{"id":2432,"date":"2011-01-11T12:38:20","date_gmt":"2011-01-11T12:38:20","guid":{"rendered":"http:\/\/hgpu.org\/?p=2432"},"modified":"2011-01-11T12:38:20","modified_gmt":"2011-01-11T12:38:20","slug":"quality-score-guided-error-correction-for-short-read-sequencing-data-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2432","title":{"rendered":"Quality-score guided error correction for short-read sequencing data using CUDA"},"content":{"rendered":"<p>Recently introduced new sequencing technologies can produce massive amounts of short-read data. Detection and correction of sequencing errors in this data is an important but time-consuming pre-processing step for de-novo genome assembly. In this paper, we demonstrate how the quality-score value associated with each base-call can be integrated in a CUDA-based parallel error correction algorithm. We show that quality-score guided error correction can improve the assembly accuracy of several datasets from the NCBI SRA (Short-Read Archive) in terms of N50-values as well as runtime. We further propose a number of improvements of to our previously published CUDA-EC algorithm to improve its runtime by a factor of up to 1.88.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recently introduced new sequencing technologies can produce massive amounts of short-read data. Detection and correction of sequencing errors in this data is an important but time-consuming pre-processing step for de-novo genome assembly. In this paper, we demonstrate how the quality-score value associated with each base-call can be integrated in a CUDA-based parallel error correction algorithm. [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,89,3],"tags":[123,1781,14,272,525,20],"class_list":["post-2432","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-bioinformatics","tag-biology","tag-cuda","tag-error-recovery","tag-genetics","tag-nvidia"],"views":1959,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2432","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2432"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2432\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2432"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2432"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2432"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}