Running the Pipeline on Your Data

This page describes how to run the ImmuneDB pipeline on your own BCR/TCR data. It is assumed that you’ve previously tried the example pipeline and understand the basics of running commands in the Docker container.

Like in the example, each code block has a header saying if the command should be run on the host or in the Docker container.

Copying Your Sequence Data Into Docker

Unlike in the example pipeline where sequencing data was provided, you’ll need to copy your own FASTA/FASTQ sequencing data or AIRR-formatted IgBLAST output into the Docker container.

To do so, on the host, we create a new directory in the shared directory into which we’ll copy your sequencing data. Here we’re calling it sequences but you’ll probably want to choose a more descriptive name. Replace PATH_TO_SEQUENCES with the path to your sequencing data.

Run on Host
$ mkdir -p $HOME/immunedb_share/input
$ cp PATH_TO_SEQUENCES $HOME/immunedb_share/input

Running IgBLAST (optional)


If your data is already in AIRR-compliant IgBLAST format or you are planning on using the built in anchoring method, you can skip this step.

The following command will run IgBLAST on your files. Valid values for species and locus are:

  • SPECIES:human, mouse


$ SPECIES LOCUS /share/input /share/input

For consistency with the commands in the rest of this tutorial, we’ll move the new IgBLAST output files to /share/input and move the FASTA/FASTQ files to /share/sequences.

$ mkdir -p /share/sequences
$ mv /share/input/*.fast[aq] /share/sequences

Creating a Metadata Sheet

Next, we’ll use the immunedb_metadata command to create a template metadata file for your sequencing data. In the Docker container run:

Run in Docker
$ cd /share/input
$ immunedb_metadata --use-filenames


This command expects the files to end in .fasta for FASTA, .fastq for FASTQ, or .tsv for AIRR.

This creates a metadata.tsv file in /share/input in Docker which is linked to $HOME/immunedb_share/input on the host.

The --use-filenames flag is optional, and simply populates the sample_name field with the file names stripped of their extension.

Editing the Metadata Sheet

On the host open the metadata file in Excel or your favorite spreadsheet editor. The headers included in the file are required. You may add additional headers as necessary for your dataset (e.g. tissue, cell_subset, timepoint) so long as they follow the following rules:

  • The headers must all be unique

  • Each header may only contain lowercase letters, numbers, and underscores

  • Each header must begin with a (lowercase) character

  • Each header must not exceed 32 characters in length

  • The values within each column cannot exceed 64 characters in length


When data is missing or not necessary in a field, leave it blank or set to NA, N/A, NULL, or None (case-insensitive).

Pipeline Steps

Much of the rest of the pipeline follows from the example pipeline’s instance creation step. To start, create a database. Here we’ll call it my_db but you’ll probably want to give it a more descriptive name:

Run in Docker
$ immunedb_admin create my_db /share/configs

Then we’ll identify or import the sequences. For this process the germline genes must be specified. The germlines are provided FASTA files in the Docker image at /root/germlines.


You can use your own germline files if you desire so long as they are IMGT gapped.

For this segment we’ll assume human B-cell heavy chains, but the process is the same for any dataset. Depending on if you want to use IgBLAST input (recommended) or the built-in annotation method the command will be one of the following:

Option 1: Importing from IgBLAST output (recommended):

Run in Docker
$ immunedb_import /share/configs/example_db.json airr \
     /root/germlines/igblast/human/IGHV.gapped.fasta \
     /root/germlines/igblast/human/IGHJ.gapped.fasta \

Option 2: Using anchoring method:

Run in Docker
$ immunedb_identify /share/configs/my_db.json \
      /root/germlines/anchor/human/IGHV.gapped.fasta \
      /root/germlines/anchor/human/IGHJ.gapped.fasta \

After importing or identifying sequences, continue running the pipeline from here:

Run in Docker
$ immunedb_collapse /share/configs/my_db.json

Then we assign clones. For B-cells we recommend:

Run in Docker
$ immunedb_clones /share/configs/my_db.json cluster

For T-cells we recommend:

Run in Docker
$ immunedb_clones /share/configs/my_db.json cluster --min-similarity 1

If you have a mixed dataset, you can assign clones in different ways, filtering on V-gene type. For example:

Run in Docker
$ immunedb_clones /share/configs/my_db.json cluster --gene IGHV
$ immunedb_clones /share/configs/my_db.json cluster --gene TCRB \
      --min-similarity 1

The last required step is to generate aggregate statistics:

Run in Docker
 $ immunedb_clone_stats /share/configs/my_db.json
 $ immunedb_sample_stats /share/configs/my_db.json

For B-cells, you might want to generate lineages too. The following excludes mutations that only occur once. immunedb_clone_trees has many other parameters for filtering which you can view with the --help flag or at Clone Trees (Optional).

Run in Docker
 $  immunedb_clone_trees /share/configs/my_db.json --min-seq-copies 2

Selection pressure can be run with the following. This process is quite time-consuming, even for small datasets:

Run in Docker
 $ immunedb_clone_pressure /share/configs/my_db.json \

Finally, the data should be available at http://localhost:8080/frontend/my_db.

Analyzing Your Data

After all the above steps are complete, you should have a fully populated database, ready for analysis via Exporting Data to Files, Querying with SQL, and the Python API.