{"title":"Advanced Molecular Diagnostics","description":null,"products":[{"product_id":"miseq-i100-system-20115694","title":"MiSeq i100 System","description":"\u003cp\u003eThe MiSeq i100 Series delivers our fastest run times yet, breakthrough simplicity, and significant sustainability advancements to empower every lab, everywhere\u003c\/p\u003e\n\u003cp\u003eThe new standard in benchtop sequencing speed and simplicity\u003c\/p\u003e\n\u003cp\u003eSame-day (and same-shift) results are now possible. The MiSeq i100 and MiSeq i100 Plus Systems provide results 4× faster than the MiSeq System, with sequencing run times as fast as four hours.\u003c\/p\u003e\n\u003cp\u003eWith the expanded output offered by the MiSeq i100 Series, you can increase sample throughput, perform deeper sequencing, and gain more insights for a wide range of applications faster than ever. XLEAP-SBS chemistry on the MiSeq i100 Series eliminates thawing requirements so reagents can be used whenever your sample is ready.\u003c\/p\u003e\n\u003cp\u003eThe MiSeq i100 Series makes NGS simpler than ever\u003c\/p\u003e\n\u003cp\u003eEvery aspect of the MiSeq i100 Series workflow is noticeably simplified, from setup to data analysis. Whether you are new to sequencing or an experienced user, you’ll appreciate the streamlined operations. Run setup requires only three steps and can be completed in less than 20 minutes.\u003c\/p\u003e\n\u003cp\u003eIntegrated cartridges simplify library loading and eliminate the need for washes between runs, improving sequencing efficiency.\u003c\/p\u003e\n\u003cp\u003eGenerate data analysis summaries in two hours or less for most applications by accessing preconfigured DRAGEN pipelines, either onboard or in the cloud.\u003c\/p\u003e\n\u003cp\u003eGround-breaking sustainability innovations\u003c\/p\u003e\n\u003cp\u003eIn addition to breakthrough advancements in system design, XLEAP-SBS chemistry, and integrated data analysis, the MiSeq i100 Series is optimized to reduce environmental impact. Key innovations, such as an 85% reduction in packaging waste* and a 35% reduction in the total carbon footprint, plus room-temperature ship-and-store consumables, deliver remarkable sustainability and user experience benefits.\u003c\/p\u003e\n\u003cp\u003eTrusted for over a decade, the MiSeq System has been shipped to over 10,000 customers globally, and has been cited in over 160,000 peer-reviewed publications.\u003c\/p\u003e\n\u003cp\u003eBuilding on our extensive expertise, Illumina has a relentless commitment to innovation and building future next-generation sequencing (NGS) capabilities and applications. The MiSeq i100 Series demonstrates our commitment to increasing access to genomics technology by continuing to deliver faster, simpler sequencing.\u003c\/p\u003e\n\u003cp\u003eSee how the MiSeq i100 Series is setting the standard for simplicity, usability, and speed of benchtop sequencing from Illumina.\u003c\/p\u003e\n\u003cp\u003eIllumina provides a world-class support team composed of experienced scientists who are experts in library prep, sequencing, and analysis. This exceptional technical support is available via phone five days a week or online 24\/7, worldwide, and in multiple languages, with rapid response time near most major metropolitan areas.\u003c\/p\u003e\n\u003cp\u003eTo help you get high-quality results on Illumina technology even faster, we offer instructor-led or hands-on courses and web-based training options.\u003c\/p\u003e\n\u003cp\u003eWith both supported workflows and a large ecosystem of choices, Illumina products are available for every step of the MiSeq i100 Series workflow.\u003c\/p\u003e\n\u003cp\u003eThe MiSeq i100 Series offers highly accurate quality data for various applications, including oncology, infectious disease, and microbiology studies.\u003c\/p\u003e\n\u003cp\u003eAre you new to sequencing or considering your first instrument?\u003c\/p\u003e\n\u003cp\u003eThese resources cover key topics in NGS designed for beginners. We'll provide tutorials, guide you through the workflow and planning your first experiment.\u003c\/p\u003e\n\u003cp\u003eGet up and running quickly with a rental or leasing program designed to work with your budget. Choose from several options to obtain financing, including instrument leasing, instrument rental, and reagent rental.\u003c\/p\u003e\n\u003cp\u003eIllumina Proactive is a secure and remote instrument performance and support service. Our field support team can identify risks before your run, perform reagent lot tracking, maximize uptime, and improve lab efficiency. Register for a MyIllumina account to get started.\u003c\/p\u003e\n\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003ePrepare libraries: With the MiSeq i100 Series, libraries are loaded in three simple steps and in under 20 minutes.\u003c\/li\u003e\n\u003cli\u003eSequence: Increase efficiency with XLEAP-SBS chemistry and streamlined operations, including onboard denaturation and clustering to obtain fast, high-quality data.\u003c\/li\u003e\n\u003cli\u003eAnalyze data: Obtain accurate and efficient secondary analysis with preconfigured DRAGEN pipelines onboard or in the cloud, generating results in two hours or less for most applications.\u003c\/li\u003e\n\u003cli\u003eInterpret results: Gain insights and drive discovery with cloud-based tertiary analysis solutions for select workflows.\u003c\/li\u003e\n\u003cli\u003eRoom-temperature reagents: The reagents are shipped and stored at room temperature and ready to use immediately, expediting sequencing setup.\u003c\/li\u003e\n\u003cli\u003eIndex-first sequencing: Experience faster demultiplexing and real-time run management with index-first sequencing.\u003c\/li\u003e\n\u003cli\u003eXLEAP-SBS chemistry: XLEAP-SBS chemistry is our fastest, most robust, highest-quality sequencing chemistry to date.\u003c\/li\u003e\n\u003cli\u003eReady-to-use cartridge: Flow cells and fluidics integrated in a single cartridge simplify setup and eliminate washes.\u003c\/li\u003e\n\u003cli\u003eStreamlined bioinformatics: Predesigned DRAGEN pipelines onboard the MiSeq i100 Series and in the cloud streamline data analysis.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eApplications\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eoncology\u003c\/li\u003e\n\u003cli\u003einfectious disease\u003c\/li\u003e\n\u003cli\u003emicrobiology studies\u003c\/li\u003e\n\u003cli\u003eTranscriptomics\u003c\/li\u003e\n\u003cli\u003eMicrobial genomics\u003c\/li\u003e\n\u003cli\u003eTargeted gene sequencing\u003c\/li\u003e\n\u003cli\u003eQuality control\u003c\/li\u003e\n\u003cli\u003e3’ gene expression\u003c\/li\u003e\n\u003cli\u003eTargeted RNA panel\u003c\/li\u003e\n\u003cli\u003emRNA-Seq\u003c\/li\u003e\n\u003cli\u003eTotal RNA-Seq\u003c\/li\u003e\n\u003cli\u003ePathogen detection\u003c\/li\u003e\n\u003cli\u003e16S amplicon sequencing\u003c\/li\u003e\n\u003cli\u003eShallow shotgun metagenomics\u003c\/li\u003e\n\u003cli\u003eShotgun metagenomics\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eMiSeq i100 Series specifications\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOutput range\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1.5–30 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePaired-end reads per run\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10–200M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax read length\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2 × 500 bp\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRun time\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~4–24 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eMiSeq i100 Series\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRun time (range)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~4–24 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eNextSeq 1000 and 2000 Systems\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRun time (range)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8–44 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eMiSeq i100 Series\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax output\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e30 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eNextSeq 1000 and 2000 Systems\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax output\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e540 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eMiSeq i100 Series\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax reads per run\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e100 million single reads; 200 million paired-end reads\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eNextSeq 1000 and 2000 Systems\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax reads per run\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1.8 billion single reads; 3.6 billion paired-end reads\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eMiSeq i100 Series\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eInformatics options\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eOn-instrument: Custom menu of DRAGEN analysis pipelines; Cloud: Broad menu of DRAGEN analysis applications available on BaseSpace Sequence Hub and Illumina Connected Analytics; On-premises: Broad menu of DRAGEN analysis applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eNextSeq 1000 and 2000 Systems\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eInformatics options\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eOn-instrument: Custom menu of DRAGEN analysis pipelines; Cloud: Broad menu of DRAGEN analysis applications available on BaseSpace Sequence Hub and Illumina Connected Analytics; On-premises: Broad menu of DRAGEN analysis applications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2.5 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e5 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1.5 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e7.5 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e15 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e30 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e3 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e15 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e30 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e25 Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eOutput per flow cell for various read lengths — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingle reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e5M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingle reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e25M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingle reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e50M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingle reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e100M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePaired-end reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePaired-end reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e50M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePaired-end reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e100M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eReads passing filter per flow cell — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePaired-end reads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e200M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 90% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eQuality scores — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e≥ 85% of bases higher than Q30\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 4 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 4.5 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e1 × 100 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 5 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 7 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 7 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 7.5 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 150 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 8 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 15 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 15 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 15.5 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 300 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~ 24 hr\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eRun time — 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003e2 × 500 bp\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – 3’ gene expression\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−5M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – 3’ gene expression\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – 3’ gene expression\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e5−25\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – 3’ gene expression\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10−50\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – 3’ gene expression\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e25−100\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Targeted RNA panel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−5M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Targeted RNA panel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Targeted RNA panel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e5−25\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Targeted RNA panel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10−50\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Targeted RNA panel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e25−100\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – mRNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10−25M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – mRNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – mRNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – mRNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – mRNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−10\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Total RNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e50M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Total RNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Total RNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Total RNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTranscriptomics – Total RNA-Seq\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Pathogen detection\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0.5−1M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Pathogen detection\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−10\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Pathogen detection\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−50\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Pathogen detection\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−100\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Pathogen detection\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−200\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – 16S amplicon sequencing\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0.1−0.2M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – 16S amplicon sequencing\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−50\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – 16S amplicon sequencing\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−250\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – 16S amplicon sequencing\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−384\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – 16S amplicon sequencing\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−384\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shallow shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0.5−10M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shallow shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−10\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shallow shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−12\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shallow shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−25\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shallow shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−50\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10−25M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e—\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Shotgun metagenomics\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−10\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Small WGS\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0.5M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Small WGS\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−10\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Small WGS\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−50\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Small WGS\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−100\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMicrobial genomics – Small WGS\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−200\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTargeted gene sequencing – Amplicon-based\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0.1−50M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTargeted gene sequencing – Amplicon-based\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 25M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTargeted gene sequencing – Amplicon-based\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−250\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 50M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTargeted gene sequencing – Amplicon-based\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−384\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 100M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTargeted gene sequencing – Amplicon-based\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−384\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Reads per sample\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTargeted gene sequencing – Enrichment-based\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0.1−50M\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cstrong\u003eEstimated sample throughput for key applications — Number of samples per flow cell: 5M\u003c\/strong\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTargeted gene sequencing – Enrichment-based\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1−50\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch2\u003eDocuments\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/content\/dam\/illumina\/gcs\/assembled-assets\/marketing-literature\/miseq-i100-specification-sheet-m-gl-02244\/miseq-i100-specification-sheet-m-gl-02244.pdf\" target=\"_blank\" rel=\"noopener\"\u003eMiSeq i100 specifications\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/content\/dam\/illumina\/gcs\/assembled-assets\/marketing-literature\/miseq-i100-brochure-m-gl-02245\/miseq-i100-brochure-m-gl-02245.pdf\" target=\"_blank\" rel=\"noopener\"\u003eMiSeq i100 Series – Product brochure\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/content\/dam\/illumina\/gcs\/assembled-assets\/marketing-literature\/sequencing-platforms-brochure-m-gl-00451\/sequencing-platforms-brochure-m-gl-00451.pdf\" target=\"_blank\" rel=\"noopener\"\u003eSequencing systems brochure\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/content\/dam\/illumina-marketing\/documents\/products\/technotes\/hiseq-x-percent-pf-technical-note-770-2014-043.pdf\" target=\"_blank\" rel=\"noopener\"\u003eReads passing filter (technical note)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/content\/dam\/illumina\/gcs\/assembled-assets\/marketing-literature\/miseq-i100-index-first-tech-note-m-gl-02415\/miseq-i100-index-first-tech-note-m-gl-02415.pdf\" target=\"_blank\" rel=\"noopener\"\u003eMiSeq i100 Series – Index-first sequencing (technical note)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/content\/dam\/illumina\/gcs\/assembled-assets\/marketing-literature\/miseq-i100-sustainability-infographic-m-gl-02696\/miseq-i100-sustainability-infographic-m-gl-02696.pdf\" target=\"_blank\" rel=\"noopener\"\u003eMiSeq i100 Series sustainability infographic\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/content\/dam\/illumina-marketing\/documents\/icommunity\/u-zurich-microbial-ngs-customer-story-m-gl-03855.pdf\" target=\"_blank\" rel=\"noopener\"\u003eProducing better genomes with the MiSeq i100 Plus System\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eRelated Products\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/systems\/sequencing-platforms\/nextseq-1000-2000.html\" target=\"_blank\" rel=\"noopener\"\u003eNextSeq 1000 and 2000 Systems\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/products\/by-type\/sequencing-kits\/cluster-gen-sequencing-reagents\/miseq-i100-series-reagent-kits.html\" target=\"_blank\" rel=\"noopener\"\u003eMiSeq i100 Series Reagent Kits\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/products\/by-type\/sequencing-kits\/library-prep-kits\/illumina-dna-prep.html\" target=\"_blank\" rel=\"noopener\"\u003eIllumina DNA Prep\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/products\/by-type\/sequencing-kits\/library-prep-kits\/rna-prep-enrichment.html\" target=\"_blank\" rel=\"noopener\"\u003eIllumina RNA Prep with Enrichment\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.illumina.com\/products\/by-type\/sequencing-kits\/library-prep-kits\/microbial-amplicon-prep.html\" target=\"_blank\" rel=\"noopener\"\u003eIllumina Microbial Amplicon Prep\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Illumina","offers":[{"title":"Default Title","offer_id":46487041671247,"sku":"20115694","price":0.0,"currency_code":"ILS","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0712\/0748\/5519\/files\/hires-miseq-i100-front.jpg?v=1787652001"},{"product_id":"streck-cell-free-dna-bct","title":"Streck Cell-Free DNA BCT® Blood Collection Tube","description":"\u003cp\u003eCell-Free DNA BCT stabilizes cell-free DNA for up to 7 days. It contains a preservative reagent that reduces the release of contaminating cellular genomic DNA that can occur during sample handling, shipping and processing. The cfDNA and CTCs isolated from stored plasma can be used in next generation sequencing assays that have been cleared or approved for use with samples collected into Cell-Free DNA BCT.\u003c\/p\u003e\n\u003cp\u003ePerformance characteristics of this device have only been established on the Guardant360 ® CDx assay and Guardant Shield™ assay.\u003c\/p\u003e\n\u003cp\u003eQ: What is Cell-Free DNA BCT and what is it used for?\nA: Cell-Free DNA BCT is a direct-draw venous whole blood collection device intended for the collection, stabilization and transport of venous whole blood samples for use in conjunction with cell-free DNA next generation sequencing assays that have been cleared or approved for use with samples collected into the Cell-Free DNA BCT device.\u003c\/p\u003e\n\u003cp\u003eQ: What NGS assays is Cell-Free DNA BCT FDA cleared for?\nA: Performance characteristics of Cell-Free DNA BCT have been established on the Guardant360 CDx assay and Guardant Shield assay. Laboratories should verify that their specific NGS assay has been cleared or approved for use with samples collected in Cell-Free DNA BCT before implementing this collection device. If their assay has not been cleared or approved for use with samples collected in Cell-Free DNA BCT, they should use of the CE\/RUO version of the product.\u003c\/p\u003e\n\u003cp\u003eQ: How long does Cell-Free DNA BCT stabilize samples?\nA: Cell-Free DNA BCT stabilizes cell-free DNA for up to 7 days. This extended stability eliminates the need for immediate plasma isolation and allows for room temperature storage and shipping, reducing pre-analytical variation that can occur during sample handling and transport.\u003c\/p\u003e\n\u003cp\u003eQ: Does Cell-Free DNA BCT require cold chain shipping?\nA: No, Cell-Free DNA BCT allows for cold chain-free transport at room temperature. This simplifies logistics, reduces shipping costs and makes it easier to collect samples at remote or off-site locations without requiring specialized shipping arrangements or immediate processing.\u003c\/p\u003e\n\u003cp\u003eQ: Why is room temperature blood sample stabilization important for cell-free DNA testing?\nA: Room temperature stabilization eliminates the need for immediate plasma separation and cold chain shipping, which are major logistical challenges for cell-free DNA testing. Without stabilization, blood cells can lyse during transport, releasing contaminating genomic DNA that dilutes the cell-free DNA signal and compromises test accuracy. Cell-Free DNA BCT solves this by maintaining sample integrity for up to 7 days at room temperature, enabling reliable testing even when samples must be shipped from remote locations.\u003c\/p\u003e\n\u003cp\u003eQ: What is pre-analytical variation and how does Cell-Free DNA BCT reduce it?\nA: Pre-analytical variation refers to changes that occur in blood samples during collection, handling, transport and storage before laboratory testing. For cell-free DNA analysis, the most critical pre-analytical variable is cell lysis, which releases genomic DNA from white blood cells and dilutes the cell-free DNA targets. Cell-Free DNA BCT prevents cell lysis and stabilizes samples, ensuring that test results reflect the patient’s true biological state rather than artifacts introduced during sample processing.\u003c\/p\u003e\n\u003cp\u003eQ: What extraction kits are compatible with Cell-Free DNA BCT?\nA: Streck Cell-Free DNA BCT is designed to be compatible with a wide range of commercially available extraction methods. For cfDNA extraction, Streck has tested the following: QIAGEN QIAamp® Circulating Nucleic Acid Kit; Promega Maxwell® RSC ccfDNA LV Plasma Kit; Zymo Research MAGicBead™ cfDNA Kit; Thermo Fisher MagMAX™ Cell-Free DNA Isolation Kit; Macherey-Nagel NucleoSpin® Plasma XS Kit\u003c\/p\u003e\n\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e14-day cell-free DNA\/genomic DNA sample stability at 6 °C to 37 °C*\u003c\/li\u003e\n\u003cli\u003e7-day CTC sample stability at 15 °C to 30 °C*\u003c\/li\u003e\n\u003cli\u003eAvailable in 10 mL draw for research and ex-US registered applications\u003c\/li\u003e\n\u003cli\u003eBenefit: Eliminates the need for immediate plasma preparation and CTC processing\u003c\/li\u003e\n\u003cli\u003eBenefit: High-quality cell-free DNA can be used for a wide range of downstream research applications\u003c\/li\u003e\n\u003cli\u003eBenefit: Allows for room temperature storage and shipping of critical samples\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eApplications\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eLiquid biopsy \/ cell-free DNA (cfDNA) blood collection, stabilization and transport\u003c\/li\u003e\n\u003cli\u003eCirculating tumor cell (CTC) preservation\u003c\/li\u003e\n\u003cli\u003eResearch studies, drug discovery and assay development\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eIntended use \/ regulatory status\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eFor In Vitro Diagnostic Use\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTube draw volume\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10.0 mL\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTube size\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e16 mm x 100 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCell-free DNA stability\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e7 days\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eAdditive\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003epreservative reagent that reduces the release of contaminating cellular genomic DNA that can occur during sample handling, shipping and processing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eValidated assays\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eGuardant360 ® CDx assay and Guardant Shield™ assay\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eStabilization claim (FAQ)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCell-Free DNA BCT stabilizes cell-free DNA for up to 7 days.\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCold chain \/ shipping\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNo, Cell-Free DNA BCT allows for cold chain-free transport at room temperature.\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompatible cfDNA extraction kits (tested by Streck)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eQIAGEN QIAamp® Circulating Nucleic Acid Kit; Promega Maxwell® RSC ccfDNA LV Plasma Kit; Zymo Research MAGicBead™ cfDNA Kit; Thermo Fisher MagMAX™ Cell-Free DNA Isolation Kit; Macherey-Nagel NucleoSpin® Plasma XS Kit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch2\u003eDocuments\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/01_Cell-Free_DNA_BCT_IFU.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IFU\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/03_Cell-Free_DNA_BCT_RUO_IFU.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT RUO IFU\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/350648-17-.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT CE IFU\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/09\/01_Cell-Free_DNA_BCT_SDS.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT SDS\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/01_Cell-Free_DNA_BCT_SDS-1.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT SDS\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/02_Cell-Free_DNA_BCT_CE_SDS.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT CE SDS\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/276_Cell-Free_DNA_BCT_IVD_CoA_Lot_5113_2026-08-17.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5113 2026 08 17\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/277_Cell-Free_DNA_BCT_IVD_CoA_Lot_5140_2026-08-17.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5140 2026 08 17\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/278_Cell-Free_DNA_BCT_IVD_CoA_Lot_5169_2026-09-30.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5169 2026 09 30\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/279_Cell-Free_DNA_BCT_IVD_CoA_Lot_5188_2026-10-07.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5188 2026 10 07\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/280_Cell-Free_DNA_BCT_IVD_CoA_Lot_5225_2026-11-05.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5225 2026 11 05\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/281_Cell-Free_DNA_BCT_IVD_CoA_Lot_5232_2026-11-19.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5232 2026 11 19\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/282_Cell-Free_DNA_BCT_IVD_CoA_Lot_5252_2026-11-26.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5252 2026 11 26\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/283_Cell-Free_DNA_BCT_IVD_CoA_Lot_5274_2026-12-16.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5274 2026 12 16\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/284_Cell-Free_DNA_BCT_IVD_CoA_Lot_5281_2026-12-30.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5281 2026 12 30\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/285_Cell-Free_DNA_BCT_IVD_CoA_Lot_5309_2027-01-21.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5309 2027 01 21\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/286_Cell-Free_DNA_BCT_IVD_CoA_Lot_5324_2027-02-04.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5324 2027 02 04\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/287_Cell-Free_DNA_BCT_IVD_CoA_Lot_5340_2027-02-18.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 5340 2027 02 18\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/288_Cell-Free_DNA_BCT_IVD_CoA_Lot_6002_2027-03-15.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 6002 2027 03 15\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/289_Cell-Free_DNA_BCT_IVD_CoA_Lot_6015_2027-03-22.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 6015 2027 03 22\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/290_Cell-Free_DNA_BCT_IVD_CoA_Lot_6076_2027-06-15.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 6076 2027 06 15\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/Cell-Free_DNA_BCT_IVD_CoA_Lot_6034_2027-04-13.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 6034 2027 04 13\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/Cell-Free_DNA_BCT_IVD_CoA_Lot_6048_2027-05-03.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 6048 2027 05 03\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/03\/Cell-Free_DNA_BCT_IVD_CoA_Lot_6065_2027-06-01.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 6065 2027 06 01\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/wp-content\/uploads\/2026\/07\/Cell-Free_DNA_BCT_IVD_CoA_Lot_6096_2027-07-12.pdf\" target=\"_blank\" rel=\"noopener\"\u003eCell Free DNA BCT IVD CoA Lot 6096 2027 07 12\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eRelated Products\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/product-category\/sample-stabilization\/blood-collection-tubes\/\" target=\"_blank\" rel=\"noopener\"\u003eCategory: Blood Collection Tubes\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.streck.com\/product\/cell-free-dna-bct-ruo-cell-free-dna-bct-ce\/\" target=\"_blank\" rel=\"noopener\"\u003eCell-Free DNA BCT® RUO \u0026amp; Cell-Free DNA BCT CE\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Streck","offers":[{"title":"230469 · IVD · 6-tube pack (10.0 mL) – 16mm x 100mm","offer_id":46733022822479,"sku":"230469","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"230470 · IVD · 100-tube box (10.0 mL) – 16mm x 100mm","offer_id":46733022855247,"sku":"230470","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"230471 · IVD · 1000-tube case (10.0 mL) – 16mm x 100mm","offer_id":46733022888015,"sku":"230471","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"218996 · CE · 6-tube pack (10.0 mL) – 16mm x 100mm","offer_id":46733022920783,"sku":"218996","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"218997 · CE · 100-tube box (10.0 mL) – 16mm x 100mm","offer_id":46733022953551,"sku":"218997","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"230244 · CE · 1000-tube case (10.0 mL) – 16mm x 100mm","offer_id":46733022986319,"sku":"230244","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"218961 · RUO · 6-tube pack (10.0 mL) – 16mm x 100mm","offer_id":46733023019087,"sku":"218961","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"218962 · RUO · 100-tube box (10.0 mL) – 16mm x 100mm","offer_id":46733023051855,"sku":"218962","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"218992 · RUO · 1000-tube case (10.0 mL) – 16mm x 100mm","offer_id":46733023084623,"sku":"218992","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"230655 · CE · 6-tube pack (10.0 mL) – 16mm x 100mm","offer_id":46733023117391,"sku":"230655","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"230656 · CE · 100-tube box (10.0 mL) – 16mm x 100mm","offer_id":46733023150159,"sku":"230656","price":0.0,"currency_code":"ILS","in_stock":true},{"title":"230657 · CE · 1000-tube case (10.0 mL) – 16mm x 100mm","offer_id":46733023182927,"sku":"230657","price":0.0,"currency_code":"ILS","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0712\/0748\/5519\/files\/Cell-Free-DNA-BCT_2024_1000x1000_d4e45f6b-b940-4767-9658-83ce23b57739.png?v=1788947402"},{"product_id":"sophia-ddm-platform","title":"SOPHiA DDM™ Platform","description":"\u003cp\u003eA cutting-edge, cloud-native SaaS IVDR-certified* platform for the accurate analysis, standardization, and interpretation of complex healthcare data. Our advanced AI algorithms are breaking data siloes and democratizing access to anonymized real-world insights.\u003c\/p\u003e\n\u003cp\u003eChoose from three bespoke modules for diverse data types\u003c\/p\u003e\n\u003cp\u003eModules: SOPHiA DDM™ for Genomics; SOPHiA DDM™ for Radiomics; SOPHiA DDM™ for Multimodal\u003c\/p\u003e\n\u003cp\u003eA universal platform for analyzing diverse health data\u003c\/p\u003e\n\u003cp\u003eThe SOPHiA DDM™ Platform encompasses three state-of-the-art computational factories for the analysis of genomic, radiomic, and multimodal data.\u003c\/p\u003e\n\u003cp\u003eAccurately detect and classify variants from complex oncology and rare \u0026amp; inherited disorder data, with information from curated databases and community insights empowering confident decision-making.\u003c\/p\u003e\n\u003cp\u003eGain novel insights through automated, validated image segmentation and feature extraction, with a platform agnostic to 3D imaging modality, equipment, and cancer indication.\u003c\/p\u003e\n\u003cp\u003eIntegrate longitudinal data from multiple sources and data modalities to generate new insights. Leverage SOPHiA CarePath® for enhanced data visualization, cohorting, and predictive analytics.\u003c\/p\u003e\n\u003cp\u003eSOPHiA DDM™ enables sensitive detection of clinically relevant variants at low allele frequencies (\u0026lt; 0.01% VAF) for MRD.\u003c\/p\u003e\n\u003cp\u003eBy incorporating our proprietary CUMIN™ unique molecular identifier (UMI) technology into sample preparation and leveraging our bioinformatic expertise, errors introduced during library preparation, target enrichment, or sequencing can be filtered out and true variant alleles identified.\u003c\/p\u003e\n\u003cp\u003eEnhance your clinical research programs with exploratory and testing\u003c\/p\u003e\n\u003cp\u003eIdentifying the specific molecular profiles of the target population for your clinical research program in an accurate, consistent, and standardized way can hinder your development process.\u003c\/p\u003e\n\u003cp\u003eBy applying our expertise in genomics and sophisticated algorithms, we help you discover, prioritize, and select the most promising biomarker targets for your clinical research program.\u003c\/p\u003e\n\u003cp\u003eConquer new summits in data-driven medicine\u003c\/p\u003e\n\u003cp\u003eSOPHiA DDM™ – Universally compatible, infinitely powerful\u003c\/p\u003e\n\u003cp\u003eA connected community for collective intelligence\u003c\/p\u003e\n\u003ch2\u003eKey Features\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eBest-in-class accuracy: Using robust AI-powered algorithms to pinpoint signals amongst noise for actionable insights.\u003c\/li\u003e\n\u003cli\u003eExpedited turnaround: Harness a comprehensive platform to quickly gather and analyze multiple data types.\u003c\/li\u003e\n\u003cli\u003eAccelerated adoption: Leverage our expertise to easily integrate and scale up the right application for your institution.\u003c\/li\u003e\n\u003cli\u003eOptimized resources: Save time and control costs on data processing and storage with our scalable infrastructure.\u003c\/li\u003e\n\u003cli\u003eFull data control: Confidently generate data in-house while securely maintaining full control over your samples and data.\u003c\/li\u003e\n\u003cli\u003eAccurate: AI\/machine learning-powered algorithms accurately, analyze, standardize, and annotate data for confident decision making.\u003c\/li\u003e\n\u003cli\u003eUniversal: The technology-agnostic platform easily integrates into existing workflows, supporting diverse data types and applications.\u003c\/li\u003e\n\u003cli\u003eDecentralized: Enabling in-house data analysis empowers institutions to maintain full control of their samples and data.\u003c\/li\u003e\n\u003cli\u003eScalable: CPUs operate alongside powerful NVIDIA GPUs to process computationally heavy workloads, with samples run on their own cloud nodes.\u003c\/li\u003e\n\u003cli\u003eInteroperable: Seamless, automated data transfer between technologies, EHR, LIMS, and interpretation tools for streamlined workflows.\u003c\/li\u003e\n\u003cli\u003eSecure: HIPAA and GDPR compliant, ISO\/IEC 27001, 27017 \u0026amp; 27018 certifications, and IVDR certified.*\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eApplications\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003eSOPHiA DDM™ for Genomics: Accurately detect and classify variants from complex oncology and rare \u0026amp; inherited disorder data, with information from curated databases and community insights empowering confident decision-making.\u003c\/li\u003e\n\u003cli\u003eSOPHiA DDM™ for Radiomics: Gain novel insights through automated, validated image segmentation and feature extraction, with a platform agnostic to 3D imaging modality, equipment, and cancer indication.\u003c\/li\u003e\n\u003cli\u003eSOPHiA DDM™ for Multimodal: Integrate longitudinal data from multiple sources and data modalities to generate new insights. Leverage SOPHiA CarePath® for enhanced data visualization, cohorting, and predictive analytics.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eDocuments\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/d3e30vlyff459p.cloudfront.net\/2b75c131-2c1d-4268-be32-67d15945dcb8\/5ab2bc78-543a-4abe-87c2-7deab9f8aad4\/5ab2bc78-543a-4abe-87c2-7deab9f8aad4_source__v.pdf\" target=\"_blank\" rel=\"noopener\"\u003eSOPHiA DDM™ Platform\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/d3e30vlyff459p.cloudfront.net\/2b75c131-2c1d-4268-be32-67d15945dcb8\/395e4ff5-a584-4140-b0fe-723ee3908181\/395e4ff5-a584-4140-b0fe-723ee3908181_source__v.pdf\" target=\"_blank\" rel=\"noopener\"\u003eData protection - US\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/d3e30vlyff459p.cloudfront.net\/2b75c131-2c1d-4268-be32-67d15945dcb8\/86e7dbdb-90fd-4a6c-a05a-406abddfb1dd\/86e7dbdb-90fd-4a6c-a05a-406abddfb1dd_source__v.pdf\" target=\"_blank\" rel=\"noopener\"\u003eData protection - Rest of world\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eRelated Products\u003c\/h2\u003e\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.sophiagenetics.com\/resource\/machine-learning-in-healthcare\/\" target=\"_blank\" rel=\"noopener\"\u003eBlog: Demystifying machine learning in healthcare: a layman's (...)\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.sophiagenetics.com\/resource\/the-power-of-multimodal-data-driven-medicine\/\" target=\"_blank\" rel=\"noopener\"\u003eBlog: The power of multimodal data-driven medicine\u003c\/a\u003e\u003c\/li\u003e\n\u003cli\u003e\u003ca href=\"https:\/\/www.sophiagenetics.com\/knowledge-hub\/\" target=\"_blank\" rel=\"noopener\"\u003eSOPHiA GENETICS Knowledge Hub\u003c\/a\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"SOPHiA GENETICS","offers":[{"title":"Default Title","offer_id":46733023248463,"sku":"SOPHIA-DDM","price":0.0,"currency_code":"ILS","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0712\/0748\/5519\/files\/Data-upload.jpg?v=1788951266"}],"url":"https:\/\/www.danyel.co.il\/collections\/advanced-molecular-diagnostics.oembed","provider":"danyel biotech","version":"1.0","type":"link"}