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          StarSeq 100
          The industry's first high-throughput gene sequencer equipped with AI deep learning algorithm technology
          Flexible throughput
          Better precision
          Better Intelligence
          StarSeq 100

          Pioneering the innovation of intelligent sequencing

          Starseq100, the world's first NGS sequencer to deeply integrate AI data analysis technology into its sequencing platform,
          leads intelligent sequencing into a new era. Its uniqueness lies in the integration of four-color fluorescence technology, the seamless connection of sequencing data through innovative custom-designed assay kits and flexible optimized experimental protocols, complemented by a cutting-edge AI-driven data processing system. This breakthrough design provides global scientific research and clinical users with an unprecedentedly accurate and integrated solution to achieve efficient end-to-end linkage from samples to results, opening up a new milestone in the field of genetic sequencing.
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          Performance Advantage

          SBS
          Sequencing-by-synthesis
          80-250M
          Reading throughput FCx2
          SE50-PE100
          Sequencing read length
          50Gb
          Sequencing throughput
          99.9%
          Q30 > 85%
          Sequencing accuracy
          13 Hr / 32 Hr
          SE75 / PE100
          Sequencing speed
          Parameter
          Number of FlowcellsNumber of Lane per FlowcellRead Number per FlowcellSupported TypesMaximum OutputQ30Sequencing Time
          2280~125MSE5012.5G>85%10hrs
          SE7518.75G13hrs
          PE3618G17hrs
          PE7537.5G26hrs
          PE10050G32hrs
          Supported sample number in a single run on the StarSeq100 platform
          ApplicationRecommended Read lengthData Volume/sample1FC/Run2FC/Run
          NIPTSE50~5Mreads25 samples50 samples
          NIPT Plus~10Mreads12 samples25 samples
          PGS~5Mreads25 samples50 samples
          tNGSSE75~0.5Mreads250 samples500 samples
          mNGS~20Mreads6 samples13 samples
          Early Cancer Screening in OncologyPE36~30Mreads4 samples8 samples
          Tumor Companion Diagnostics / FFPEPE75~2Gb9 samples18 samples
          Tumor Small Panel TestingPE100~1Gb25 samples50 samples
          Tumor Large Panel Testing~5Gb5 samples10 samples
          Bacterial and Viral Whole Genome Sequencing (WGS)~1Gb25 samples50 samples
          Test Data
          StarSeq ? 100 Testing Results of a Primary Bioinformatics Pipeline Driven by Deep Learning
          Experiment IDSequencing PurposeAlgorithmRead number(M)Mapping Reads(M)Average Q30 base percentageAverage Q30
          1Biochemical ExperimentsConventional Algorithms84.7473.2486.42%0.82
          Deep Learning123.05119.9297.46%0.85
          2Instrument Quality TestingConventional Algorithms90.2581.7890.61%0.83
          Deep Learning128.26124.997.38%0.85
          3Customer Environment SequencingConventional Algorithms83.8777.292.04%0.82
          Deep Learning117.53113.8596.87%0.85

          Simple operation process

          Process Animation

          Who trusts StarSeq100?

          Related file downloads
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