Usage of collect_MaChIAto_data.py

The user should execute collect_MaChIAto_data.py to make input file of MaChIAto Analyzer and MaChIAto Reviewer

Parameter list

-i or –indir {str}: input directory which includes the outputs of MaChIAto Classifier

-o or –outdir {str}: output directory

-ul or –untreated_label {str}: untreated label (This must be one.)’ (default: “machiato_dummy_sample”) (optional)

-ol or –knock_out_label {str}: knock-out label’ (default: []) (optional)

-il or –knock_in_label {str}: knock-in label’ (default: []) (optional)

-sc or –scaffold_seq {str}: scaffold sequence of sgRNA (default: “gttttagagctagaaatagcaagttaaaataaggctagtccgttatcaacttgaaaaagtggcaccgagtcggtgc”) (optional)

-t or –target_type {str}: target sequence type {“lmh” | “rmh” | “bmh” | “elmh” | “ermh” | “ebmh” | “protospacer”} (default: “bmh”) (optional)

–ignore_list {str}: The list of ignore target set contains target names which are not desired to analyze for some reasons. The data (e.g. DBF4B-A, DBF4B-B, DBF4B-C, DBF4B-D) including target name (e.g. DBF4B) shown in the list is skipped through the process of MaChIAto Analyzer. The format should be comma-separated like “TargetA, TargetB, …” “example_data” directory has “ignore_list.csv” as example. (default: “”) (optional)

Template command

Case of Double knock-in analysis (*ADVANCE: The analysis includes the comparison between two knock-in methods.)

python MaChIAto_Analyzer/collect_MaChIAto_data.py \
-i (the prefix of MaChIAto Classifier output) \
-o (output directory) \
-sc (scaffold sequence of sgRNA) \
-ul (untreated label) \
-ol (knock-out label) \
-il (knock-in label 1) (knock-in label 2) \
-t (calculation target) \
--ignore_list (list of samples ignored);

Case of Single knock-in analysis (*STANDARD)

python MaChIAto_Analyzer/collect_MaChIAto_data.py \
-i (the prefix of MaChIAto Classifier output) \
-o (output directory) \
-sc (scaffold sequence of sgRNA) \
-ul (untreated label) \
-ol (knock-out label) \
-il (knock-in label) \
-t (calculation target) \
--ignore_list (list of samples ignored);

Case of Simple knock-in analysis (*SIMPLE: The analysis can be applied when there is no knock-out sample used as control.)

python MaChIAto_Analyzer/collect_MaChIAto_data.py \
-i (the prefix of MaChIAto Classifier output) \
-o (output directory) \
-sc (scaffold sequence of sgRNA) \
-il (knock-in label) \
-t (calculation target) \
--ignore_list (list of samples ignored);

Case of Double knock-out analysis (*ADVANCE: The analysis includes the comparison between two knock-out methods.)

python MaChIAto_Analyzer/collect_MaChIAto_data.py \
-i (the prefix of MaChIAto Classifier output) \
-o (output directory) \
-sc (scaffold sequence of sgRNA) \
-ul (untreated label) \
-ol (knock-out label 1) (knock-out label 2) \
-t (calculation target) \
--ignore_list (list of samples ignored);

Case of Single knock-out analysis (*STANDARD)

python MaChIAto_Analyzer/collect_MaChIAto_data.py \
-i (the prefix of MaChIAto Classifier output) \
-o (output directory) \
-sc (scaffold sequence of sgRNA) \
-ul (untreated label) \
-ol (knock-out label) \
-t (calculation target) \
--ignore_list (list of samples ignored);

*If you do not enter “-ul (untreated label)”, the process can work. However, some filtering process will be skipped.

Example of the command

Here is example of the command using docker.

If you don’t have the docker image for collect_MaChIAto_data.py, please go the Preparation page: https://machiatopage.github.io/2100/01/01/Preparation/

The input file can be downloaded from the following link:
https://sourceforge.net/projects/machiato-example-files/files/example_MaChIAtoClassifier_docker_output.zip/download

You can check the output file of the below examples following the following link:
https://sourceforge.net/projects/machiato-example-files/files/collections.zip/download

Simple knock-out analysis: knock-out using MS2-tagged sgRNA/calculation target: protospacer

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../collect_MaChIAto_data.py \
-i example_MaChIAtoClassifier_docker_output \
-o collections/Simple_knockout_analysis \
-sc gttttagagctaggccaacatgaggatcacccatgtctgcagggcctagcaagttaaaataaggctagtccgttatcaacttggccaacatgaggatcacccatgtctgcagggccaagtggcaccgagtcggtgc \
-ol KO \
-t protospacer;

Single knock-out analysis: knock-out using MS2-tagged sgRNA/calculation target: protospacer

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../collect_MaChIAto_data.py \
-i example_MaChIAtoClassifier_docker_output \
-o collections/Single_knockout_analysis \
-sc gttttagagctaggccaacatgaggatcacccatgtctgcagggcctagcaagttaaaataaggctagtccgttatcaacttggccaacatgaggatcacccatgtctgcagggccaagtggcaccgagtcggtgc \
-ul WT \
-ol KO \
-t protospacer;

Double knock-out analysis: MS2-tagged sgRNA/calculation target: protospacer

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../collect_MaChIAto_data.py \
-i example_MaChIAtoClassifier_docker_output \
-o collections/Double_knockout_analysis \
-sc gttttagagctaggccaacatgaggatcacccatgtctgcagggcctagcaagttaaaataaggctagtccgttatcaacttggccaacatgaggatcacccatgtctgcagggccaagtggcaccgagtcggtgc \
-ul WT \
-ol KO KI \
-t protospacer;

Simple knock-in analysis: PITCh using MS2-tagged sgRNA/calculation target: both homology arm

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../collect_MaChIAto_data.py \
-i example_MaChIAtoClassifier_docker_output \
-o collections/Simple_knockin_analysis \
-sc gttttagagctaggccaacatgaggatcacccatgtctgcagggcctagcaagttaaaataaggctagtccgttatcaacttggccaacatgaggatcacccatgtctgcagggccaagtggcaccgagtcggtgc \
-il KI \
-t bmh;

Single knock-in analysis: PITCh using MS2-tagged sgRNA/calculation target: both homology arm

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../collect_MaChIAto_data.py \
-i example_MaChIAtoClassifier_docker_output \
-o collections/Single_knockin_analysis \
-sc gttttagagctaggccaacatgaggatcacccatgtctgcagggcctagcaagttaaaataaggctagtccgttatcaacttggccaacatgaggatcacccatgtctgcagggccaagtggcaccgagtcggtgc \
-ul WT \
-ol KO \
-il KI \
-t bmh;

Double knock-in analysis: PITCh and LoAD knock-in using MS2-tagged sgRNA/calculation target: both homology arm

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../collect_MaChIAto_data.py \
-i example_MaChIAtoClassifier_docker_output \
-o collections/Double_knockin_analysis \
-sc gttttagagctaggccaacatgaggatcacccatgtctgcagggcctagcaagttaaaataaggctagtccgttatcaacttggccaacatgaggatcacccatgtctgcagggccaagtggcaccgagtcggtgc \
-ul WT \
-ol KO \
-il KI KI2 \
-t bmh;