{"id":47,"date":"2013-04-13T21:07:35","date_gmt":"2013-04-13T21:07:35","guid":{"rendered":"http:\/\/pop-gen.eu\/wordpress\/?page_id=47"},"modified":"2024-07-01T20:06:38","modified_gmt":"2024-07-01T20:06:38","slug":"sweed","status":"publish","type":"page","link":"https:\/\/pop-gen.eu\/wordpress\/software\/sweed","title":{"rendered":"SweeD"},"content":{"rendered":"<p>We developed SweeD, a parallel and checkpointable tool that implements a composite likelihood ratio test for detecting selective sweeps.<br \/>\nSweeD is based on the SweepFinder algorithm (Nielsen et al. 2005).<\/p>\n<p>SweeD can calculate the theoretical SFS of a given demographic model (stepwise changes or with an exponential growth phase + stepwise changes) by using the <a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/21426909\">method<\/a> by \u017divkovi\u0107 and Stephan (2011).<\/p>\n<p>SweeD is numerically more stable than SweepFinder (in terms of floating-point arithmetic operations and in particular for folded data), and is faster than SweepFinder when the number of sequences is large.<br \/>\nSweeD has been tested on simulated datasets with up to 10,000 sequences and 1,000,000 SNPs.<\/p>\n<p>The sequential version of SweeD is up to 21 times faster than SweepFinder, depending on the number of SNPs and the number of sequences.<br \/>\nPerformance improves over SweepFinder with an increasing number of sequences.<br \/>\nFor few sequences, SweeD is as fast as SweepFinder.<\/p>\n<p>SweeD has been also used to analyze the Chromosome 1 from the <a href=\"http:\/\/www.1000genomes.org\/\">1000 Genomes Project<\/a>.<br \/>\nThe dataset comprises more than 2000 sequences and about 2,896,000 SNPs. The analysis required 8h and 15mins.<\/p>\n<p><strong>You can download the source code of version 4.0.0 from the \u00a0<a href=\"https:\/\/github.com\/alachins\/sweed\">github repository<\/a><\/strong><br \/>\n<strong>(git clone https:\/\/github.com\/alachins\/sweed.git)<\/strong><\/p>\n<p>The manual is available <a href=\"http:\/\/sco.h-its.org\/exelixis\/countSweepManual.php\">here<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We developed SweeD, a parallel and checkpointable tool that implements a composite likelihood ratio test for detecting selective sweeps. SweeD is based on the SweepFinder algorithm (Nielsen et al. 2005). SweeD can calculate the theoretical SFS of a given demographic model (stepwise changes or with an exponential growth phase + stepwise changes) by using the &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/pop-gen.eu\/wordpress\/software\/sweed\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;SweeD&#8221;<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"parent":21,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-47","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/pages\/47","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/comments?post=47"}],"version-history":[{"count":25,"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/pages\/47\/revisions"}],"predecessor-version":[{"id":793,"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/pages\/47\/revisions\/793"}],"up":[{"embeddable":true,"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/pages\/21"}],"wp:attachment":[{"href":"https:\/\/pop-gen.eu\/wordpress\/wp-json\/wp\/v2\/media?parent=47"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}