The Jupyter notebook "Oligo-Analytical-Characterization - Screening method in Jupyter" was developed to perform automated data post-processing for high-throughput oligonucleotide analytical characterization. It uses pre-processed LC-MS data exported from UNIFI Intact Protein (MS-RT Window Based) workflow within waters connect (Version 1.9.12.7, Waters Corporation) as published in (DOI added after publication).
The notebook identifies synthetic oligonucleotides based on deconvolved average mass, calculates UVxMS purity and performs partial impurity profiling and grouping into impurity classes. The analysis is done in batch-format for a complete sample run sequence. Two kinds of reports are created per sample run sequence: An individual report per sample, consisting of a full MS peak list with partial impurity assignment, full-length product identification, impurity class information, UV chromatogram and deconvolved mass spectrum. A summary report table (named: Batch_summary) for the complete sample run sequence, consisting of FLP identification, purity analysis and impurity groups of all samples.
- Python 3.13 needs to be installed on your computer in order to run the notebook. Information about how to open and run Jupyter Notebooks can be found on https://jupyter.org/.
- Download the Jupyter Notebook "Oligo-Analytical-Characterization - Screening method in Jupyter" and save it on the computer (format: .ipynb).
- Open the Jupyter Notebook.
- A list with package requirements is provided in the requirements.txt file.
- Follow the instructions within the notebook.
Kathrin Stavenhagen, [email protected]
Rapid and Flexible Analytical Characterization of Oligonucleotides in Early Drug Discovery https://doi.org/10.1016/j.chroma.2025.466517
- Apache 2.0