ExpressionExpertIpynb
The workflow facilitates analysis of promoter libraries using Jupyter Notebooks. Clone the repository or directly use it in the cloud with binder by clicking the following binder symbol.
The analysis is distributed in different notebooks:
| *.ipynb | Description |
|---|---|
| 0-Worflow | Workflow set-up. Parameters are defined for the machine learning type, threshold for positional analysis, and output file names and types. |
| 1-Statistical-Analysis | Statistical analysis of sequence and reporter. The notebook reports the unique promoter number, sequence and position sampling diversity, and reporter cross-correlation. |
| 2-Regressor-Training | Machine learning training. The data is separated into training and test sets and and trained to the defined machine learning tool. |
| 3-Regressor-Performance | Performance evaluation of machine learning. The machine learning regressor is loaded and evaluated based on cross validation and feature importance. |
| 4-Exploration-Space | Sampling from the predictable sequence space. The machine learning regressor is used to predict sequences that are covered by the library sampling space. |
| 5-Promoter-Prediction | Activity prediction of defined sequences. The activity of single sequences can be assessed as well as to identify sequences with defined activity. |
Last Version: 2020/07/01
Author: Ulf Liebal
Contact: [email protected]
License: see LICENSE file