Start by running:

python genconfig.py --mode chipbinner

This line will create a config/config.yaml with the instructions to run the pipeline, and config/sample_list.csv file. Some configurations should be modified according to the analysis. For example: `

organism: hg38
MS_normalize: True
merge_replicates: True

MS_yaml: references/MS_coefficients.yaml

comparisons:
- treatment1_control1

Specify MS_normalize = True if your analysis implies MS normalizing bigWig files, and set merge_replicates = False if you want to use a single replicate as bigWig.

If you want to use 2 replicates, specify those replicates in the config/sample_list.csv file, or just 1 if no merging is needed. Each replicate/pair-of-replicates should have a condition and a mark associated, and specified in the config/sample_list.csv. After each condition is defined in the config/sample_list.csv file, the comparisons should be set in the config/config.yaml in the comparisons level, and separated by an _. For example:

![[Pasted image 20241125145716.png]]

![[Pasted image 20241125145758.png]]

The file references/MS_coefficients.yaml should specify the MS coefficients used to normalize the bigWig files, and each entry should refer to a replicate file, with the ".bam" suffix removed. For example:

![[Pasted image 20241125150259.png]]

Limitations:

• ==Don't specify conditions that only contain one replicate and other with two replicates. If you use this pipelines, either all conditions should have one replicate or all have both replicates.==
• ==Due to the common use of the folders to deposit the final bw files, after each run, you should erase these output files before rerunning the script!==