The aim of this project is to explore the possible application of Graphics Processors (GPUs) to accelerate and speed up sequence alignment by Fourier-space cross-correlation. Aligning signals using cross-correlations is a well studied approach in the world of signal processing, but has found relatively little reception in the realm of computational genomics. As long as we can treat DNA as a signal by encoding it numerically, we can utilize these cross-correlations to align and compare 2 or more strands of DNA or RNA. Fourier-space cross-correlations have a favorable computational complexity of O(n log_2(n)), where n is the length of the longer input strand. A single cross-correlation consists of three FFTs and a sliding dot-product, both of these types of operations are inherently parallel. Due to the extraordinary length of DNA sequences and the independence between operations, we can extort parallelism to a high degree. Therefore this problem maps very well to the highly parallel architecture of the modern GPU. This project explores the method, execution and performance of GPU-based DNA\/RNA alignment using cross-correlations.<\/p>\n","protected":false},"excerpt":{"rendered":"
The aim of this project is to explore the possible application of Graphics Processors (GPUs) to accelerate and speed up sequence alignment by Fourier-space cross-correlation. Aligning signals using cross-correlations is a well studied approach in the world of signal processing, but has found relatively little reception in the realm of computational genomics. As long as […]<\/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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,89,3,41],"tags":[123,1781,659,14,20,176,513,695,209,1789],"class_list":["post-9500","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","category-signal-processing","tag-bioinformatics","tag-biology","tag-computational-complexity","tag-cuda","tag-nvidia","tag-package","tag-python","tag-rna","tag-sequence-alignment","tag-signal-processing"],"views":2670,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9500","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=9500"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9500\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9500"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9500"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9500"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}