GNUMAP tools, their alignment accuracy for approximately 1 million simulated reads of chromosome one was evaluated using these five alignment tools. Bowtie has the highest accuracy, which is 92.42%, while GMAP has the lowest, which is 49.63%. Tophat has the highest sensitivity , which is 71.35% , while GMAP has the lowest, which is 51.69%.","abstract_html":"Aligning millions of short reads to a reference genome is a critical task in high throughput sequencing. In recent years, a large number of mapping algorithms have been developed, all of which have in common that they align a vast number of reads to genomic or transcriptomic sequences. RNA-Seq data is discrete in nature, therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. To provide guidance in the choice of alignment algorithms, five different alignment tools for RNA-Seq data are evaluated. In order to compare the accuracy and sensitivity of the Bowtie, Bowtie2, GMAP, Tophat and GNUMAP tools, their alignment accuracy for approximately 1 million simulated reads of chromosome one was evaluated using these five alignment tools. Bowtie has the highest accuracy, which is 92.42%, while GMAP has the lowest, which is 49.63%. Tophat has the highest sensitivity , which is 71.35% , while GMAP has the lowest, which is 51.69%.","abstract_has_math":false,"creators":["Lu, Yuanpeng"],"institution":null,"degree_name":"Master of Science in Bioinformatics - (M.S.)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhi Wei","Usman W. Roshan","Egbert Ammicht"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-05-31T07:00:00Z","date_published":"2013-05-31T07:00:00Z","updated_at":"2026-07-24T03:22:26Z","subjects":["RNA-seq alignment tools","High throughput sequencing","Bioinformatics","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/theses/167","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhi Wei","Usman W. 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In recent years, a large number of mapping algorithms have been developed, all of which have in common that they align a vast number of reads to genomic or transcriptomic sequences. RNA-Seq data is discrete in nature, therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. To provide guidance in the choice of alignment algorithms, five different alignment tools for RNA-Seq data are evaluated. In order to compare the accuracy and sensitivity of the Bowtie, Bowtie2, GMAP, Tophat and GNUMAP tools, their alignment accuracy for approximately 1 million simulated reads of chromosome one was evaluated using these five alignment tools. Bowtie has the highest accuracy, which is 92.42%, while GMAP has the lowest, which is 49.63%. Tophat has the highest sensitivity , which is 71.35% , while GMAP has the lowest, which is 51.69%."]},{"key":"dc:title","label":"Title","values":["Performance comparison of five RNA-seq alignment tools"]}]}],"canonical_facts":{"dc:contributor":["Zhi Wei","Usman W. Roshan","Egbert Ammicht"],"dc:creator":["Lu, Yuanpeng"],"dc:description.abstract":["Aligning millions of short reads to a reference genome is a critical task in high throughput sequencing. In recent years, a large number of mapping algorithms have been developed, all of which have in common that they align a vast number of reads to genomic or transcriptomic sequences. RNA-Seq data is discrete in nature, therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. To provide guidance in the choice of alignment algorithms, five different alignment tools for RNA-Seq data are evaluated. In order to compare the accuracy and sensitivity of the Bowtie, Bowtie2, GMAP, Tophat and GNUMAP tools, their alignment accuracy for approximately 1 million simulated reads of chromosome one was evaluated using these five alignment tools. Bowtie has the highest accuracy, which is 92.42%, while GMAP has the lowest, which is 49.63%. Tophat has the highest sensitivity , which is 71.35% , while GMAP has the lowest, which is 51.69%."],"dc:identifier":["https://digitalcommons.njit.edu/theses/167"],"dc:subject":["RNA-seq alignment tools","High throughput sequencing","Bioinformatics","Computer Sciences"],"dc:title":["Performance comparison of five RNA-seq alignment tools"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science in Bioinformatics - (M.S.)"]},"updated_at":"2026-07-24T03:22:26Z"}