{"id":12958,"date":"2014-10-20T20:42:16","date_gmt":"2014-10-20T17:42:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=12958"},"modified":"2014-10-20T20:42:16","modified_gmt":"2014-10-20T17:42:16","slug":"massively-parallel-read-mapping-on-gpus-with-the-q-group-index-and-peanut","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12958","title":{"rendered":"Massively parallel read mapping on GPUs with the q-group index and PEANUT"},"content":{"rendered":"<p>We present the q-group index, a novel data structure for read mapping tailored towards graphics processing units (GPUs) with a small memory footprint and efficient parallel algorithms for querying and building. On top of the q-group index we introduce PEANUT, a highly parallel GPU-based read mapper. PEANUT provides the possibility to output both the best hits or all hits of a read. Our benchmarks show that PEANUT outperforms other state-of-the-art read mappers in terms of speed while maintaining or slightly increasing precision, recall and sensitivity.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the q-group index, a novel data structure for read mapping tailored towards graphics processing units (GPUs) with a small memory footprint and efficient parallel algorithms for querying and building. On top of the q-group index we introduce PEANUT, a highly parallel GPU-based read mapper. PEANUT provides the possibility to output both the best [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,10,90,3],"tags":[1787,123,1781,20,1504,1793],"class_list":["post-12958","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-biology","category-opencl","category-paper","tag-algorithms","tag-bioinformatics","tag-biology","tag-nvidia","tag-nvidia-geforce-gtx-780","tag-opencl"],"views":2648,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12958","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=12958"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12958\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12958"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12958"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12958"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}