Python client for uploading NGS / HTS sequencing runs to MyTardis
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mytardis_ngs_ingestor is a client to register and upload next-generation sequencing data (NGS) to a MyTardis data management server.

It runs on Linux and currently supports runs from Illumina HiSeq, NextSeq and MiSeq instruments.


Python dependencies are installed from requirements.txt


Create a Python virtualenv:

pip install virtualenv
virtualenv ~/.virtualenvs/mytardis_ngs_ingestor
source ~/.virtualenvs/mytardis_ngs_ingestor/bin/activate

Install Python dependencies:

pip install -U pip
pip install -r requirements.txt

While not recommended, the package can alternatively be installed system-wide via:

sudo python install

and the script invoked using illumina_uploader.

Ingestor configuration

The program will look for a configuration file named uploader_config.yaml in the current working directory.

The path to the configuration file can be specified using the --config commandline option.

There is an annotated example configuration file uploader_config_example.yaml. Copy this to uploader_config.yaml and edit it with your site requirements.

All configuration options can be overridden with a corresponding commandline option (eg, fastqc_bin: /usr/local/bin/fastqc in the config file becomes --fastqc-bin=/usr/local/bin/fastqc)

Running the ingestor

To ingest a run:

If the package is installed:

illumina_uploader --path=/mnt/bigdisk/160915_FHT451_0119_AC6AMWACXZ/ \

where {run_path}/{run_id}.bcl2fastq is the path where the bcl2fastq (fastq.gz files in project/sample directories) is located.

In this example, {run_path} is /mnt/bigdisk/160915_FHT451_0119_AC6AMWACXZ/ and {run_id} is 160915_FHT451_0119_AC6AMWACXZ. does not run bcl2fastq automatically. It is a assumed that the run has already been demultiplexed.

Autoprocessing - demultiplexing and QC

The script can be used to demultiplex (bcl2fastq) and do run QC (eg fastqc), as well as trigger ingestion into MyTardis. It is intended to be executed by a cron job every few minutes to process any completed sequencing runs as they appear.

If the package is installed:

illumina_autoprocess --config autoprocess_config.toml --runs /data/sequencing_runs

Each task in the autoprocessing pipeline creates a file <run_dir>/tasks/<task_name>. This file is JSON and contains the status, (eg running, complete, error) and sometimes the stdout/stderr from a wrapped tool (eg bcl2fastq stderr messages).

Autoprocessing will ignore any run folders containing the file tasks/ignore. You can create this for existing processed runs by running something like:

cd /data/illumina/  # base directory for runs
for d in $(ls -d */); do mkdir -p ${d}tasks/; touch ${d}tasks/ignore; done

If any task fails for any run, the autoprocessing pipeline will not continue to other steps. To retry after fixing the problem remove the corresponding <run_dir>/tasks/<task_name> file. For example, if ingestion failed due to the server being inaccessible, remove <run_dir>/tasks/mytardis_upload. Alternatively, the --retry flag can be used to retry the last failed task.

Once autoprocessing has completed successfully for a run, the <run_dir>/tasks/all_complete file is created. If a single step needs to be re-run after successful completion (eg bcl2fastq), you must remove the all_complete file and the required <task_name> file.

Configuring the autoprocessing pipeline

See autoprocessing_config_example.toml - copy this to autoprocessing_config.toml to get started.

The autoprocessing pipeline uses a default config file which can be overriden with a run-specific config in the run directory.

The default autoprocessing_config.toml can be specified with --config autoprocessing_config.toml. If there is an autoprocessing_config.toml file in the run directory, this config is used instead. If there is no autoprocessing_config.toml file in the run directory, one is created from a copy of the default file (this aids reproducability - in the case the default config file is modified, the run will still have record of the config used at the time of processing).

Commandline options (eg --run-storage-base) override the equivalent setting in top level of any config file.


If the file logging_config.toml exists in the current working directory it will be used to configure logging. A log configuration file can be specified with the --logging-config commandline options.

See logging_config_example.toml as an example. The settings here are serialized from TOML format and used as a Python logging dictConfig.

How a 'run' is structured in the MyTardis data model

When a run is ingested, two types of Experiment are produced - a single Run Experiment and one or more Project Experiments.

The Run Experiment represents the whole run and should be only accessible to Facility Managers.

The Project Experiments correspond to distinct projects produced after demultiplexing (eg the 'SampleProjects' in SampleSheet.csv). These contain a subset of the data from the entire run and are shared with end-users by the Facilty Manager. A Project Experiment is also created for the reads where a barcode could not be assigned.

Datasets are created containing:

  • FASTQ files
  • FastQC reports

In addition, the Run Experiment contains a Dataset for configuration and log files that the facility may wish to preserve (currently only SampleSheet.csv).

'Raw' .bcl files are not ingested.


Here is a quick overview of how the project is structured.

Instances of the mytardis_uploader.MyTardisUploader class handle (most) REST requests to the MyTardis server. also contains basic config and commandline parsing functions. There is no domain-specific code related NGS runs in The ingestor script adds additional config/commandline options and instatiates MyTardisUploader instances (one per MyTardis StorageBox) for making REST requests. It handles parsing of metadata from Illumina sequencing runs and registering/uploading files to MyTardis in a structure suitable of multiplexed runs from multi-user facilities. and provide data structures for holding run metadata and can serialize themselves to JSON for use by MyTardisUploader. is due for a refactor - run specific parsing functions should be split out into their own library, and logic around different filenaming and directoty structures (eg bcl2fastq v1.8.4 vs. 2.17) should be abstracted behind an API to simplify some of the conditionals in

The ingestor and autoprocessing tools can be run without installation - use this Python module syntax to run them.


python -m mytardis_uploader.illumina_uploader --path=/mnt/bigdisk/160915_FHT451_0119_AC6AMWACXZ/ \


python -m mytardis_ngs_ingestor.autoprocess --runs /data/sequencing_runs