{"id":2736,"date":"2020-05-21T15:18:14","date_gmt":"2020-05-21T19:18:14","guid":{"rendered":"https:\/\/www.med.unc.edu\/genomics\/?page_id=2736"},"modified":"2023-02-22T19:21:21","modified_gmt":"2023-02-23T00:21:21","slug":"illumina","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/","title":{"rendered":"Illumina"},"content":{"rendered":"<h4>Platforms Available<\/h4>\n<span class=\"collapseomatic \" id=\"id69d0e207c0df7\"  tabindex=\"0\" title=\"MiSeq Platforms\"    >MiSeq Platforms<\/span><div id=\"target-id69d0e207c0df7\" class=\"collapseomatic_content \">\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignleft\" src=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg\" alt=\"Sequencing MISEQ\" width=\"300\" height=\"1670\" \/>The HTSF has MiSeq Platform to cover the 20M read\/ lane study needs. It is very versatile with a<img loading=\"lazy\" decoding=\"async\" class=\"alignright\" src=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/06\/hiseq-1.png\" alt=\"Hiseq\" width=\"300\" height=\"241\" \/> number\u00a0of\u00a0different cycle version that can standardly be run (up to paired end, 300 cycles) and can easily be manipulated to run unique or custom cycle formats. In the HTSF hands, MiSeq has been found to be more forgivable about library quality.The ability to run custom cycles and library quality flexibility, make MiSeq especially helpful with R&amp;D library development or pilot assays. It is the workhorse for a variety of applications, including targeted gene, small genome, and amplicon sequencing, ITS and 16S metagenomics. See applications for this platform on the table below.<\/p>\n<p>20M reads too much? HTSF also now offers the MiSeq Nano version for minimal cost. It yields only 1M reads per lane. For example, the HTSF uses the MiSeq Nano as a standard QAQC tool for pools which are planned to be run on the NovaSeq. This small data yield is enough for us to check library and barcode quality of the libraries in the pool. But more critical to a NovaSeq, is the balance of the pool. A Nano run allows us to check the balance, rebalance as needed prior to loading the larger much more expensive platform.<\/p>\n<p>The below table which compares the various MiSeq Nano and MiSeq capabilities can be found in the Forms and Guides tab, Illumina as part of <a style=\"background-color: #ffffff\" href=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/07\/HTSF-Illumina_Sequencing_Platform_Comparison_Chart_v7_12-2021_FINAL.pdf\">Illumina Platform Comparison and Specification Table<\/a>.<\/p>\n<div>\n<h3>Illumina MiSeq System Comparison and Specifications<\/h3>\n<table style=\"height: 602px;width: 99.0573%;border-collapse: collapse;border-style: solid;border-color: #007fae\" border=\"3\">\n<tbody>\n<tr style=\"height: 23px\">\n<td style=\"width: 16.3534%;height: 23px;text-align: center\"><\/td>\n<td style=\"width: 18.593%;height: 23px;text-align: center\"><strong style=\"color: #007fae\">MiSeq<\/strong><\/td>\n<td style=\"height: 23px;text-align: center;width: 19.6353%\"><strong style=\"color: #007fae\">MiSeq<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 23px\">\n<td style=\"width: 16.3534%;height: 23px;text-align: center\"><strong>Nickname for system<\/strong><\/td>\n<td style=\"width: 18.593%;height: 23px;text-align: center\" width=\"17%\"><strong>Nano<\/strong><\/td>\n<td style=\"width: 19.6353%;height: 23px;text-align: center\" width=\"23%\"><strong>MISEQ<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 16.3534%;height: 33px;text-align: center\" width=\"17%\"><strong>Platform<\/strong><\/td>\n<td style=\"width: 18.593%;height: 33px;text-align: center\" width=\"17%\">MiSeq, Nano<\/td>\n<td style=\"width: 19.6353%;height: 33px;text-align: center\" width=\"23%\">MiSeq<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 16.3534%;height: 53px;text-align: center\" width=\"17%\"><strong>Flowcells processed<\/strong><\/td>\n<td style=\"width: 18.593%;height: 53px;text-align: center\" width=\"17%\">1<\/td>\n<td style=\"width: 19.6353%;height: 53px;text-align: center\" width=\"23%\">1<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 16.3534%;height: 33px;text-align: center\" width=\"17%\"><strong>Depth<\/strong><\/td>\n<td style=\"width: 18.593%;height: 33px;text-align: center\" width=\"17%\">300-500 Mb<\/td>\n<td style=\"width: 19.6353%;height: 33px;text-align: center\" width=\"23%\">0.3 \u2013 15 Gb<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 16.3534%;height: 33px;text-align: center\" width=\"17%\"><strong>Run time<\/strong><\/td>\n<td style=\"width: 18.593%;height: 33px;text-align: center\" width=\"17%\">5-24 hr<\/td>\n<td style=\"width: 19.6353%;height: 33px;text-align: center\" width=\"23%\">5 \u2013 65 hr<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 16.3534%;height: 33px;text-align: center\" width=\"17%\"><strong>Lanes\/flowcell<\/strong><\/td>\n<td style=\"width: 18.593%;height: 33px;text-align: center\" width=\"17%\">1<\/td>\n<td style=\"width: 19.6353%;height: 33px;text-align: center\" width=\"23%\">1<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 16.3534%;height: 53px;text-align: center\" width=\"17%\"><strong>Max PE Reads\/LANE<\/strong><\/td>\n<td style=\"width: 18.593%;text-align: center;height: 53px\" width=\"17%\">2 million<\/td>\n<td style=\"width: 19.6353%;text-align: center;height: 53px\" width=\"23%\">25 million<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 16.3534%;height: 53px;text-align: center\" width=\"17%\"><strong>Max Clusters\/LANE<\/strong><\/td>\n<td style=\"width: 18.593%;text-align: center;height: 53px\" width=\"17%\">1 million<\/td>\n<td style=\"width: 19.6353%;text-align: center;height: 53px\" width=\"23%\">12.5 million<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 16.3534%;height: 53px;text-align: center\" width=\"17%\"><strong>Read Type Format<\/strong><\/td>\n<td style=\"width: 18.593%;text-align: center;height: 53px\" width=\"17%\">Single or Paired End<\/td>\n<td style=\"width: 19.6353%;text-align: center;height: 53px\" width=\"23%\">Single or Paired End<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 16.3534%;height: 53px;text-align: center\" width=\"17%\"><strong>Read Length Available<\/strong><\/td>\n<td style=\"width: 18.593%;text-align: center;height: 53px\" width=\"17%\">50X, 100X, 150X, 250X, 300x<\/td>\n<td style=\"width: 19.6353%;text-align: center;height: 53px\" width=\"23%\">50X, 100X, 150X, 250X, 300x<\/td>\n<\/tr>\n<tr style=\"height: 93px\">\n<td style=\"width: 16.3534%;height: 93px;text-align: center\" width=\"17%\"><strong>Guaranteed read #\/lane(see note below)<\/strong><\/td>\n<td style=\"width: 18.593%;text-align: center;height: 93px\" width=\"17%\">V2 chemistry <u>only<\/u><\/td>\n<td style=\"width: 19.6353%;text-align: center;height: 93px\" width=\"23%\">\n<ul>\n<li>15M , single end, \u00a0\u00a0v3 Chemistry<\/li>\n<li>30 M, Paired end, \u00a0\u00a0v3 Chemistry,<\/li>\n<li>8 M, single end, \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0v2 Chemistry,<\/li>\n<li>16 M , paired end,\u00a0\u00a0\u00a0v2 Chemistry<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<tr style=\"height: 54px\">\n<td style=\"width: 16.3534%;height: 54px;text-align: center\" width=\"17%\"><strong>Key applications<\/strong><\/td>\n<td style=\"width: 18.593%;text-align: center;height: 54px\" width=\"17%\">Small genome, amplicon, and targeted gene panel sequencing, confirming complex balanced pools<\/td>\n<td style=\"width: 19.6353%;text-align: center;height: 54px\" width=\"23%\">Small genome, amplicon, and targeted gene panel sequencing, confirming complex balanced pools<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<span class=\"collapseomatic \" id=\"id69d0e207c0f1a\"  tabindex=\"0\" title=\"NextSeq 2000\"    >NextSeq 2000<\/span><div id=\"target-id69d0e207c0f1a\" class=\"collapseomatic_content \">\n<p>The NextSeq 2000 is a mid range Illumina sequencing platform. There are multiple flowcell versions which each have different data yields\/ lane.The NextSeq works well for:<\/p>\n<ul>\n<li>Single Cell Gene Expression<\/li>\n<li style=\"text-align: left\">Small Scale Whole Genome Sequencing (WGS)<\/li>\n<li>Small Scale Whole Exome Sequencing (WES)<\/li>\n<li>Shotgun Metagenomics<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignleft wp-image-3907\" src=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2022\/02\/use-next-300x223.jpeg\" alt=\"A laboratory technician works with the NextSeq 2000 system\" width=\"200\" height=\"149\" srcset=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2022\/02\/use-next-300x223.jpeg 300w, https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2022\/02\/use-next-280x210.jpeg 280w, https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2022\/02\/use-next.jpeg 593w\" sizes=\"auto, (max-width: 200px) 100vw, 200px\" \/>Please view the table below to determine the best fit for your needs. If you have issues determining the best fit, HTSF is here to help you figure out the best format for your study\u2019s data need. There are 3 different flowcell sizes (P1, P2, P3). There is a more limited number of standard cycle set up. But the HTSF continues to run custom cycles on the NovaSeq.<\/p>\n<p>A few quick view details about the NextSeq 2000:<img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-3906\" src=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2022\/02\/illumna-1-264x300.jpeg\" alt=\"Material is inserted into the NextSeq 2000\" width=\"185\" height=\"210\" srcset=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2022\/02\/illumna-1-264x300.jpeg 264w, https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2022\/02\/illumna-1.jpeg 480w\" sizes=\"auto, (max-width: 185px) 100vw, 185px\" \/><\/p>\n<ul>\n<li>It uses patterned flowcells and 2 color channel chemistry.<\/li>\n<li>This system replaced the HiSeq platforms with some version similar in data output size, but at a lower cost\/ run.<\/li>\n<li>With a single lane, sample can be loaded as soon as the machine is available.<\/li>\n<li>To determine the best version for your study\u2019s needs, a conference with the HTSF is required.<\/li>\n<\/ul>\n<p>The below table which compares the various NextSeq loading version abilities can be found in the Forms and Guides tab, Illumina as part of <a href=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/07\/HTSF-Illumina_Sequencing_Platform_Comparison_Chart_v3_06-2020_FINAL.pdf\">Illumina Platform Comparison and Specification Table<\/a>.<\/p>\n<p>Please note, cycles can be made custom to certain library prep methods (i.e. 10x genomics, DropSeq).<\/p>\n<p>&nbsp;<\/p>\n<table style=\"height: 615px;width: 114.71%;border-color: #007fae\" border=\"3\" width=\"114.71%\">\n<tbody>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 28.6486%\" width=\"160\"><\/td>\n<td style=\"height: 33px;width: 71.1712%\" colspan=\"3\" width=\"398\"><strong>NextSeq 2000<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 28.6486%\" width=\"160\"><strong>Platform<\/strong><\/td>\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\"><strong>P1<\/strong><\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\"><strong>P2 Reagents<\/strong><\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\"><strong>P3 Reagents<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 28.6486%\" width=\"160\"><strong>Nickname for system<\/strong><\/td>\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">NextSeq P1<\/p>\n<\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">NextSeq P2<\/p>\n<\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">NextSeq P3<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 28.6486%\" width=\"160\"><strong>Flowcells processed<\/strong><\/td>\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 28.6486%\" width=\"160\"><strong>L<\/strong><strong>anes\/flowcell<\/strong><\/td>\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"height: 53px;width: 28.6486%\" width=\"160\"><strong>Max <\/strong><span style=\"color: #ff0000\"><strong>PAIRED END <\/strong><\/span><strong>Reads\/Flowcell<\/strong><\/td>\n<td style=\"height: 53px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">200 M<\/p>\n<\/td>\n<td style=\"height: 53px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">800 million<\/p>\n<\/td>\n<td style=\"height: 53px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">2.2 billion<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"height: 53px;width: 28.6486%\" width=\"160\"><strong>Max <\/strong><span style=\"color: #ff0000\"><strong>SINGLE END <\/strong><\/span><strong>Reads\/Flowcell<\/strong><\/td>\n<td style=\"height: 53px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">100 M<\/p>\n<\/td>\n<td style=\"height: 53px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">400 M<\/p>\n<\/td>\n<td style=\"height: 53px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">1.1 B<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 28.6486%\" width=\"160\"><span style=\"color: #ff0000\"><strong>Clusters<\/strong><\/span><strong>\/Flowcell<\/strong><\/td>\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">100 M<\/p>\n<\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">400 M<\/p>\n<\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">1.1 B<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 66px;width: 28.6486%\" rowspan=\"2\" width=\"160\"><strong>Read Type Format Available<\/strong><\/td>\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">Paired End<\/p>\n<\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">Paired End<\/p>\n<\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">Paired End*<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 71.1712%\" colspan=\"3\" width=\"398\">*some single end runs available upon request<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"height: 86px;width: 28.6486%\" rowspan=\"2\" width=\"160\"><strong>Read Length Available<\/strong><\/td>\n<td style=\"height: 53px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">150x, Custom<\/p>\n<\/td>\n<td style=\"height: 53px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">50x, 100x, 150x, Custom<\/p>\n<\/td>\n<td style=\"height: 53px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">50x*, 50x, 100x, 150x, Custom<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">na<\/p>\n<\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">na<\/p>\n<\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">*1&#215;50 available<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"height: 86px;width: 28.6486%\" rowspan=\"2\" width=\"160\"><strong>Guaranteed Data Yield\/ Flowcell <\/strong><\/td>\n<td style=\"height: 33px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">100M Clusters<\/p>\n<\/td>\n<td style=\"height: 33px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">400M clusters<\/p>\n<\/td>\n<td style=\"height: 33px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">1.1 B clusters<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"height: 53px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">200M Paired End Reads<\/p>\n<\/td>\n<td style=\"height: 53px;width: 19.2793%\" width=\"108\">\n<p style=\"text-align: center\">800M paired end\u00a0 Reads<\/p>\n<\/td>\n<td style=\"height: 53px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">2.2 billion paired end Reads<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 73px\">\n<td style=\"height: 73px;width: 28.6486%\" width=\"160\"><strong>Key applications<\/strong><\/td>\n<td style=\"height: 73px;width: 21.8018%\" width=\"122\">\n<p style=\"text-align: center\">Small WGS, WES, scRNA-Seq<\/p>\n<\/td>\n<td style=\"height: 73px;width: 19.2793%\" width=\"108\">Small WGS, WES, scRNA-Seq<\/td>\n<td style=\"height: 73px;width: 30.0901%\" width=\"168\">\n<p style=\"text-align: center\">Small WGS, WES, scRNA-Seq, smallRNA\/miRNA-Seq<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<table style=\"border-color: #007fae;width: 100.756%;height: 266px\" border=\"3\" width=\"484\">\n<tbody>\n<tr style=\"height: 24px\">\n<td style=\"width: 37.037%;height: 57px\" rowspan=\"2\" width=\"180\"><\/td>\n<td style=\"width: 152.079%;height: 24px\" colspan=\"2\" width=\"304\"><strong>NextSeq 2000 Requirements<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 31.2757%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">P2 Reagents<\/p>\n<\/td>\n<td style=\"width: 120.803%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">P3 Reagents<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 37.037%;height: 33px\" width=\"180\"><strong>Number of Lanes<\/strong><\/td>\n<td style=\"width: 31.2757%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<td style=\"width: 120.803%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">1<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 37.037%;height: 33px\" width=\"180\"><strong>Minimum Volume (ul) <\/strong><\/td>\n<td style=\"width: 31.2757%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">15<\/p>\n<\/td>\n<td style=\"width: 120.803%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">15<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 37.037%;height: 33px\" width=\"180\"><strong>Molarity (nM), <\/strong>preferred<\/td>\n<td style=\"width: 31.2757%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">5<\/p>\n<\/td>\n<td style=\"width: 120.803%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">5<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 43px\">\n<td style=\"width: 37.037%;height: 43px\" width=\"180\"><strong>Molarity (nM), <\/strong>min to make with NO dilution in prep to run FC<\/td>\n<td style=\"width: 31.2757%;height: 43px\" width=\"152\">\n<p style=\"text-align: center\">3<\/p>\n<\/td>\n<td style=\"width: 120.803%;height: 43px\" width=\"152\">\n<p style=\"text-align: center\">3<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 37.037%;height: 33px\" width=\"180\"><strong>Number of Clusters\/Flowcell<\/strong><\/td>\n<td style=\"width: 31.2757%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">400M<\/p>\n<\/td>\n<td style=\"width: 120.803%;height: 33px\" width=\"152\">\n<p style=\"text-align: center\">1.1B<\/p>\n<\/td>\n<\/tr>\n<tr style=\"height: 34px\">\n<td style=\"width: 37.037%;height: 34px\" width=\"180\"><strong>Number of Single End Reads Per Flowcell<\/strong><\/td>\n<td style=\"width: 31.2757%;height: 34px\" width=\"152\">\n<p style=\"text-align: center\">400M<\/p>\n<\/td>\n<td style=\"width: 120.803%;height: 34px\" width=\"152\">\n<p style=\"text-align: center\">1.1B<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<table style=\"border-color: #007fae;width: 99.7732%\" border=\"3\" width=\"484\">\n<tbody>\n<tr>\n<td style=\"width: 37.037%\" width=\"180\"><\/td>\n<td style=\"width: 150.231%\" colspan=\"2\" width=\"304\"><strong>Recommended Number of Samples per Pool for Each NextSeq Platform<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 37.037%\" width=\"180\"><strong>Small whole-genome sequencing<\/strong> (300 cycles) 130 Mb genome; &gt;\u00a030\u00d7 coverage<\/td>\n<td style=\"width: 31.2757%\" width=\"152\">\n<p style=\"text-align: center\">30<\/p>\n<\/td>\n<td style=\"width: 118.955%\" width=\"152\">\n<p style=\"text-align: center\">82<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 37.037%\" width=\"180\"><strong>Whole-exome sequencing<\/strong> (200 cycles) 50\u00d7 mean targeted coverage; 90% targeted coverage at 20\u00d7<\/td>\n<td style=\"width: 31.2757%\" width=\"152\">\n<p style=\"text-align: center\">16<\/p>\n<\/td>\n<td style=\"width: 118.955%\" width=\"152\">\n<p style=\"text-align: center\">44<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 37.037%\" width=\"180\"><strong>Shotgun Metagenomics<\/strong> (300 cycles) 50M reads\/sample<\/td>\n<td style=\"width: 31.2757%\" width=\"152\">\n<p style=\"text-align: center\">8<\/p>\n<\/td>\n<td style=\"width: 118.955%\" width=\"152\">\n<p style=\"text-align: center\">20<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 37.037%\" width=\"180\"><strong>Single-cell RNA-Seq<\/strong> (100 cycles) 4K cells, 50K reads\/cell<\/td>\n<td style=\"width: 31.2757%\" width=\"152\">\n<p style=\"text-align: center\">2<\/p>\n<\/td>\n<td style=\"width: 118.955%\" width=\"152\">\n<p style=\"text-align: center\">5<\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 37.037%\" width=\"180\"><strong>miRNA-Seq or small RNA analysis<\/strong> (50 cycles) 11M reads\/sample<\/td>\n<td style=\"width: 31.2757%\" width=\"152\">\n<p style=\"text-align: center\">n\/a<\/p>\n<\/td>\n<td style=\"width: 118.955%\" width=\"152\">\n<p style=\"text-align: center\">96<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<span class=\"collapseomatic \" id=\"id69d0e207c0fa7\"  tabindex=\"0\" title=\"NovaSeq 6000 Platform\"    >NovaSeq 6000 Platform<\/span><div id=\"target-id69d0e207c0fa7\" class=\"collapseomatic_content \">\n<p>The NovaSeq 6000 is currently the most powerful Illumina sequencing platform. There are multiple flowcell versions which each have different data yields\/ lane. The NovaSeq works well for:<\/p>\n<ul>\n<li>Large production studies<\/li>\n<li>Deep sequencing<\/li>\n<li>Whole Genome Sequencing (WGS)<\/li>\n<li>Whole Exome Sequencing (WES)<\/li>\n<li>RNA sequencing ( whole Transcriptome Sequencing)<\/li>\n<li>Tumor-Normal Profiling<\/li>\n<\/ul>\n<p>Please view the table below to determine the best fit for your needs. It can be difficult to determine best NovaSeq flowcell version. HTSF is here to help you figure out the best format for your study\u2019s data need. There are 2 pool loading workflows on the NovaSeq. The standard mode has a single pool run over the full flowcell. The XP mode allows different pool to be loaded on each lane. There is an additional fee per lane for the XP mode to be used. There is a more limited number of standard cycle set up. But the HTSF continues to run custom cycles on the NovaSeq.<\/p>\n<p>A few quick view details about the Novaseq6000:<\/p>\n<ul>\n<li style=\"list-style-type: none\">\n<ul>\n<li>It uses patterned flowcells and 2 color channel chemistry.<\/li>\n<li>This system provides users with the speed and flexibility to complete\u00a0large scale\u00a0projects faster and with greater output than previous HiSeq systems.<\/li>\n<li>The NovaSeq provides more data when compared to previous HiSeq systems.<\/li>\n<li>All NovaSeq version can run a different pool on each lane with the use an XP kit\u00a0(for an additional fee). Standard loading format has the same pool loaded over all lanes of the flowcell.<\/li>\n<li>To determine the best version for your study\u2019s needs, a conference with the HTSF is required.<\/li>\n<li>Due to high cost of NovaSeq flowcells, HTSF requires extra QAQC for all pools to\u00a0support\u00a0a successful run.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignright\" src=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/06\/novaseq-1.png\" alt=\"novaseq\" width=\"273\" height=\"274\" \/><\/p>\n<ul>\n<li>QAQC can include qPCR for library concentration determinant.<\/li>\n<li>QAQC usually includes a MiSeq Nano run to check library and barcodes quality and pool balance. We won\u2019t require a Nano if using SP\/PE\/50x flowcell, unless it is an extremely large pools of &gt;50 samples.<\/li>\n<li>NovaSeq requires more material to be loaded that is typically required on HiSeq Platforms. Please discuss with the HTSF what is required if you plan to submit study made libraries or pools.<\/li>\n<\/ul>\n<p>The below table which compares the various NovaSeq loading version abilities can be found in the Forms and Guides tab, Illumina as part of <a href=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/07\/HTSF-Illumina_Sequencing_Platform_Comparison_Chart_v7_12-2021_FINAL.pdf\">Illumina Platform Comparison and Specification Table<\/a>.<\/p>\n<p>Please note, cycles can be made custom to certain library prep methods (i.e. 10x genomics).<\/p>\n<h3>NovaSeq Standard Loading (one pool\/flowcell)<\/h3>\n<table style=\"width: 100%;border-collapse: collapse;border-style: solid;border-color: #007fae;height: 754px\" border=\"3\">\n<tbody>\n<tr style=\"height: 57px\">\n<td style=\"width: 14.2019%;height: 57px;text-align: center\" width=\"103\"><strong>Nickname for system<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center;height: 57px\" width=\"12%\"><strong style=\"color: #007fae\">SP<\/strong><\/td>\n<td style=\"width: 21.4789%;text-align: center;height: 57px\" width=\"14%\"><strong style=\"color: #007fae\">S1<\/strong><\/td>\n<td style=\"width: 17.4007%;text-align: center;height: 57px\" width=\"12%\"><strong style=\"color: #007fae\">S2<\/strong><\/td>\n<td style=\"text-align: center;width: 21.2442%;height: 57px\" width=\"10%\"><strong style=\"color: #007fae\">S4<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 14.2019%;height: 53px;text-align: center\" width=\"103\"><strong>Platform<\/strong><\/td>\n<td style=\"width: 24.1784%;height: 53px;text-align: center\" width=\"95\">S Prime<\/td>\n<td style=\"width: 21.4789%;height: 53px;text-align: center\">NovaSeq 6000 S1<\/td>\n<td style=\"width: 17.4007%;height: 53px;text-align: center\">NovaSeq 6000 S2<\/td>\n<td style=\"width: 21.2442%;height: 53px;text-align: center\">NovaSeq 6000 S4<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 14.2019%;height: 53px;text-align: center\" width=\"103\"><strong>Flowcells processed<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">1 or 2<\/td>\n<td style=\"width: 21.4789%;text-align: center\">1 or 2<\/td>\n<td style=\"width: 17.4007%;text-align: center\">1 or 2<\/td>\n<td style=\"width: 21.2442%;text-align: center\">1 or 2<\/td>\n<\/tr>\n<tr style=\"height: 43px\">\n<td style=\"width: 14.2019%;height: 43px;text-align: center\" width=\"103\"><strong>Depth<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">200-250 Gb (2x150bp)<\/td>\n<td style=\"width: 21.4789%;text-align: center\">400-500 Gb (2x150bp)<\/td>\n<td style=\"width: 17.4007%;text-align: center\">1000-1250 Gb (2x150bp)<\/td>\n<td style=\"width: 21.2442%;text-align: center\">2400-3000 Gb (2x150bp)<\/td>\n<\/tr>\n<tr style=\"height: 43px\">\n<td style=\"width: 14.2019%;height: 43px;text-align: center\" width=\"103\"><strong>Run time<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">1 dy \u2013 2 dy<\/td>\n<td style=\"width: 21.4789%;text-align: center\">1 dy \u2013 2 dy<\/td>\n<td style=\"width: 17.4007%;text-align: center\">1 dy \u2013 3 dy<\/td>\n<td style=\"width: 21.2442%;text-align: center\">1 dy \u20133 dy<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 14.2019%;height: 33px;text-align: center\" width=\"103\"><strong>Lanes\/flowcell<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">2<\/td>\n<td style=\"width: 21.4789%;text-align: center\">2<\/td>\n<td style=\"width: 17.4007%;text-align: center\">2<\/td>\n<td style=\"width: 21.2442%;text-align: center\">4<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 14.2019%;height: 53px;text-align: center\" width=\"103\"><strong>Max PE Reads\/Flowcell<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">1600 million<\/td>\n<td style=\"width: 21.4789%;text-align: center\">3.2 billion<\/td>\n<td style=\"width: 17.4007%;text-align: center\">7.6 billion<\/td>\n<td style=\"width: 21.2442%;text-align: center\">20 billion<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 14.2019%;height: 53px;text-align: center\" width=\"103\"><strong>Max Clusters\/Flowcell<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">800 million<\/td>\n<td style=\"width: 21.4789%;text-align: center\">1.6 billion<\/td>\n<td style=\"width: 17.4007%;text-align: center\">3.8 billion<\/td>\n<td style=\"width: 21.2442%;text-align: center\">10 billion<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 14.2019%;height: 53px;text-align: center\" width=\"103\"><strong>Read Type Format<\/strong><\/td>\n<td style=\"width: 63.058%;text-align: center;height: 53px\" colspan=\"3\" width=\"12%\">Paired End<\/p>\n<p>*single end available upon request<\/td>\n<td style=\"width: 21.2442%;text-align: center;height: 53px\" width=\"12%\">Paired End<\/td>\n<\/tr>\n<tr style=\"height: 83px\">\n<td style=\"width: 14.2019%;height: 83px;text-align: center\" width=\"103\"><strong>Read Length Available*<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">50x, 100x (SE), 150x<\/td>\n<td style=\"width: 21.4789%;text-align: center\">50x, 100x, and 150x<\/td>\n<td style=\"width: 17.4007%;text-align: center\">50x, 100x, and 150x<\/td>\n<td style=\"width: 21.2442%;text-align: center\">100x, 150x<\/td>\n<\/tr>\n<tr style=\"height: 93px\">\n<td style=\"width: 14.2019%;height: 93px;text-align: center\" width=\"103\"><strong>Guaranteed read #\/Flowcell (see note below)<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">1.4 billion, paired end<\/td>\n<td style=\"width: 21.4789%;text-align: center\">3 billion, paired end<\/td>\n<td style=\"width: 17.4007%;text-align: center\">7 billion, paired end<\/td>\n<td style=\"width: 21.2442%;text-align: center\">17 billion, paired end.<\/td>\n<\/tr>\n<tr style=\"height: 104px\">\n<td style=\"width: 14.2019%;height: 104px;text-align: center\" width=\"103\"><strong>Key applications<\/strong><\/td>\n<td style=\"width: 24.1784%;text-align: center\" width=\"95\">WGS model organisms, FAIRE\/ChIP-seq large pools, metagenomics<\/td>\n<td style=\"width: 21.4789%;text-align: center\">Single Trio Human, 10X single cell, Chip-seq transcriptome<\/td>\n<td style=\"width: 17.4007%;text-align: center\">Production-scale genome, exome, transcriptome, sequencing, and more<\/td>\n<td style=\"width: 21.2442%;text-align: center\">Large production-scale genome, exome, transcriptome, sequencing, and more<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>NovaSeq-XP Mode Loading (one pool\/lane)<\/h3>\n<table style=\"height: 528px;width: 100%;border-collapse: collapse;border-style: solid;border-color: #007fae\" border=\"3\">\n<tbody>\n<tr style=\"height: 34px\">\n<td style=\"width: 20.0704%;text-align: center;height: 34px\" width=\"94\"><strong>Nickname for system<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 34px\" width=\"104\"><strong style=\"color: #007fae\">SP-XP<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 34px\" width=\"131\"><strong style=\"color: #007fae\">S1- XP<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 34px\" width=\"113\"><strong style=\"color: #007fae\">S2-XP<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 34px\" width=\"99\"><strong style=\"color: #007fae\">S4-XP<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 20.0704%;text-align: center;height: 33px\" width=\"94\"><strong>Platform<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"104\">NovaSeq 6000 SP XP<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"131\">NovaSeq 6000 S1 XP<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"113\">NovaSeq 6000 S2 XP<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"99\">NovaSeq 6000 S4 XP<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 20.0704%;text-align: center;height: 33px\" width=\"94\"><strong>Flowcells processed<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"104\">1<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"131\">1<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"113\">1<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"99\">1<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 20.0704%;text-align: center;height: 33px\" width=\"94\"><strong>Depth<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"104\">125 Gb (2x150bp)<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"131\">200-250 Gb (2x150bp)<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"113\">500-625 (2x150bp)<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"99\">600-750 Gb (2x150bp)<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 20.0704%;text-align: center;height: 33px\" width=\"94\"><strong>Run time<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"104\">1 dy \u2013 2 dy<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"131\">1 dy- 2 dy<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"113\">1 dy \u2013 2 dy<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"99\">1 dy \u2013 2 dy<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 20.0704%;text-align: center;height: 33px\" width=\"94\"><strong>Lanes\/flowcell<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"104\">2<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"131\">2<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"113\">2<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"99\">4<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 20.0704%;text-align: center;height: 33px\" width=\"94\"><strong>Max PE Reads\/LANE<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"104\">800 million<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"131\">1600 million<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"113\">3.8 billion<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"99\">5 billion<\/td>\n<\/tr>\n<tr style=\"height: 33px\">\n<td style=\"width: 20.0704%;text-align: center;height: 33px\" width=\"94\"><strong>Max Clusters\/LANE<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"104\">400 milion<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"131\">800 million<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"113\">1.9 billion<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 33px\" width=\"99\">2.5 billion<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 20.0704%;text-align: center;height: 53px\" width=\"94\"><strong>Read Type Format<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"104\">Paired End. Single End for some cycles<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"131\">Paired End. Single End for some cycles<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"113\">Paired End. Single End for some cycles<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"99\">Paired End<\/td>\n<\/tr>\n<tr style=\"height: 53px\">\n<td style=\"width: 20.0704%;text-align: center;height: 53px\" width=\"94\"><strong>Read Length Available<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"104\">50x, 150x,<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"131\">50x, 100x, 150x<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"113\">50x, 100x, and 150x<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 53px\" width=\"99\">100x, 150x<\/td>\n<\/tr>\n<tr style=\"height: 83px\">\n<td style=\"width: 20.0704%;text-align: center;height: 83px\" width=\"94\"><strong>Guaranteed read #\/lane(see note below)<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 83px\" width=\"104\">600 million, paired end<\/p>\n<p>&nbsp;<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 83px\" width=\"131\">1.4 billion, paired end<\/p>\n<p>&nbsp;<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 83px\" width=\"113\">3.2 billion, paired end<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 83px\" width=\"99\">4 billion, paired end<\/td>\n<\/tr>\n<tr style=\"height: 74px\">\n<td style=\"width: 20.0704%;text-align: center;height: 74px\" width=\"94\"><strong>Key applications<\/strong><\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 74px\" width=\"104\">10X single cell, Chip-seq transcriptome<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 74px\" width=\"131\">Single Trio Human, 10X single cell, Chip-seq transcriptome<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 74px\" width=\"113\">\u00a0genome, exome, transcriptome, ChIP-seq<\/td>\n<td style=\"width: 19.9531%;text-align: center;height: 74px\" width=\"99\">genome, exome, transcriptome,<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone\" src=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/working-sequencing_1-scaled.jpg\" alt=\"Sequencing\" width=\"2560\" height=\"1152\" \/><\/p>\n<\/div>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Platforms Available &nbsp; &nbsp; &nbsp;<\/p>\n","protected":false},"author":64010,"featured_media":0,"parent":2730,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"layout":"","cellInformation":"","apiCallInformation":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-2736","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Illumina - High-Throughput Sequencing Facility (HTSF)<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Illumina - High-Throughput Sequencing Facility (HTSF)\" \/>\n<meta property=\"og:description\" content=\"Platforms Available &nbsp; &nbsp; &nbsp;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/\" \/>\n<meta property=\"og:site_name\" content=\"High-Throughput Sequencing Facility (HTSF)\" \/>\n<meta property=\"article:modified_time\" content=\"2023-02-23T00:21:21+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/\",\"url\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/\",\"name\":\"Illumina - High-Throughput Sequencing Facility (HTSF)\",\"isPartOf\":{\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg\",\"datePublished\":\"2020-05-21T19:18:14+00:00\",\"dateModified\":\"2023-02-23T00:21:21+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#primaryimage\",\"url\":\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg\",\"contentUrl\":\"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg\",\"width\":2560,\"height\":1670},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/www.med.unc.edu\/genomics\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Technology\",\"item\":\"https:\/\/www.med.unc.edu\/genomics\/technology\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Illumina\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/www.med.unc.edu\/genomics\/#website\",\"url\":\"https:\/\/www.med.unc.edu\/genomics\/\",\"name\":\"High-Throughput Sequencing Facility (HTSF)\",\"description\":\"Integrated Genomics Cores\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/www.med.unc.edu\/genomics\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Illumina - High-Throughput Sequencing Facility (HTSF)","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/","og_locale":"en_US","og_type":"article","og_title":"Illumina - High-Throughput Sequencing Facility (HTSF)","og_description":"Platforms Available &nbsp; &nbsp; &nbsp;","og_url":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/","og_site_name":"High-Throughput Sequencing Facility (HTSF)","article_modified_time":"2023-02-23T00:21:21+00:00","og_image":[{"url":"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg","type":"","width":"","height":""}],"twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"8 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/","url":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/","name":"Illumina - High-Throughput Sequencing Facility (HTSF)","isPartOf":{"@id":"https:\/\/www.med.unc.edu\/genomics\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#primaryimage"},"image":{"@id":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#primaryimage"},"thumbnailUrl":"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg","datePublished":"2020-05-21T19:18:14+00:00","dateModified":"2023-02-23T00:21:21+00:00","breadcrumb":{"@id":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#primaryimage","url":"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg","contentUrl":"https:\/\/www.med.unc.edu\/genomics\/wp-content\/uploads\/sites\/708\/2020\/08\/sequencing_MISEQ_1-scaled.jpg","width":2560,"height":1670},{"@type":"BreadcrumbList","@id":"https:\/\/www.med.unc.edu\/genomics\/technology\/illumina\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.med.unc.edu\/genomics\/"},{"@type":"ListItem","position":2,"name":"Technology","item":"https:\/\/www.med.unc.edu\/genomics\/technology\/"},{"@type":"ListItem","position":3,"name":"Illumina"}]},{"@type":"WebSite","@id":"https:\/\/www.med.unc.edu\/genomics\/#website","url":"https:\/\/www.med.unc.edu\/genomics\/","name":"High-Throughput Sequencing Facility (HTSF)","description":"Integrated Genomics Cores","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.med.unc.edu\/genomics\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links_to":[],"_links_to_target":[],"_links":{"self":[{"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/pages\/2736","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/users\/64010"}],"replies":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/comments?post=2736"}],"version-history":[{"count":0,"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/pages\/2736\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/pages\/2730"}],"wp:attachment":[{"href":"https:\/\/www.med.unc.edu\/genomics\/wp-json\/wp\/v2\/media?parent=2736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}