End motifs analysis of circulating DNA from the plasma of patients with stage II-III breast cancer (n=50), stage I-III non-small cell lung cancer (n=56), metastatic colorectal cancer (mCRC) (n=15) and healthy individuals (n=37)..
We have developed an algorithm designed to discriminate cancer patients and healthy individuals based on cirDNA fragment end motif analysis assisted by machine learning, using data obtained from shallow whole genome sequencing (a method we call EMA). We applied EMA to cirDNA from the plasma of patients with stage II-III breast cancer, stage I-III non-small cell lung cancer, and metastatic colorectal cancer (mCRC). CirDNA from 158 individuals was prepared following the conventional double-stranded DNA library preparation (DSP). We also performed a single-stranded DNA library preparation (SSP) using mCRC patients and healthy control cirDNA, which allowed us to make the first ever end motif analysis in the literature which compares the use of DSP and SSP.
- Type: Whole Genome Sequencing
- Archiver: European Genome-Phenome Archive (EGA)
Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data
| Dataset ID | Description | Technology | Samples |
|---|---|---|---|
| EGAD50000001877 | Illumina MiSeq | 50 | |
| EGAD50000001878 | Illumina MiSeq | 56 | |
| EGAD50000001879 | Illumina MiSeq | 30 | |
| EGAD50000001880 | Illumina MiSeq | 62 |
