SBS
Sequencing-by-synthesis
Pioneering the innovation of intelligent sequencing
Performance Advantage
| 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 | ~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 | |
| 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 |
| Deep Learning | 123.05 | 119.92 | 97.46% | 0.85 | ||
| 2 | Instrument Quality Testing | Conventional Algorithms | 90.25 | 81.78 | 90.61% | 0.83 |
| Deep Learning | 128.26 | 124.9 | 97.38% | 0.85 | ||
| 3 | Customer Environment Sequencing | Conventional Algorithms | 83.87 | 77.2 | 92.04% | 0.82 |
| Deep Learning | 117.53 | 113.85 | 96.87% | 0.85 | ||
Simple operation process
