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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 Flowcells	Number of Lane per Flowcell	Read Number per Flowcell	Supported Types	Maximum Output	Q30	Sequencing Time
2	2	80~125M	SE50	12.5G	>85%	10hrs
			SE75	18.75G		13hrs
			PE36	18G		17hrs
			PE75	37.5G		26hrs
			PE100	50G		32hrs

Supported sample number in a single run on the StarSeq100 platform
Application	Recommended Read length	Data Volume/sample	1FC/Run	2FC/Run
NIPT	SE50	~5Mreads	25 samples	50 samples
NIPT Plus		~10Mreads	12 samples	25 samples
PGS		~5Mreads	25 samples	50 samples
tNGS	SE75	~0.5Mreads	250 samples	500 samples
mNGS	SE75	~20Mreads	6 samples	13 samples
Early Cancer Screening in Oncology	PE36	~30Mreads	4 samples	8 samples
Tumor Companion Diagnostics / FFPE	PE75	~2Gb	9 samples	18 samples
Tumor Small Panel Testing	PE100	~1Gb	25 samples	50 samples
Tumor Large Panel Testing		~5Gb	5 samples	10 samples
Bacterial and Viral Whole Genome Sequencing (WGS)		~1Gb	25 samples	50 samples

Test Data

StarSeq ? 100 Testing Results of a Primary Bioinformatics Pipeline Driven by Deep Learning
Experiment ID	Sequencing Purpose	Algorithm	Read number（M）	Mapping Reads（M)	Average Q30 base percentage	Average Q30
1	Biochemical Experiments	Conventional Algorithms	84.74	73.24	86.42%	0.82
1	Biochemical Experiments	Deep Learning	123.05	119.92	97.46%	0.85
2	Instrument Quality Testing	Conventional Algorithms	90.25	81.78	90.61%	0.83
2	Instrument Quality Testing	Deep Learning	128.26	124.9	97.38%	0.85
3	Customer Environment Sequencing	Conventional Algorithms	83.87	77.2	92.04%	0.82
3	Customer Environment Sequencing	Deep Learning	117.53	113.85	96.87%	0.85