Quick Start

We demonstrate a simple example.

1. Download MaChIAto with example files

Enter the following command terminal after you download and install Docker Desktop: https://docs.docker.com/engine/install/#desktop.

1 2	# Download Docker image for MaChIAto Classifier docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato:latest python ../MaChIAto.py -h;

2. Prepare the output folder of CRISPResso2.

You can download the example output of CRISPResso2: https://sourceforge.net/projects/machiato-example-files/files/example_CRISPResso2_output.zip/download

3. Re-classify the allele frequency table derived from CRISPResso2.

The re-classification command of MaChIAto (Classifier)

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato:latest python ../MaChIAto.py \
-m CRISPResso2 \
-ccf (Alleles_frequency_table.zip of the CRISPResso2 output) \
-o (output directory) \
-a (wt amplicon sequence) \
-g (protospaser sequence) \
(other parameters);

The other parameters varies per experiment type {knock-out, homology-based knokc-in, Prime Editing}.
Please refer to the usage of MaChIAto Classifier: https://machiatopage.github.io/2022/06/22/Usage-of-MaChIAto-Classifier/

The below example is for the knock-out analysis.

Case of knock-out

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato:latest python ../MaChIAto.py \
-ccf (Alleles_frequency_table.zip of the CRISPResso2 output) \
-o (output directory) \
-a (wt amplicon sequence) \
-g (protospaser sequence);

4. Check the classification result.

You can get the classification result of the editing. The result is visualized as the pie chart based on the ALL_dataframe.txt.

We provide the detailed description how to read them in the page of the MaChIAtoClassifier output: https://machiatopage.github.io/2022/06/22/Output-of-MaChIAto-Classifier/

5. Get local alignment and the mutation profiling using MaChIAto Aligner.

MaChIAto can profile and visualize the MaChIAto output using MaChIAto Aligner, MaChIAto Analyzer, and MaChIAto Reviewer.
You can download the docker images for MaChIAto Aligner, MaChIAto Analyzer, and MaChIAto Reviewer using the below commands.

# Download Docker image for MaChIAto Aligner
docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato_aligner:latest Rscript ../MaChIAtoAligner.R
# Download Docker image for collect_MaChIAto_data.py
docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../collect_MaChIAto_data.py;
# Download Docker image for MaChIAto Analyzer
docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato_analyzer:latest Rscript ../MaChIAtoAnalyzer.R;
# Download Docker image for MaChIAto Reviewer
docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato_reviewer:latest Rscript ../MaChIAtoReviewer.R;

The below command is example command of MaChIAto Aligner.

The alignment command of MaChIAto Aligner

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docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato_aligner:latest Rscript ../MaChIAtoAligner.R \
(directory of MaChIAto Classifier) \
(output directory);

*You can download pre-processed dataset:
https://sourceforge.net/projects/machiato-example-files/files/example_MaChIAtoClassifier_docker_output.zip/download

If your donor insert has the extra sequence outside of the homology arm, you should enter it.
Please refer to the usage of MaChIAto Aligner: https://machiatopage.github.io/2022/06/22/Usage-of-MaChIAto-Aligner/

6. Check the alignment result.

You can get the local alignment of the editing. The result is visualized as the map.

We provide a detailed description of how to read them in the page of the MaChIAtoAligner output: https://machiatopage.github.io/2022/06/22/Output-of-MaChIAto-Aligner/

7. Aggregate the multiple data.

If you have multiple data (n>3) to profile the characteristics, you should aggregate it using the following command.

*You can download pre-processed dataset:
https://sourceforge.net/projects/machiato-example-files/files/example_MaChIAtoClassifier_docker_output.zip/download

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/collect_machiato_data:latest python ../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);

The above command is used for the knock-out analysis. The calculation target for MaChIAto Analyzer/Reviewer is protospacer.
You can also target the homology arm and RT template using another command. Please see the usage of collect_MaChIAto_data.py: https://machiatopage.github.io/2022/07/27/Usage-of-collect-MaChIAto-data-py/#more

8. Investigate the relationship with (epi-)genomic context

You can see the correlation with the >70 (epi-)genomic context using MaChIAto Analyzer.

*You can download pre-processed dataset:
MaChIAto Aligner: https://sourceforge.net/projects/machiato-example-files/files/example_MaChIAtoAligner_docker_output.zip/download
Collections of collect_MaChIAto_data.py output: https://sourceforge.net/projects/machiato-example-files/files/collections.zip/download

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docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato_analyzer:latest Rscript ../MaChIAtoAnalyzer.R \
(directory of collect_MaChIAto_data.py) \
(output directory);

MaChIAtoAnalyzer basically use > 70 genomic context.
If you want to use epigenomic context or other context, please check the usage of MaChIAto Analyzer: https://machiatopage.github.io/2022/06/22/Usage-of-MaChIAto-Analyzer/

9. Check the correlation.

You can get the correlation between the editing efficacy and the context. The result is visualized as the scatter plot.

We provide a detailed description of how to read them in the page of the MaChIAtoAnalyzer output: https://machiatopage.github.io/2022/06/22/Output-of-MaChIAto-Analyzer/

10. Profile the mutation tendency.

You can generate the profile the mutation tendency using MaChIAto Reviewer.

*You can download pre-processed dataset:
MaChIAto Classifier: https://sourceforge.net/projects/machiato-example-files/files/example_MaChIAtoClassifier_docker_output.zip/download
MaChIAto Aligner: https://sourceforge.net/projects/machiato-example-files/files/example_MaChIAtoAligner_docker_output.zip/download
Collections of collect_MaChIAto_data.py output: https://sourceforge.net/projects/machiato-example-files/files/collections.zip/download

docker run --rm -v `pwd`:/DATA -w /DATA -i kazukinakamae/machiato_reviewer:latest Rscript ../MaChIAtoReviewer.R \
Rscript MaChIAto_Reviewer/MaChIAtoReviewer.R \
(the prefix of MaChIAto Classifier output) \
(the prefix of MaChIAto Aligner output) \
(directory of collect_MaChIAto_data.py) \
(output directory);

11. Check the mutation profile.

You can get the mutation profile of the editing. The result is visualized as the bar plot and pie chart.

We provide a detailed description of how to read them in the page of the MaChIAtoReviewer output: https://machiatopage.github.io/2022/06/22/Output-of-MaChIAto-Reviewer/