{"id":1028,"date":"2026-04-19T15:17:41","date_gmt":"2026-04-19T15:17:41","guid":{"rendered":"https:\/\/www.embl.org\/groups\/genomics\/?page_id=1028"},"modified":"2026-06-16T07:41:53","modified_gmt":"2026-06-16T07:41:53","slug":"transcriptome-sequencing-rna-sequencing","status":"publish","type":"page","link":"https:\/\/www.embl.org\/groups\/genomics\/transcriptome-sequencing-rna-sequencing\/","title":{"rendered":"Transcriptome sequencing: RNA-Sequencing"},"content":{"rendered":"\n<div class=\"vf-grid | vf-grid__col-1\"><div><!--[vf\/content]-->\n<div class=\"vf-content\">\n\n<p>Transcriptome sequencing (often called RNA-seq) measures RNA molecules in a sample to show which genes are active and at what levels. It enables quantitative comparisons between conditions (e.g., treated vs untreated) and can reveal changes in cellular state and biological pathways. Practical differences between studies typically come from sample preparation, library design (e.g., poly(A) selection vs ribosomal RNA depletion), sequencing depth, and data analysis, which mainly influence sensitivity and the level of detail that can be resolved.<\/p>\n\n\n<style>\n    <\/style>\n\n<section id=\"wp-block-1\">\n  <div class=\"vf-card-container vf-card-container__col-3 | vf-u-fullbleed  \n  | vf-u-background-color-ui--white \">\n        <div class=\"vf-card-container__inner\">\n            <div class=\"vf-section-header | vf-u-margin__bottom--600 | vf-u-sr-only\">\n        <h2 class=\"vf-section-header__heading\" >\n        Cards    <\/h2>\n              <\/div>\n      \n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      mRNA-seq    <\/h3>\n                <p class=\"vf-card__text\">Is a sequencing method that enriches for messenger RNA (mRNA), typically by selecting polyadenylated RNA (poly(A)+), which represents transcripts produced by active genes. By sequencing these transcripts, it provides a snapshot of gene expression, showing which genes are expressed and at what levels, and it can also reveal alternative transcript isoforms depending on the assay design.\r\n\r\n&nbsp;<\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      Ribosomal RNA depleted RNA-seq    <\/h3>\n                <p class=\"vf-card__text\">Ribosomal RNA depleted RNA-seq is a sequencing method in which ribosomal RNA is removed so that the remaining RNA, including mRNA, long non-coding RNAs, and other transcripts, can be analyzed. This approach provides a broad view of the transcriptome, not only protein-coding genes\u200e.<\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n      Quant-seq ( 3&#039;-end seq )    <\/h3>\n                <p class=\"vf-card__text\">Quant-seq (3\u2032-end seq ) is an RNA-seq approach that sequences the 3\u2032 ends of polyadenylated transcripts near the poly(A) tail for efficient and cost-effective gene expression quantification across many samples. It can also be used to study alternative polyadenylation, but generally provides less information on full-length isoforms than standard RNA-seq.<\/p>\n      <\/div>\n<\/article>\n\n\n\n          <\/div>\n      <\/div>\n<\/section>\n\n\n<hr class=\"vf-divider\">\n\n\n<style>\n    <\/style>\n\n<section id=\"wp-block-2\">\n  <div class=\"vf-card-container vf-card-container__col-3 | vf-u-fullbleed  \n  | vf-u-background-color-ui--white \">\n        <div class=\"vf-card-container__inner\">\n            <div class=\"vf-section-header | vf-u-margin__bottom--600\">\n        <h2 class=\"vf-section-header__heading\" >\n        Key References    <\/h2>\n              <\/div>\n      \n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n              <a class=\"vf-card__link\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/18516045\/\">\n      mRNA-seq       <svg aria-hidden=\"true\" class=\"vf-card__heading__icon | vf-icon vf-icon-arrow--inline-end\" width=\"1em\" height=\"1em\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path d=\"M0 12c0 6.627 5.373 12 12 12s12-5.373 12-12S18.627 0 12 0C5.376.008.008 5.376 0 12zm13.707-5.209l4.5 4.5a1 1 0 010 1.414l-4.5 4.5a1 1 0 01-1.414-1.414l2.366-2.367a.25.25 0 00-.177-.424H6a1 1 0 010-2h8.482a.25.25 0 00.177-.427l-2.366-2.368a1 1 0 011.414-1.414z\" fill=\"currentColor\" fill-rule=\"nonzero\"><\/path>\n       <\/svg>\n        <\/a>\n          <\/h3>\n                <p class=\"vf-card__text\">&#8220;<em>Mapping and quantifying mammalian transcriptomes by RNA-Seq<\/em>&#8220;<\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n              <a class=\"vf-card__link\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33221877\/\">\n      Ribosomal RNA depleted RNA-seq       <svg aria-hidden=\"true\" class=\"vf-card__heading__icon | vf-icon vf-icon-arrow--inline-end\" width=\"1em\" height=\"1em\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path d=\"M0 12c0 6.627 5.373 12 12 12s12-5.373 12-12S18.627 0 12 0C5.376.008.008 5.376 0 12zm13.707-5.209l4.5 4.5a1 1 0 010 1.414l-4.5 4.5a1 1 0 01-1.414-1.414l2.366-2.367a.25.25 0 00-.177-.424H6a1 1 0 010-2h8.482a.25.25 0 00.177-.427l-2.366-2.368a1 1 0 011.414-1.414z\" fill=\"currentColor\" fill-rule=\"nonzero\"><\/path>\n       <\/svg>\n        <\/a>\n          <\/h3>\n                <p class=\"vf-card__text\">&#8220;<em>Optimized design of antisense oligomers for targeted rRNA depletion<\/em>&#8220;<\/p>\n      <\/div>\n<\/article>\n\n\n\n\n<article class=\"vf-card vf-card--brand vf-card--striped vf-u-margin__bottom--800\" default>\n  <div class=\"vf-card__content | vf-stack vf-stack--400\">\n      <h3 class=\"vf-card__heading\">\n              <a class=\"vf-card__link\" href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC6906367\/\">\n      Quant-seq ( 3&#039;-end seq )       <svg aria-hidden=\"true\" class=\"vf-card__heading__icon | vf-icon vf-icon-arrow--inline-end\" width=\"1em\" height=\"1em\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n          <path d=\"M0 12c0 6.627 5.373 12 12 12s12-5.373 12-12S18.627 0 12 0C5.376.008.008 5.376 0 12zm13.707-5.209l4.5 4.5a1 1 0 010 1.414l-4.5 4.5a1 1 0 01-1.414-1.414l2.366-2.367a.25.25 0 00-.177-.424H6a1 1 0 010-2h8.482a.25.25 0 00.177-.427l-2.366-2.368a1 1 0 011.414-1.414z\" fill=\"currentColor\" fill-rule=\"nonzero\"><\/path>\n       <\/svg>\n        <\/a>\n          <\/h3>\n                <p class=\"vf-card__text\">&#8220;<em>QuantSeq. 3&#8242; Sequencing combined with Salmon provides a fast, reliable approach for high throughput RNA expression analysis<\/em>&#8220;<\/p>\n      <\/div>\n<\/article>\n\n\n\n          <\/div>\n      <\/div>\n<\/section>\n\n\n<hr class=\"vf-divider\">\n\n\n\n<div class=\"vf-grid | vf-grid__col-2\"><div><!--[vf\/content]-->\n<div class=\"vf-content\">\n\n<a href=\"https:\/\/www.embl.org\/groups\/genomics\/#Applications\" target=\"_blank\">\n<button class=\"vf-button vf-button--primary\">More applications<\/button>\n<\/a>\n<!--\/vf-button-->\n\n\n<\/div>\n<\/div>\n\n\n<div><!--[vf\/content]-->\n<div class=\"vf-content\">\n\n<\/div>\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<div class=\"vf-grid | vf-grid__col-1\"><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":19,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"template-title-left-aligned.php","meta":{"_acf_changed":false,"footnotes":""},"embl_taxonomy":[],"class_list":["post-1028","page","type-page","status-publish","hentry"],"acf":[],"embl_taxonomy_terms":[],"_links":{"self":[{"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/pages\/1028","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/users\/19"}],"replies":[{"embeddable":true,"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/comments?post=1028"}],"version-history":[{"count":54,"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/pages\/1028\/revisions"}],"predecessor-version":[{"id":1820,"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/pages\/1028\/revisions\/1820"}],"wp:attachment":[{"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/media?parent=1028"}],"wp:term":[{"taxonomy":"embl_taxonomy","embeddable":true,"href":"https:\/\/www.embl.org\/groups\/genomics\/wp-json\/wp\/v2\/embl_taxonomy?post=1028"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}