{"id":5349,"date":"2019-10-26T14:59:44","date_gmt":"2019-10-26T16:59:44","guid":{"rendered":"http:\/\/nubo.ai\/?p=5349"},"modified":"2019-10-26T15:00:21","modified_gmt":"2019-10-26T17:00:21","slug":"mpi-blastn-and-ncbi-taxcollector-improving-metagenomic-analysis-with-high-performance-classification-and-wide-taxonomic-attachment","status":"publish","type":"post","link":"https:\/\/nubo.ai\/?p=5349","title":{"rendered":"MPI-blastn and NCBI-TaxCollector: Improving metagenomic analysis with high performance classification and wide taxonomic attachment"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"5349\" class=\"elementor elementor-5349\" data-elementor-settings=\"[]\">\n\t\t\t<div class=\"elementor-inner\">\n\t\t\t\t<div class=\"elementor-section-wrap\">\n\t\t\t\t\t\t\t<section class=\"elementor-element elementor-element-67fb4b02 elementor-section-boxed elementor-section-height-default elementor-section-height-default elementor-section elementor-top-section\" data-id=\"67fb4b02\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t<div class=\"elementor-row\">\n\t\t\t\t<div class=\"elementor-element elementor-element-16f4ec7 elementor-column elementor-col-100 elementor-top-column\" data-id=\"16f4ec7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-column-wrap  elementor-element-populated\">\n\t\t\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t<div class=\"elementor-element elementor-element-100db479 elementor-widget elementor-widget-text-editor\" data-id=\"100db479\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-text-editor elementor-clearfix\"><p><!-- wp:paragraph --><\/p>\n<div>\n<p>Metagenomic sequencing technologies are advancing rapidly and the size of output data from high-throughput genetic sequencing has increased substantially over the years. This brings us to a scenario where advanced computational optimizations are requested to perform a metagenomic analysis. In this paper, we describe a new parallel implementation of nucleotide BLAST (MPI-blastn) and a new tool for taxonomic attachment of Basic Local Alignment Search Tool (BLAST) results that supports the NCBI taxonomy (NCBI-TaxCollector). MPI-blastn obtained a high performance when compared to the mpiBLAST and ScalaBLAST. In our best case, MPI-blastn was able to run 408 times faster in 384 cores. Our evaluations demonstrated that NCBI-TaxCollector is able to perform taxonomic attachments 125 times faster and needs 120 times less RAM than the previous TaxCollector. Through our optimizations, a multiple sequence search that currently takes 37 hours can be performed in less than 6 min and a post processing with NCBI taxonomic data attachment, which takes 48 hours, now is able to run in 23 min.<\/p>\n<\/div>\n<p>https:\/\/doi.org\/10.1142\/S0219720014500139<\/p>\n<p><!-- \/wp:paragraph --><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"Metagenomic sequencing technologies are advancing rapidly and the size of output data from high-throughput genetic sequencing has increased substantially over the years. This brings us to a scenario where advanced computational optimizations are requested to perform a metagenomic analysis. In this paper, we describe a new parallel implementation of nucleotide BLAST (MPI-blastn) and a new&#8230;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","footnotes":""},"categories":[49],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/nubo.ai\/index.php?rest_route=\/wp\/v2\/posts\/5349"}],"collection":[{"href":"https:\/\/nubo.ai\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nubo.ai\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nubo.ai\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/nubo.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=5349"}],"version-history":[{"count":2,"href":"https:\/\/nubo.ai\/index.php?rest_route=\/wp\/v2\/posts\/5349\/revisions"}],"predecessor-version":[{"id":5352,"href":"https:\/\/nubo.ai\/index.php?rest_route=\/wp\/v2\/posts\/5349\/revisions\/5352"}],"wp:attachment":[{"href":"https:\/\/nubo.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5349"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nubo.ai\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5349"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nubo.ai\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5349"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}