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CLI Usecase

A video of this usecase is available here: CLI interface Youtube

Here, we show how to use the simulator with the CLI interface and provide an example client that randomly decides what action to take. Note that the client does not use the official ReadUntil API as our setup is slightly different. Since our tool is for research, SSL encryption is not needed (as of now), we restrict to only the essential ReadUntil API methods and the API returns basecalled rather than raw chunks (raise an issue if you want it added).

Assuming you have followed the installation instructions, do the following starting from a directory of your choice:

mkdir server_client_cli_example
cd server_client_cli_example

source ~/ont_project_all/ont_project_venv/bin/activate
usecase_generate_random_reads random_reads.fasta
# or: python ~/ont_project_all/ont_project/src/simreaduntil/usecase_helpers/generate_random_reads.py random_reads.fasta

source ~/ont_project_all/ont_project_venv/bin/activate
simulator_server_cli random_reads.fasta example_run --overwrite --verbosity info --dont_start --port 12000
# or: python ~/ont_project_all/ont_project/src/simreaduntil/usecase_helpers/simulator_server_cli.py random_reads.fasta example_run --overwrite --verbosity info --dont_start --port 12000

Open a new terminal window and run the following command:

source ~/ont_project_all/ont_project_venv/bin/activate
usecase_simulator_client_cli 12000
# or: python ~/ont_project_all/ont_project/src/simreaduntil/usecase_helpers/simulator_client_cli.py 12000

You can stop the client with Ctrl-C. Also stop the server with Ctrl-C in the other terminal window, so that the final summary files get written (action results and sequencing summary). You can also stop the server directly with Ctrl-C without doing so first for the client.

Create plots with:

sim_plots_cli example_run --verbosity info
plot_seqsum example_run/sequencing_summary.txt --save_dir example_run/figures/
# also possible to plot coverage of a reference by providing relevant args to "plot_seqsum"

# put all into an overview html page
usecase_make_html_report example_run/figures/ && open example_run/figures/figures.html

Typically, you can leave the server cli script as it is, but want to modify the client and input reads. You can take a look at the available options with --help appended to the commands.

Advanced Usecases

Config files are located in the directory configs which contain configs for the simulator (and ReadFish). The paths in these scripts may need to be adapted to your local setup.

Activate the environment and start jupyter lab:

source ~/ont_project_all/ont_project_venv/bin/activate
cd ~/ont_project_all/ont_project
jupyter lab # for convenience

Setup (Data + Extra Dependencies)

You can get the data and install the dependencies with:

cd ~/ont_project_all/ont_project
curl https://public.bmi.inf.ethz.ch/user/mmordig/ont_project/prepackaged/usecase_data.tar.gz -O
tar -xvzf usecase_data.tar.gz
cd runs
mkdir enrich_usecase && cd enrich_usecase
ln -s ~/ont_project_all/ont_project/usecases/configs/enrich_usecase configs
ln -s ../data data
cd ..
mkdir run_replication && cd run_replication
ln -s ~/ont_project_all/ont_project/usecases/configs/run_replication configs
ln -s ../data data
cd ..
cd ..

# install ReadFish
git submodule update --init --depth 1 external/ont_readfish
source ~/ont_project_all/ont_project_venv/bin/activate
pip uninstall -y readfish; pip install './[readfish]'; pip show readfish

# optional: install NanoSim and minimap2, but the usecase also works without
# git submodule update --init --depth 1 external/ont_nanosim
# bash usecases/install_usecase_deps.sh

You may want to check out the Docker image as described in the README.md in the repo root, if the installation does not work. See below for how the data was obtained.

After possibly adapting the config files at ~/ont_project_all/ont_project/usecases/configs/, you can run the usecases described further below:

~/ont_project_all/ont_project/usecases/replicate_run_submission.sh "sampler_per_rolling_window_channel"
~/ont_project_all/ont_project/usecases/enrich_usecase_submission.sh

Enrichment with ReadFish

This combines ReadFish with the SimReadUntil simulator. Reads are generated on the fly by sampling read start position, length and strand. With this ground-truth information, the alignment step in ReadFish can be accelerated which is useful when we accelerate the run by a factor of 10 where minimap2 alignment may otherwise become a bottleneck. Alternatively, a reads file can be provided by adding reads_file = <path> to the simulator config. If the read ids are NanoSim ids with ground-truth alignment information, minimap2 is not needed. Alternatively, a minimap2 reference index can be provided by adding it in the ReadFish config file. The index itself can be created by uncommenting the line create_minimap_index_if_inexistent in the usecase file.

Files:

  • enrich_usecase.py: end-to-end script that runs an enrichment with ReadFish connected to the simulator, see the instructions in that file
  • enrich_usecase_submission.sh: condor submission script, can also be run locally
  • compute_absolute_enrichment.ipynb: compute the absolute enrichment for the enrich usecase by comparing to control
  • install_usecase_deps.sh: to install minimap2 and NanoSim (optional), launch it from the repo root
  • generate_nanosim_reads.sh: script to create NanoSim reads that can be fed into the simulator

Parameter Extraction from an Existing Run

This experiment learns parameters from an existing run and then simulated a run with the learned parameters. The gaps are learnt from the run. Since the input to the simulator is in terms of basecalled reads, reads consisting of random letters are generated to match the read durations during the real run as closely as possible.

Files:

  • replicate_run.py: end-to-end script that runs the parameter extraction and simulation, see the instructions in that file
  • replicate_run_submission.sh: condor submission script, can also be run locally
  • compare_replication_methods.ipynb: merge plots into one (for the paper) to compare the different parameter extraction methods

Parameter extraction takes some time (few minutes) since the sequencing summary file must be loaded. Therefore, we implement a caching mechanism in the function create_simparams_if_inexistent:

sequencing summary from an existing run 
--> sequencing summary with mux scans removed (prefixed with 'no_mux_scans_') 
--> cleaned sequencing summary (prefixed with 'cleaned_'): some reads of a channel overlap, so we shift them
--> sim params (per channel, saved in one '.npz' file)

We check from the back whether any of the files already exists and use the existing file if possible. Make sure to delete the appropriate files when changing relevant parameters. When running several configurations in parallel and some cache files do not exist, make sure they don't get created by different processes at the same time.

Other Files

These files are for our own reference and may not work for you out of the box:

  • analyze_readfish_outputs.py: to check whether ReadFish is mapping reads correctly by parsing the ground-truth from the read id
  • plot_existing_seqsum.py: to plot an existing sequencing summary file, e.g., from a real run; probably needs to be adapted to your setting
  • remove_mux_scans.ipynb: notebook showing how mux scans are removed (you don't need to run this, this is done automatically in the usecases)
  • prepare_small_refgenome.py: to create a small reference genome for the usecase
  • results_preparation.md: commands to create the results in the paper
  • plot_existing_seqsum_submission.sh: condor submission script, can also be run locally

How the data was obtained

We provide more details on how the usecase data was created that you downloaded before. You do not need to run this.

cd ~/ont_project_all/ont_project
mkdir runs && cd runs
mkdir data && cd data
curl https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz -O
wget https://labshare.cshl.edu/shares/schatzlab/www-data/UNCALLED/simulator_files/20190809_zymo_seqsum.txt.gz
# gz file slow to read and write, so we uncompress it
gunzip 20190809_zymo_seqsum.txt.gz

source ~/ont_project_all/ont_project_venv/bin/activate
normalize_fasta "chm13v2.0.fa.gz" "chm13v2.0_normalized3chroms.fa.gz" --seq_names "chr20,chr21,chr22"
normalize_fasta "chm13v2.0.fa.gz" "chm13v2.0_normalized.fa.gz"
cd ..

mkdir enrich_usecase && cd enrich_usecase
ln -s ~/ont_project_all/ont_project/usecases/configs/enrich_usecase configs
ln -s ../data data
cd ..

mkdir run_replication && cd run_replication
ln -s ~/ont_project_all/ont_project/usecases/configs/run_replication configs
ln -s ../data data