The goal of this work was to develop an approach for the rapid and sensitive detection of microbial contaminants at low abundance from low volume of samples during the manufacturing process.
| Accession | PRJNA869859 |
| Data Type | Raw sequence reads |
| Scope | Multispecies |
| Publications | - Published online: Strutt J et al., "Machine-learning based detection of adventitious microbes in T-cell therapy cultures using long read sequencing", bioRxiv, 2022;
|
| Submission | Registration date: 16-Aug-2022
Singapore-MIT Alliance for Research and Technology |
| Relevance | Medical |
Project Data:
| Resource Name | Number
of Links |
|---|
| Sequence data |
| SRA Experiments | 261 |
| Other datasets |
| BioSample | 261 |